This document presents an overview of a thesis project on computer-assisted screening of microcalcifications in digitized mammograms for early detection of breast cancer. The project aims to develop a system that can automatically detect microcalcifications in mammogram images to assist radiologists. The system will use techniques like image segmentation, morphological operations, filtering, and feature extraction to preprocess mammogram images and identify microcalcification clusters. A mini-MIAS database containing 322 mammogram images will be used to test and evaluate the methodology. The document outlines the background, motivation, challenges, plan of action and materials/tools for the project.
This document summarizes a presentation on identifying microcalcifications in digital mammograms for early detection of breast cancer. It provides background on breast cancer and microcalcifications, outlines steps in computerized breast cancer detection systems including detection and diagnosis, and reviews literature on using techniques like wavelet and contourlet transforms to enhance mammograms and identify microcalcifications for improved cancer screening. The presentation will focus on microcalcification detection and diagnosis using a contourlet transform approach to enhance mammograms by applying directional filters to contourlet subbands before reconstructing an approximation of the mammogram with enhanced microcalcifications.
The document discusses computer assisted screening of microcalcifications in digitized mammograms for early detection of breast cancer. It begins with an introduction to breast cancer and computer aided detection and diagnosis systems. It then provides background on areas of interest including improvement of pictorial information and machine vision. Next, it discusses microcalcifications, mammography, and mammograms. The document reviews literature on various preprocessing, feature extraction, and detection techniques. It identifies challenges in microcalcification detection including their small size and variable clusters. Finally, it outlines the plan of action for the thesis including use of the mini-MIAS mammogram database and a range of techniques to remove pectoral muscle and x-ray labels.
Microcalcification Enhancement in Digital MammogramNashid Alam
The document discusses early detection of breast cancer through computer-aided detection of microcalcifications in digital mammograms. It describes microcalcifications and how mammography is used to detect them as early signs of cancer. The problem is the difficulty for radiologists to accurately detect microcalcifications. The goal is to develop a computer model to better detect microcalcification clusters and determine cancer likelihood from mammogram images.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
MICROCALCIFICATION IDENTIFICATION IN DIGITAL MAMMOGRAM FOR EARLY DETECTION OF...Nashid Alam
Digital mammogram has become the most effective technique for early breast cancer detection modality. Digital mammogram takes an electronic image of the breast and stores it directly in a computer. High quality mammogram images are high resolution and large size images. Processing these images require high computational capabilities. The transmission of these images over the net is sometimes critical especially if the diagnosis of remote radiologists is required. The aim of this study is to develop an automated system for assisting the analysis of digital mammograms. Computer image processing techniques will be applied to enhance images and this is followed by segmentation of the region of interest (ROI). Subsequently, the textural features will be extracted from the ROI. These texture features will be used to classify the microcalcifications as either malignant or benign.
This document discusses the use of artificial intelligence in breast imaging, specifically for the early detection of breast cancer. It provides background on common breast imaging techniques like mammography, tomosynthesis, ultrasound and MRI. It then discusses traditional CAD (computer-aided detection) systems and their limitations in detecting cancers. The document introduces artificial intelligence and how techniques like machine learning and deep learning can improve upon traditional CAD systems. It reviews several studies that have found AI-based systems can help radiologists achieve higher accuracy and reduce false-positive rates compared to unaided diagnosis. Finally, it mentions several companies developing AI solutions for applications in mammography, tomosynthesis and breast MRI.
IRJET- Comparison of Breast Cancer Detection using Probabilistic Neural Netwo...IRJET Journal
1) The document compares two machine learning algorithms, probabilistic neural network (PNN) and support vector machine (SVM), for detecting breast cancer in mammogram images.
2) It evaluates the performance of PNN and SVM on a dataset of 322 mammogram images containing both benign and malignant tumors.
3) The proposed methodology applies techniques like image enhancement, segmentation, and feature extraction before classifying the images using PNN and SVM to detect tumors and determine if they are benign or malignant.
This document summarizes a presentation on identifying microcalcifications in digital mammograms for early detection of breast cancer. It provides background on breast cancer and microcalcifications, outlines steps in computerized breast cancer detection systems including detection and diagnosis, and reviews literature on using techniques like wavelet and contourlet transforms to enhance mammograms and identify microcalcifications for improved cancer screening. The presentation will focus on microcalcification detection and diagnosis using a contourlet transform approach to enhance mammograms by applying directional filters to contourlet subbands before reconstructing an approximation of the mammogram with enhanced microcalcifications.
The document discusses computer assisted screening of microcalcifications in digitized mammograms for early detection of breast cancer. It begins with an introduction to breast cancer and computer aided detection and diagnosis systems. It then provides background on areas of interest including improvement of pictorial information and machine vision. Next, it discusses microcalcifications, mammography, and mammograms. The document reviews literature on various preprocessing, feature extraction, and detection techniques. It identifies challenges in microcalcification detection including their small size and variable clusters. Finally, it outlines the plan of action for the thesis including use of the mini-MIAS mammogram database and a range of techniques to remove pectoral muscle and x-ray labels.
Microcalcification Enhancement in Digital MammogramNashid Alam
The document discusses early detection of breast cancer through computer-aided detection of microcalcifications in digital mammograms. It describes microcalcifications and how mammography is used to detect them as early signs of cancer. The problem is the difficulty for radiologists to accurately detect microcalcifications. The goal is to develop a computer model to better detect microcalcification clusters and determine cancer likelihood from mammogram images.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
MICROCALCIFICATION IDENTIFICATION IN DIGITAL MAMMOGRAM FOR EARLY DETECTION OF...Nashid Alam
Digital mammogram has become the most effective technique for early breast cancer detection modality. Digital mammogram takes an electronic image of the breast and stores it directly in a computer. High quality mammogram images are high resolution and large size images. Processing these images require high computational capabilities. The transmission of these images over the net is sometimes critical especially if the diagnosis of remote radiologists is required. The aim of this study is to develop an automated system for assisting the analysis of digital mammograms. Computer image processing techniques will be applied to enhance images and this is followed by segmentation of the region of interest (ROI). Subsequently, the textural features will be extracted from the ROI. These texture features will be used to classify the microcalcifications as either malignant or benign.
This document discusses the use of artificial intelligence in breast imaging, specifically for the early detection of breast cancer. It provides background on common breast imaging techniques like mammography, tomosynthesis, ultrasound and MRI. It then discusses traditional CAD (computer-aided detection) systems and their limitations in detecting cancers. The document introduces artificial intelligence and how techniques like machine learning and deep learning can improve upon traditional CAD systems. It reviews several studies that have found AI-based systems can help radiologists achieve higher accuracy and reduce false-positive rates compared to unaided diagnosis. Finally, it mentions several companies developing AI solutions for applications in mammography, tomosynthesis and breast MRI.
IRJET- Comparison of Breast Cancer Detection using Probabilistic Neural Netwo...IRJET Journal
1) The document compares two machine learning algorithms, probabilistic neural network (PNN) and support vector machine (SVM), for detecting breast cancer in mammogram images.
2) It evaluates the performance of PNN and SVM on a dataset of 322 mammogram images containing both benign and malignant tumors.
3) The proposed methodology applies techniques like image enhancement, segmentation, and feature extraction before classifying the images using PNN and SVM to detect tumors and determine if they are benign or malignant.
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
Breast Cancer Detection using Convolution Neural NetworkIRJET Journal
This document discusses using convolutional neural networks to detect breast cancer from images. It begins with an abstract stating that breast cancer starts as uncontrolled growth of breast cells that can form tumors. Early detection at the first stage allows for curing. The proposed approach uses a convolutional neural network to take input images, perform preprocessing, compare to a database of cancer images, and detect cancer along with its stage to recommend treatment. It discusses using CNN algorithms inspired by the visual cortex to perform image recognition like humans. The document provides definitions of CNNs and deep learning, technologies used like image processing, and concludes that detecting and treating cancer early at its first stage is preferable.
Segmentation of thermograms breast cancer tarek-to-slid shareTarek Gaber
This document presents a new method for segmenting regions of interest (ROIs) in breast thermograms to detect breast abnormalities. The method uses features extracted from the ROIs, like statistical and texture features, and supports vector machines for classification. It was tested on a database of 149 patients, achieving 100% accuracy in detecting normal vs. abnormal breasts. The method provides an automatic and low-cost approach to segmenting thermograms for breast cancer detection.
A Review of Super Resolution and Tumor Detection Techniques in Medical Imagingijtsrd
Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23525/a-review-of-super-resolution-and-tumor-detection-techniques-in-medical-imaging/fathimath-safana-c-k
Image processing techniques play a significant role in many areas in life, especially
in medical images, where they play a prominent role in diagnosing many diseases such
as detection of the brain tumor, breast cancer, kidney cancer, and the fractions.
Breast cancer is a common disease, regardless of the type of this disease, whether
it is benign or malignant, it is very dangerous and early detection may reduce the risk
of the disease spreading in the body leading to death. This work presents an approach
to detect breast cancer based on image processing algorithms, including image
preprocessing, enhancement, segmentation, Morphological operations, and feature
extraction to detect and extract the breast cancer region
This document proposes using a DenseNet-II neural network model to classify mammogram images as benign or malignant. It first preprocesses mammogram images through normalization and data augmentation. It then improves the original DenseNet model by replacing the first convolutional layer with an Inception structure, creating a new DenseNet-II model. This model, along with other common models, are tested on mammogram data and the DenseNet-II model achieves the highest average accuracy of 94.55% for benign-malignant classification.
A survey on enhancing mammogram image saradha arumugam academiaPunit Karnani
This document summarizes research on enhancing mammogram images to improve the detection of breast cancer. It discusses how mammogram images have low contrast and are noisy, making it difficult to identify microcalcifications that could indicate cancer. Various image enhancement techniques are reviewed that aim to improve contrast, reduce noise, and sharpen edges to make microcalcifications more visible. The techniques discussed include nonlinear unsharp masking, wavelet-based enhancement, adaptive contrast enhancement, and integrated wavelet decompositions. Evaluation of the techniques suggests they can improve cancer diagnosis by enhancing image details and increasing radiologist performance.
Skin cancer is a term given to the uncontrolled
growth of strange skin cells. It occurs whenever unrepaired
DNA damages to skin cells trigger mutations, or any other
genetic defects, that lead the skin cells to multiply readily
and form malignant tumors. Image processing is a
commonly used method for skin cancer detection from the
appearance of the affected area on the skin. The input to the
system is that the skin lesion image so by applying novel
image process techniques, it analyses it to conclude about
the presence of skin cancer. The Lesion Image analysis tools
checks for the various Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, (ABCD rule), etc. by
texture, size and form analysis for image segmentation and
have stages. The extracted feature parameters are
accustomed classify the image as traditional skin and
malignant melanoma cancerlesion.
Artificial Neural Network (ANN) is one of the
important branches of Artificial Intelligence, which has
been accepted as a brand-new technology in computer
science for image processing. Neural Networks is currently
the area of interest in medicine, particularly in the fields of
radiology, urology, cardiology, oncology, etc. Neural
Network plays a vital role in an exceedingly call network. It
has been used to analyze Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, etc. which are
calculated using MATLAB from skin cancer images
intending to developing diagnostic algorithms that might
improve triage practices in the emergency department.
Using the ABCD rules for melanoma skin cancer, we use
ANN in the classification stage. Initially, we train the
network with known target values. The network is well
trained with 96.9% accuracy, and then the unknown values
are tested for the cancer classification. This classification
method proves to be more efficient for skin cancer
classification
A Virtual Instrument to Detect Masses In Breast Cancer using CAD toolstheijes
Breast cancer is the second-most driving and normal explanation behind death in view of tumor among one in every ten women. It has become a major health problem in the world over the past 50 years, and it has increased in recent years. Early detection is an effective way to diagnose and manage breast cancer. Mammography is the best and most suitable imaging technique for treatment of cancer at the early stage. The problems in mammography images such as high brightness value, dense tissues, noise and inefficient contrast level make analysis of these images a hard task for physicians for mass identification. This paper presents a CAD tool which are combination of image processing techniques to remove noise and enhancement of mammography images for identification & classification of masses. Efficient methods includes wavelet transformation and adaptive histogram equalization techniques, in addition with fusion techniques are used. Algorithms for identification of signs are tested on five patients, the associated abnormalities are clearly identified. The images for experimentation are taken from radiopedia. Experimental results show that a detection rate of 94.44% or higher can be achieved using this method, hence improved accuracy in breast cancer lesion detection. The proposed system achieves 100% sensitivity and 2.56 false positive for every image
Artificial neural network based cancer cell classificationAlexander Decker
This document summarizes an artificial neural network (ANN) based system called ANN-C3 for cancer cell classification using medical images. The system performs image pre-processing, segmentation using Harris corner detection and region growing, feature extraction of Tamura texture features, and classification using a neural network ensemble. Segmentation detects threshold points using Harris corner detection and performs region growing from these seed points. Feature extraction converts the image data into numerical form using Tamura texture features that capture variations in illumination and surfaces that human vision and surgeons use to differentiate cancerous and non-cancerous cells. The neural network is trained on a large set of labeled data to accurately classify cells.
This document discusses using MATLAB to detect breast cancer through analysis of mammogram and thermal images. It introduces breast cancer and explains that early detection is key to successful treatment. Currently, mammography and thermography are used for detection, but mammography has weaknesses like pain and radiation. The purpose of this project is to design a system to detect signs in mammogram and thermal images using image processing techniques in MATLAB. Mammogram images will be analyzed using morphology before feature extraction and classification. Thermal images will have features extracted from the heat distribution to identify possible cancer areas.
IRJET- Detection & Classification of Melanoma Skin CancerIRJET Journal
This document discusses methods for detecting and classifying melanoma skin cancer. It begins with an introduction to skin cancer and the importance of detecting melanoma early. It then reviews literature on existing techniques for melanoma detection using image processing and machine learning. The proposed system uses image segmentation, feature extraction using the ABCD criteria, principal component analysis to select key features, and support vector machine classification to determine whether images contain cancerous or non-cancerous lesions. The system aims to provide an accurate and fast evaluation of skin lesions to help in melanoma diagnosis.
This document presents a method for detecting melanoma skin cancer using image processing and machine learning techniques. Images of skin lesions are first segmented using active contour models. Features like color, size, shape and texture are then extracted from the segmented images. Texture is analyzed using local binary patterns (LBP). The extracted features are used to classify images as melanoma or non-melanoma using a support vector machine (SVM) classifier. The goal is to develop an automated system for early detection of melanoma to help reduce death rates from this dangerous form of skin cancer.
Melanoma Skin Cancer Detection using Image Processing and Machine Learningijtsrd
Dermatological Diseases are one of the biggest medical issues in 21st century due to its highly complex and expensive diagnosis with difficulties and subjectivity of human interpretation. In cases of fatal diseases like Melanoma diagnosis in early stages play a vital role in determining the probability of getting cured. We believe that the application of automated methods will help in early diagnosis especially with the set of images with variety of diagnosis. Hence, in this article we present a completely automated system of dermatological disease recognition through lesion images, a machine intervention in contrast to conventional medical personnel based detection. Our model is designed into three phases compromising of data collection and augmentation, designing model and finally prediction. We have used multiple AI algorithms like Convolutional Neural Network and Support Vector Machine and amalgamated it with image processing tools to form a better structure, leading to higher accuracy of 85 . Vijayalakshmi M M ""Melanoma Skin Cancer Detection using Image Processing and Machine Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23936.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/23936/melanoma-skin-cancer-detection-using-image-processing-and-machine-learning/vijayalakshmi-m-m
Breast cancer detection using Artificial Neural NetworkSubroto Biswas
This presentation summarizes research on diagnosing breast cancer using an artificial neural network. It begins with an introduction of the topic and presenter. The contents include descriptions of breast cancer, artificial neural networks, and backpropagation. It then details the breast cancer database used, the neural network model developed, and its performance in diagnosing cancers as benign or malignant. The conclusion is that neural networks show potential for medical diagnosis but require further optimization. Suggested future work includes exploring other training methods, feature selection, and adding treatment recommendations.
Microcalcification oriented content based mammogram retrieval for breast canc...Lazaros Tsochatzidis
Microcalcifications (MCs) provide a significant
early indication of breast malignancy. This work introduces a
supervised scheme for malignancy risk assessment of mammograms containing MCs. The proposed scheme employs shape and
textural features as input to a support vector machine (SVM)
ensemble, in order to perform content-based image retrieval
(CBIR) of mammograms. The retrieval performance of the
proposed scheme has been evaluated by taking into account
the variation of MCs morphology as defined in BI-RADS. In
our experiments, we use a set of 87 mammograms containing
MCs, obtained from the widely adopted DDSM database for
screening mammography. The experimental results demonstrate
that the proposed supervised CBIR scheme addresses effective
retrieval of MCs mammograms outperforming relevant unsupervised schemes.
RECOGNITION OF SKIN CANCER IN DERMOSCOPIC IMAGES USING KNN CLASSIFIERADEIJ Journal
The largest organ of the body is human skin. Melanoma is a fastest growing & deadliest cancer which starts in pigment cells (melanocytes) of the skin that mostly occurs on the exposed parts of the body. Early detection is vital in treating this type of skin cancer but the time and effort required is immense. Dermoscopy is a non invasive skin imaging technique of acquiring a magnified and illuminated image of a region of skin for increased clarity of the spots on the skin The use of machine learning and automation of the process involved in detection will not only save time but will also provide a more accurate diagnosis. The skin images collected from the databases cannot be directly classified by the automation techniques. The reason is twofold: (a) Lack of clarity in the features which is mainly due to the poor contrast of the raw image and (b) Large dimensions of the input image which causes the complexity of the system. Hence, suitable techniques must be adopted prior to the image classification process to overcome these drawbacks. The first drawback can be minimized by adopting suitable pre- processing techniques which can enhance the contrast of the input images. The second drawback is solved by incorporating the feature extraction technique which reduces the dimensions of the input image to high extent. Further, K-NN (K-Nearest Neighbor) classifier is used for classification of the given image into cancerous or non- cancerous.
Brain tumor detection and localization in magnetic resonance imagingijitcs
A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the
surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and
responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate
in the world. Great knowledge and experience on radiology are required for accurate tumor detection in
medical imaging. Automation of tumor detection is required because there might be a shortage of skilled
radiologists at a time of great need. We propose an automatic brain tumor detectionand localization
framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain
tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge
detection, modified histogram clustering and morphological operations. After morphological operations,
tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our
system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization
system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging.
The preliminary results demonstrate how a simple machine learning classifier with a set of simple
image-based features can result in high classification accuracy. The preliminary results also demonstrate the
efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to
extend this framework to detect and localize a variety of other types of tumors in other types of medical
imagery.
Computer Aided System for Detection and Classification of Breast CancerIJITCA Journal
Breast cancer is one of the most important causes of death among all type of cancers for grown-up and
older women, mainly in developed countries, and its rate is rising. Since the cause of this disease is not yet
known, early detection is the best way to decrease the breast cancer mortality. At present, early detection of
breast cancer is attained by means of mammography. An intelligent computer-aided diagnosis system can
be very helpful for radiologist in detecting and diagnosing cancerous cell patterns earlier and faster than
typical screening programs. This paper proposes a computer aided system for automatic detection and
classification of breast cancer in mammogram images. Intuitionistic Fuzzy C-Means clustering technique
has been used to identify the suspicious region or the Region of Interest automatically. Then, the feature
data base is designed using histogram features, Gray Level Concurrence wavelet features and wavelet
energy features. Finally, the feature database is submitted to self-adaptive resource allocation network
classifier for classification of mammogram image as normal, benign or malignant. The proposed system is
verified with 322 mammograms from the Mammographic Image Analysis Society Database. The results
show that the proposed system produces better results.
Breast cancer detection using ensemble of convolutional neural networksIJECEIAES
Early detection leading to timely treatment in the initial stages of cancer may decrease the breast cancer death rate. We propose deep learning techniques along with image processing for the detection of tumors. The availability of online datasets and advances in graphical processing units (GPU) have promoted the application of deep learning models for the detection of breast cancer. In this paper, deep learning models using convolutional neural network (CNN) have been built to automatically classify mammograms into benign and malignant. Issues like overfitting and dataset imbalance are overcome. Experimentation has been done on two publicly available datasets, namely mammographic image analysis society (MIAS) database and digital database for screening mammography (DDSM). Robustness of the models is accomplished by merging the datasets. In our experimentation, MatConvNet has achieved an accuracy of 94.2% on the merged dataset, performing the best amongst all the CNN models used individually. Hungarian optimization algorithm is employed for selection of individual CNN models to form an ensemble. Ensemble of CNN models led to an improved performance, resulting in an accuracy of 95.7%.
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
Breast Cancer Detection using Convolution Neural NetworkIRJET Journal
This document discusses using convolutional neural networks to detect breast cancer from images. It begins with an abstract stating that breast cancer starts as uncontrolled growth of breast cells that can form tumors. Early detection at the first stage allows for curing. The proposed approach uses a convolutional neural network to take input images, perform preprocessing, compare to a database of cancer images, and detect cancer along with its stage to recommend treatment. It discusses using CNN algorithms inspired by the visual cortex to perform image recognition like humans. The document provides definitions of CNNs and deep learning, technologies used like image processing, and concludes that detecting and treating cancer early at its first stage is preferable.
Segmentation of thermograms breast cancer tarek-to-slid shareTarek Gaber
This document presents a new method for segmenting regions of interest (ROIs) in breast thermograms to detect breast abnormalities. The method uses features extracted from the ROIs, like statistical and texture features, and supports vector machines for classification. It was tested on a database of 149 patients, achieving 100% accuracy in detecting normal vs. abnormal breasts. The method provides an automatic and low-cost approach to segmenting thermograms for breast cancer detection.
A Review of Super Resolution and Tumor Detection Techniques in Medical Imagingijtsrd
Images with high resolution are desirable in many applications such as medical imaging, video surveillance, astronomy etc. In medical imaging, images are obtained for medical investigative purposes and for providing information about the anatomy, the physiologic and metabolic activities of the volume below the skin. Medical imaging is an important diagnosis instrument to determine the presence of certain diseases. Therefore increasing the image resolution should significantly improve the diagnosis ability for corrective treatment. Brain tumor detection is used for identifying the tumor present in the Brain. MRI images help the doctors for identifying the Brain tumor size and shape of the tumor. The purpose of this report to provide a survey of research related super resolution and tumor detection methods. Fathimath Safana C. K | Sherin Mary Kuriakose ""A Review of Super Resolution and Tumor Detection Techniques in Medical Imaging"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23525.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/23525/a-review-of-super-resolution-and-tumor-detection-techniques-in-medical-imaging/fathimath-safana-c-k
Image processing techniques play a significant role in many areas in life, especially
in medical images, where they play a prominent role in diagnosing many diseases such
as detection of the brain tumor, breast cancer, kidney cancer, and the fractions.
Breast cancer is a common disease, regardless of the type of this disease, whether
it is benign or malignant, it is very dangerous and early detection may reduce the risk
of the disease spreading in the body leading to death. This work presents an approach
to detect breast cancer based on image processing algorithms, including image
preprocessing, enhancement, segmentation, Morphological operations, and feature
extraction to detect and extract the breast cancer region
This document proposes using a DenseNet-II neural network model to classify mammogram images as benign or malignant. It first preprocesses mammogram images through normalization and data augmentation. It then improves the original DenseNet model by replacing the first convolutional layer with an Inception structure, creating a new DenseNet-II model. This model, along with other common models, are tested on mammogram data and the DenseNet-II model achieves the highest average accuracy of 94.55% for benign-malignant classification.
A survey on enhancing mammogram image saradha arumugam academiaPunit Karnani
This document summarizes research on enhancing mammogram images to improve the detection of breast cancer. It discusses how mammogram images have low contrast and are noisy, making it difficult to identify microcalcifications that could indicate cancer. Various image enhancement techniques are reviewed that aim to improve contrast, reduce noise, and sharpen edges to make microcalcifications more visible. The techniques discussed include nonlinear unsharp masking, wavelet-based enhancement, adaptive contrast enhancement, and integrated wavelet decompositions. Evaluation of the techniques suggests they can improve cancer diagnosis by enhancing image details and increasing radiologist performance.
Skin cancer is a term given to the uncontrolled
growth of strange skin cells. It occurs whenever unrepaired
DNA damages to skin cells trigger mutations, or any other
genetic defects, that lead the skin cells to multiply readily
and form malignant tumors. Image processing is a
commonly used method for skin cancer detection from the
appearance of the affected area on the skin. The input to the
system is that the skin lesion image so by applying novel
image process techniques, it analyses it to conclude about
the presence of skin cancer. The Lesion Image analysis tools
checks for the various Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, (ABCD rule), etc. by
texture, size and form analysis for image segmentation and
have stages. The extracted feature parameters are
accustomed classify the image as traditional skin and
malignant melanoma cancerlesion.
Artificial Neural Network (ANN) is one of the
important branches of Artificial Intelligence, which has
been accepted as a brand-new technology in computer
science for image processing. Neural Networks is currently
the area of interest in medicine, particularly in the fields of
radiology, urology, cardiology, oncology, etc. Neural
Network plays a vital role in an exceedingly call network. It
has been used to analyze Melanoma parameters Like
Asymmetry, Border, Colour, Diameter, etc. which are
calculated using MATLAB from skin cancer images
intending to developing diagnostic algorithms that might
improve triage practices in the emergency department.
Using the ABCD rules for melanoma skin cancer, we use
ANN in the classification stage. Initially, we train the
network with known target values. The network is well
trained with 96.9% accuracy, and then the unknown values
are tested for the cancer classification. This classification
method proves to be more efficient for skin cancer
classification
A Virtual Instrument to Detect Masses In Breast Cancer using CAD toolstheijes
Breast cancer is the second-most driving and normal explanation behind death in view of tumor among one in every ten women. It has become a major health problem in the world over the past 50 years, and it has increased in recent years. Early detection is an effective way to diagnose and manage breast cancer. Mammography is the best and most suitable imaging technique for treatment of cancer at the early stage. The problems in mammography images such as high brightness value, dense tissues, noise and inefficient contrast level make analysis of these images a hard task for physicians for mass identification. This paper presents a CAD tool which are combination of image processing techniques to remove noise and enhancement of mammography images for identification & classification of masses. Efficient methods includes wavelet transformation and adaptive histogram equalization techniques, in addition with fusion techniques are used. Algorithms for identification of signs are tested on five patients, the associated abnormalities are clearly identified. The images for experimentation are taken from radiopedia. Experimental results show that a detection rate of 94.44% or higher can be achieved using this method, hence improved accuracy in breast cancer lesion detection. The proposed system achieves 100% sensitivity and 2.56 false positive for every image
Artificial neural network based cancer cell classificationAlexander Decker
This document summarizes an artificial neural network (ANN) based system called ANN-C3 for cancer cell classification using medical images. The system performs image pre-processing, segmentation using Harris corner detection and region growing, feature extraction of Tamura texture features, and classification using a neural network ensemble. Segmentation detects threshold points using Harris corner detection and performs region growing from these seed points. Feature extraction converts the image data into numerical form using Tamura texture features that capture variations in illumination and surfaces that human vision and surgeons use to differentiate cancerous and non-cancerous cells. The neural network is trained on a large set of labeled data to accurately classify cells.
This document discusses using MATLAB to detect breast cancer through analysis of mammogram and thermal images. It introduces breast cancer and explains that early detection is key to successful treatment. Currently, mammography and thermography are used for detection, but mammography has weaknesses like pain and radiation. The purpose of this project is to design a system to detect signs in mammogram and thermal images using image processing techniques in MATLAB. Mammogram images will be analyzed using morphology before feature extraction and classification. Thermal images will have features extracted from the heat distribution to identify possible cancer areas.
IRJET- Detection & Classification of Melanoma Skin CancerIRJET Journal
This document discusses methods for detecting and classifying melanoma skin cancer. It begins with an introduction to skin cancer and the importance of detecting melanoma early. It then reviews literature on existing techniques for melanoma detection using image processing and machine learning. The proposed system uses image segmentation, feature extraction using the ABCD criteria, principal component analysis to select key features, and support vector machine classification to determine whether images contain cancerous or non-cancerous lesions. The system aims to provide an accurate and fast evaluation of skin lesions to help in melanoma diagnosis.
This document presents a method for detecting melanoma skin cancer using image processing and machine learning techniques. Images of skin lesions are first segmented using active contour models. Features like color, size, shape and texture are then extracted from the segmented images. Texture is analyzed using local binary patterns (LBP). The extracted features are used to classify images as melanoma or non-melanoma using a support vector machine (SVM) classifier. The goal is to develop an automated system for early detection of melanoma to help reduce death rates from this dangerous form of skin cancer.
Melanoma Skin Cancer Detection using Image Processing and Machine Learningijtsrd
Dermatological Diseases are one of the biggest medical issues in 21st century due to its highly complex and expensive diagnosis with difficulties and subjectivity of human interpretation. In cases of fatal diseases like Melanoma diagnosis in early stages play a vital role in determining the probability of getting cured. We believe that the application of automated methods will help in early diagnosis especially with the set of images with variety of diagnosis. Hence, in this article we present a completely automated system of dermatological disease recognition through lesion images, a machine intervention in contrast to conventional medical personnel based detection. Our model is designed into three phases compromising of data collection and augmentation, designing model and finally prediction. We have used multiple AI algorithms like Convolutional Neural Network and Support Vector Machine and amalgamated it with image processing tools to form a better structure, leading to higher accuracy of 85 . Vijayalakshmi M M ""Melanoma Skin Cancer Detection using Image Processing and Machine Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23936.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/23936/melanoma-skin-cancer-detection-using-image-processing-and-machine-learning/vijayalakshmi-m-m
Breast cancer detection using Artificial Neural NetworkSubroto Biswas
This presentation summarizes research on diagnosing breast cancer using an artificial neural network. It begins with an introduction of the topic and presenter. The contents include descriptions of breast cancer, artificial neural networks, and backpropagation. It then details the breast cancer database used, the neural network model developed, and its performance in diagnosing cancers as benign or malignant. The conclusion is that neural networks show potential for medical diagnosis but require further optimization. Suggested future work includes exploring other training methods, feature selection, and adding treatment recommendations.
Microcalcification oriented content based mammogram retrieval for breast canc...Lazaros Tsochatzidis
Microcalcifications (MCs) provide a significant
early indication of breast malignancy. This work introduces a
supervised scheme for malignancy risk assessment of mammograms containing MCs. The proposed scheme employs shape and
textural features as input to a support vector machine (SVM)
ensemble, in order to perform content-based image retrieval
(CBIR) of mammograms. The retrieval performance of the
proposed scheme has been evaluated by taking into account
the variation of MCs morphology as defined in BI-RADS. In
our experiments, we use a set of 87 mammograms containing
MCs, obtained from the widely adopted DDSM database for
screening mammography. The experimental results demonstrate
that the proposed supervised CBIR scheme addresses effective
retrieval of MCs mammograms outperforming relevant unsupervised schemes.
RECOGNITION OF SKIN CANCER IN DERMOSCOPIC IMAGES USING KNN CLASSIFIERADEIJ Journal
The largest organ of the body is human skin. Melanoma is a fastest growing & deadliest cancer which starts in pigment cells (melanocytes) of the skin that mostly occurs on the exposed parts of the body. Early detection is vital in treating this type of skin cancer but the time and effort required is immense. Dermoscopy is a non invasive skin imaging technique of acquiring a magnified and illuminated image of a region of skin for increased clarity of the spots on the skin The use of machine learning and automation of the process involved in detection will not only save time but will also provide a more accurate diagnosis. The skin images collected from the databases cannot be directly classified by the automation techniques. The reason is twofold: (a) Lack of clarity in the features which is mainly due to the poor contrast of the raw image and (b) Large dimensions of the input image which causes the complexity of the system. Hence, suitable techniques must be adopted prior to the image classification process to overcome these drawbacks. The first drawback can be minimized by adopting suitable pre- processing techniques which can enhance the contrast of the input images. The second drawback is solved by incorporating the feature extraction technique which reduces the dimensions of the input image to high extent. Further, K-NN (K-Nearest Neighbor) classifier is used for classification of the given image into cancerous or non- cancerous.
Brain tumor detection and localization in magnetic resonance imagingijitcs
A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the
surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and
responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate
in the world. Great knowledge and experience on radiology are required for accurate tumor detection in
medical imaging. Automation of tumor detection is required because there might be a shortage of skilled
radiologists at a time of great need. We propose an automatic brain tumor detectionand localization
framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain
tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge
detection, modified histogram clustering and morphological operations. After morphological operations,
tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our
system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization
system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging.
The preliminary results demonstrate how a simple machine learning classifier with a set of simple
image-based features can result in high classification accuracy. The preliminary results also demonstrate the
efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to
extend this framework to detect and localize a variety of other types of tumors in other types of medical
imagery.
Computer Aided System for Detection and Classification of Breast CancerIJITCA Journal
Breast cancer is one of the most important causes of death among all type of cancers for grown-up and
older women, mainly in developed countries, and its rate is rising. Since the cause of this disease is not yet
known, early detection is the best way to decrease the breast cancer mortality. At present, early detection of
breast cancer is attained by means of mammography. An intelligent computer-aided diagnosis system can
be very helpful for radiologist in detecting and diagnosing cancerous cell patterns earlier and faster than
typical screening programs. This paper proposes a computer aided system for automatic detection and
classification of breast cancer in mammogram images. Intuitionistic Fuzzy C-Means clustering technique
has been used to identify the suspicious region or the Region of Interest automatically. Then, the feature
data base is designed using histogram features, Gray Level Concurrence wavelet features and wavelet
energy features. Finally, the feature database is submitted to self-adaptive resource allocation network
classifier for classification of mammogram image as normal, benign or malignant. The proposed system is
verified with 322 mammograms from the Mammographic Image Analysis Society Database. The results
show that the proposed system produces better results.
Breast cancer detection using ensemble of convolutional neural networksIJECEIAES
Early detection leading to timely treatment in the initial stages of cancer may decrease the breast cancer death rate. We propose deep learning techniques along with image processing for the detection of tumors. The availability of online datasets and advances in graphical processing units (GPU) have promoted the application of deep learning models for the detection of breast cancer. In this paper, deep learning models using convolutional neural network (CNN) have been built to automatically classify mammograms into benign and malignant. Issues like overfitting and dataset imbalance are overcome. Experimentation has been done on two publicly available datasets, namely mammographic image analysis society (MIAS) database and digital database for screening mammography (DDSM). Robustness of the models is accomplished by merging the datasets. In our experimentation, MatConvNet has achieved an accuracy of 94.2% on the merged dataset, performing the best amongst all the CNN models used individually. Hungarian optimization algorithm is employed for selection of individual CNN models to form an ensemble. Ensemble of CNN models led to an improved performance, resulting in an accuracy of 95.7%.
IRJET - Classification of Cancer Images using Deep LearningIRJET Journal
This document presents a methodology for classifying breast cancer histopathology images using deep learning. Specifically, it aims to classify images as either invasive ductal carcinoma (IDC) or non-IDC using a convolutional neural network (CNN) model. The proposed methodology involves preprocessing the images, building a CNN with convolutional, pooling and fully connected layers, training the model on labeled image data, and using the trained model to classify new images as IDC or non-IDC. The goal is to develop an automated system for early and accurate detection of breast cancer subtypes to improve diagnosis and patient outcomes.
Automated breast cancer detection system from breast mammogram using deep neu...nooriasukmaningtyas
All over the world breast cancer is a major disease which mostly affects the women and it may also cause death if it is not diagnosed in its early stage. But nowadays, several screening methods like magnetic resonance imaging (MRI), ultrasound imaging, thermography and mammography are available to detect the breast cancer. In this article mammography images are used to detect the breast cancer. In mammography image the cancerous lumps/microcalcifications are seen to be tiny with low contrast therefore it is difficult for the doctors/radiologist to detect it. Hence, to help the doctors/radiologist a novel system based on deep neural network is introduced in this article that detects the cancerous lumps/microcalcifications automatically from the mammogram images. The system acquires the mammographic images from the mammographic image analysis society (MIAS) data set. After pre-processing these images by 2D median image filter, cancerous features are extracted from the images by the hybridization of convolutional neural network with rat swarm optimization algorithm. Finally, the breast cancer patients are classified by integrating random forest with arithmetic optimization algorithm. This system identifies the breast cancer patients accurately and its performance is relatively high compared to other approaches.
The current big challenge facing radiologists in healthcare is the automatic detection and classification of masses in breast mammogram images. In the last few years, many researchers have proposed various solutions to this problem. These solutions are effectively dependent and work on annotated breast image data. But these solutions fail when applied to unlabeled and non-annotated breast image data. Therefore, this paper provides the solution to this problem with the help of a neural network that considers any kind of unlabeled data for its procedure. In this solution, the algorithm automatically extracts tumors in images using a segmentation approach, and after that, the features of the tumor are extracted for further processing. This approach used a double thresholding-based segmentation technique to obtain a perfect location of the tumor region, which was not possible in existing techniques in the literature. The experimental results also show that the proposed algorithm provides better accuracy compared to the accuracy of existing algorithms in the literature.
The document describes a proposed method for detecting and extracting brain tumors from MRI images using convolutional neural networks. The method involves 5 steps: 1) acquiring MRI images, 2) pre-processing the images, 3) segmenting the images, 4) extracting features, and 5) classifying the images using a convolutional neural network. The proposed method aims to automatically segment and detect brain tumors from MRI images more efficiently compared to existing methods that use support vector machines or other classifiers.
BRAIN TUMOR DETECTION USING CNN & ML TECHNIQUESIRJET Journal
1) The document proposes two methods for detecting brain tumors using MRI images - one using traditional machine learning classifiers after segmentation with FCM and feature extraction, and one using a convolutional neural network.
2) For the first method, MRI images undergo preprocessing like skull stripping and noise removal before segmentation with Fuzzy C-Means clustering and morphological operations. Features are then extracted and classified with models like KNN, logistic regression, random forest.
3) For the second method, a 5-layer CNN is used to directly classify tumor images. The CNN includes convolutional, max pooling, flatten, and dense layers to reduce parameters and detect tumors with 92.42% accuracy.
IRJET- A Novel Segmentation Technique for MRI Brain Tumor ImagesIRJET Journal
This document summarizes several research papers on techniques for segmenting brain tumors in MRI images. It discusses challenges in brain tumor segmentation and describes various approaches that have been proposed, including methods using feature selection, kernel sparse representation, multiple kernel learning (MKL), and post-processing techniques. The document also reviews state-of-the-art segmentation, registration, and modeling methods for brain tumor images and their performance.
This document presents a model to detect and classify brain tumors using watershed algorithm for image segmentation and convolutional neural networks (CNN). The model takes MRI images as input, pre-processes the images by converting them to grayscale and removing noise, then uses watershed algorithm for image segmentation and CNN for tumor classification. The CNN architecture achieves classification of three tumor types. Previous related works that also used deep learning methods for brain tumor detection and classification are discussed. The proposed system methodology involves inputting MRI images, pre-processing, segmentation using watershed algorithm, and classification of tumorous vs non-tumorous cells using CNN.
An Innovative Deep Learning Framework Integrating Transfer- Learning And Extr...IRJET Journal
This paper proposes a deep learning framework that uses transfer learning and an XGBoost classifier to classify breast ultrasound images. It uses a VGG16 model pre-trained on general images to extract features from ultrasound images. These features are then classified using an XGBoost classifier. On a dataset of breast ultrasound images, the approach achieved 96.7% accuracy, and precision/recall/F-scores of 100%/96%/96% for benign images, 95%/97%/96% for malignant images, and 95%/98%/97% for normal images, outperforming other automatic image classification methods.
The document describes a skin cancer detection mobile application that uses image processing and machine learning. The application analyzes skin images for characteristics of melanoma like asymmetry, border, color, diameter and texture. It trains a model using the MobileNet-v2 architecture on datasets containing thousands of images. The trained model achieves 70% accuracy in detecting melanoma and differentiating normal and abnormal skin lesions when tested on new images. The application has potential to help identify skin cancer in early stages and assist medical practitioners.
A Re-Learning Based Post-Processing Step For Brain Tumor Segmentation From Mu...CSCJournals
We propose a brain tumor segmentation method from multi-spectral MRI images. The method is based on classification and uses Multiple Kernel Learning (MKL) which jointly selects one or more kernels associated to each feature and trains SVM (Support Vector Machine).
First, a large set of features based on wavelet decomposition is computed on a small number of voxels for all types of images. The most significant features from the feature base are then selected and a classifier is then learned. The images are segmented using the trained classifier on the selected features. In our framework, a second step called re-learning is added. It consists in training again a classifier from a reduced part of the training set located around the segmented tumor in the first step. A fusion of both segmentation procures the final results.
Our algorithm was tested on the real data provided by the challenge of Brats 2012. This dataset includes 20 high-grade glioma patients and 10 low-grade glioma patients. For each patient, T1, T2, FLAIR, and post-Gadolinium T1 MR images are available. The results show good performances of our method.
Brain Tumor Detection and Classification Using MRI Brain ImagesIRJET Journal
This document presents research on detecting and classifying brain tumors using MRI images. It discusses:
1) Using k-means clustering for pre-processing MRI images to reduce noise and increase detection accuracy. Marker-controlled watershed transformation and grey-level co-occurrence matrix are then used for tumor detection and feature extraction.
2) Two classification methods are employed: support vector machine (SVM) and artificial neural network (ANN). SVM and ANN have been shown to accurately classify tumors in an effective manner.
3) The paper proposes an algorithm to differentiate between benign and malignant tumors using watershed segmentation and extracting grey-level co-occurrence matrix features from MRI images, which are then classified using SVM and AN
11.segmentation and feature extraction of tumors from digital mammogramsAlexander Decker
This document summarizes a proposed computer-aided detection (CAD) system for segmenting tumors and extracting features from digital mammograms to help radiologists diagnose breast cancer. The proposed CAD system includes five stages: 1) collecting input images and extracting regions of interest, 2) enhancing the regions of interest, 3) segmenting the tumors, 4) filtering noises, and 5) extracting texture and statistical features. Texture features like Haralick features extracted from gray-level co-occurrence matrices are calculated to classify tumors as benign or malignant. The system is tested on mammogram images from the Mammographic Image Analysis Society database to evaluate the segmentation and feature extraction methods.
11.[37 46]segmentation and feature extraction of tumors from digital mammogramsAlexander Decker
This document summarizes a proposed computer-aided detection (CAD) system for segmenting tumors and extracting features from digital mammograms to help radiologists diagnose breast cancer. The proposed CAD system includes five stages: 1) collecting input images and extracting regions of interest, 2) enhancing the regions of interest, 3) segmenting the tumors, 4) filtering noises, and 5) extracting texture and statistical features. Texture features like Haralick features extracted from gray-level co-occurrence matrices are calculated to classify tumors as benign or malignant. The system is tested on mammogram images from the Mammographic Image Analysis Society database to evaluate the segmentation and feature extraction methods.
This document outlines a project on brain tumor detection and diagnosis using convolutional neural networks. It discusses the objective of outlining current automatic segmentation techniques using CNNs. It then provides an introduction on the importance of accurate brain tumor segmentation for diagnosis and treatment. The remaining sections cover literature reviews on CNN segmentation methods, the overall architecture and working principles, applications and the future scope of this area of research.
Detection of Breast Cancer using BPN Classifier in MammogramsIRJET Journal
This document presents a method for detecting breast cancer in mammograms using a Back Propagation Network (BPN) classifier. The method involves preprocessing mammogram images, extracting Grey Level Co-occurrence Matrix (GLCM) texture features from wavelet sub-bands of the images, and training a BPN classifier on the features to classify mammograms as normal or abnormal. The BPN classifier is trained using a backpropagation algorithm to minimize error and accurately classify mammograms based on the extracted GLCM features. Experimental results found the method achieved a sensitivity of 100%, specificity of 75%, and accuracy of 90.91% for breast cancer detection and classification in mammograms.
The document summarizes a study that uses neural networks to detect breast lesions in medical digital images. The study aims to improve existing neural network architectures for better detection of possible lesions. Medical images are preprocessed and classified by neural networks to detect suspicious areas. The study presents a method using multilayer perceptrons trained through backpropagation to analyze image features and classify tissues as benign or malignant.
A Novel and Efficient Lifting Scheme based Super Resolution Reconstruction fo...CSCJournals
Mammography is the most effective method for early detection of breast diseases. However, the typical diagnostic signs, such as masses and microcalcifications, are difficult to be detected because mammograms are low contrast and noisy images. We concentrate on a special case of super resolution reconstruction for early detection of cancer from low resolution mammogram images. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. This paper describes a novel approach for enhancing the resolution of mammographic images. We are proposing an efficient lifting wavelet based denoising with adaptive interpolation for super resolution reconstruction. Under this frame work, the digitized low resolution mammographic images are decomposed into many levels to obtain different frequency bands. We use Daubechies (D4) lifting schemes to decompose low resolution mammogram images into multilevel scale and wavelet coefficients. Then our proposed novel soft thresholding technique is used to remove the noisy coefficients, by fixing optimum threshold value. In order to obtain an image of higher resolution adaptive interpolation is applied. Our proposed lifting wavelet transform based restoration and adaptive interpolation preserves the edges as well as smoothens the image without introducing artifacts. The proposed algorithm avoids the application of iterative method, reduces the complexity of calculation and applies to large dimension low-resolution images. Experimental results show that the proposed approach has succeeded in obtaining a high-resolution mammogram image with a high PSNR, ISNR ratio and a good visual quality.
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Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
Masters' whole work(big back-u_pslide)
1. Computer Assisted Screening of
Microcalcifications In Digitized
Mammogram For Early
Detection of Breast Cancer
Thesis Presentation
Nashid Alam
Registration No: 2012321028
annanya_cse@yahoo.co.uk
Supervisor: Prof. Dr. Mohammed Jahirul Islam
Department of Computer Science and Engineering
Shahjalal University of Science and TechnologyFriday, December 25, 2015
Driving research for better breast cancer treatment
“The best protection is early detection”
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2. Introduction
Breast cancer:
The most devastating and deadly diseases for women.
o Computer aided detection (CADe)
o Computer aided diagnosis (CADx) systems
We will emphasis on :
4. Background Interest
Interest comes from two primary backgrounds
1. Improvement of pictorial information
- - For Human Perception
How can an image/video be made more aesthetically pleasing
How can an image/video be enhanced to facilitate:
extraction of useful information
8. Micro-calcification
Micro-calcifications :
- Tiny deposits of calcium
- May be benign or malignant
- A first cue of cancer.
Position:
1. Can be scattered throughout the mammary gland, or
2. Occur in clusters.
(diameters from some µm up to approximately 200 µm.)
3. Considered regions of high frequency.
9. Micro-calcification
They are caused by a number of reasons:
1. Aging –
The majority of diagnoses are made in women over 50
2. Genetic –
Involving the BRCA1 (breast cancer 1, early onset) and
BRCA2 (breast cancer 2, early onset) genes
Micro-calcifications Pattern Determines :
The future course of the action-
I. Whether it be further investigatory techniques (as part of the triple
assessment), or
II.More regular screening
11. Mammography
USE:
I. Viewing x-ray image
II. Manipulate X-ray image on a computer screen
Mammography :
Process of using low-energy
x-rays to examine the human breast
Used as a diagnostic and a screening tool.
The goal of mammography :
The early detection of breast cancer
Mammography Machine
13. Mammogram
Mammogram:
An x-ray picture of the breast
Use:
To look for changes that are
not normal.
Result Archive:
The results are recorded:
1. On x-ray film or
2.Directly into a computer
mdb226.jpg
14.
15. Literature Review
To detect micro-calcifications in an automatic manner-
A number of methods have been proposed
These include:
Global and local thresholding
Statistical approaches
Neural networks
Fuzzy logic
Thresholding of wavelet coefficients and related techniques.
Literature Review
16. Literature Review
Camilus et al.(2011)[1] propose an efficient method
To identify pectoral mussel using:
Watershed transformation
Merging algorithm to combine catchment basins
MIAS database(84 mammograms)
Literature Review
17. Literature Review
Pronoj et al.(2011)[2] reviews on :
Thresholding techniques
Boundary based method
Hybrid techniques
Watershed transformation
Edge detection:
Sobel
Prewitt
Roberts
Laplacian of Gaussian
Zero-cross
Canny
Goal:
oTo improve quality of image
oFacilate further processing
oRemove noise
oRemove unwanted part
from the background
Literature Review
18. Oliver et al.(2010)[3] worked on:
Local feature extraction from a bank of filters.
Performs training steps:
-To automatically learn and select:
The features of microcalcifications.
Literature Review
Goal:
oTo obtain different microcalcification morphology
Literature Review
19. Oliver et al.(2012)[4] :
MC Detection based on:
microcalcifications morphology
Local image features-
Set of feature is trained a pixel-based
boosting classifier
Pixel-based boosting classifier:
At each round automatically selects the most
salient microcalcifictions features.
Literature Review
Goal:
oDetect microcalcification and cluster
Literature Review
20. Oliver et al. (2012)[4] :
Testing new mammogram:
Only salient fractures are computed
Microcalcification clusters are found:
By inspecting the local neighborhood of
each microcalcification.
Literature ReviewLiterature Review
21. Papadopoulus et al. (2008)[5] :
Microcalcification detection using neural network
Preprocessing image enhancement
Got best result by applying:
The local range modification algorithm
Redundant discrete wavelet linear stretching
and shrinkage algorithm.
Literature ReviewLiterature Review
22. Pal et al.(2008)[6] :
To detect microcalcification cluster used:
oWeighted density function:
-Position of microcalcifications
(take into account)
Used:
oMulti-layered perception network for selecting
29 features
Features are used :
- To segment mammograms
Literature ReviewLiterature Review
23. Razzi et al.(2009)[7] proposed :
A two-stage decomposition wavelet filtering
First stage:
Reduce background noise
Second stage:
A hard thresholding technique:
-To identify microcalcification
Cluster was considered if more then 3 microcalcifications were
detected in a 1cm2 area
Literature Review
24. Yu et al.(2010)[8] :
Clustered microcalcification detection
used combined :
-Model-based and statistical texture features
Firstly:
Suspicious region containing microcalcification were
detected using-
Wavelet filter and two thresholds
Literature Review
25. Yu et al. 2010 [8] proposed :
Secondly:
Textural features were extracted:
-From each suspicious region
Features classified by:
-A back propagated neural network
Texture features based on both:
oMorkov random fields and
oFractal models
Literature Review
26. Wang et.al.(1989) [9]:
The mammograms are:
-Decomposed into different frequency subbands.
The low-frequency subband discarded.
Literature Review
28. Strickland et.at (1996)[11] :
Used biorthogonal filter bank
-To compute four dyadic and
-Two cinterpolation scales.
Applied binary threshold-operator
-In six scales.
Literature Review
30. Zhibo et.al.(2007)[13]:
A method aimed at minimizing image noise.
Optimize contrast of mammographic image features
Emphasize mammographic features:
A nonlinear mapping function is applied:
-To the set of coefficient from each level.
Use Contourlets:
For more accurate detection of microcalcification clusters
The transformed image is denoised
-using stein's thresholding [18].
The results presented correspond to the enhancement of regions
with large masses only.
Literature Review
31. Fatemeh et.al.(2007) [14]:
Focus on:
-Analysis of large masses instead of microcalcifications.
- Detect /Classify mammograms:
Normal and Abnormal
Use Contourlets Transform:
For automatic mass classification
Literature Review
32. Balakumaran et.al.(2010) [15] :
Focus on:
- Microcalcification Detection
Use :
- Wavelet Transform and Fuzzy Shell Clustering
Literature Review
33. Literature Review
Zhang et.al.(2013)[16] :
Use Hybrid Image Filtering Method:
- Morphological image processing
- Wavelet transform technique
Focus on:
- Presence of microcalcification clusters
34. Literature Review
Lu et.al.(2013) [17]:
Use Hybrid Image Filtering Method:
- Multiscale regularized reconstruction
Focus on:
- Detecting subtle mass lesions in Digital breast
tomosynthesis (DBT)
- Noise regularization in DBT reconstruction
35. Literature Review
Leeuw et.al.(2014) [18]:
Use:
- Phase derivative to detect microcalcifications
- A template matching algorithm was designed
Focus on:
- Detect microcalcifications in breast
specimens using MRI
- Noise regularization in image reconstruction
36. Literature Review
Shankla et.al.(2014)[19] :
Automatic insertion of simulated microcalcification clusters
-in a software breast phantom
Focus on:
-Algorithm developed as part of a virtual clinical trial (VCT) :
-Includes the simulation of breast anatomy,
- Mechanical compression
- Image acquisition
- Image processing, displaying and interpretation.
38. Burdensome Task Of Radiologist :
Eye fatigue:
-Huge volume of images
-Detection accuracy rate tends to decrease
Non-systematic search patterns of humans
Performance gap between :
Specialized breast imagers and
general radiologists
Interpretational Errors:
Similar characteristics:
Abnormal and normal microcalcification
Problem Statement
Reason behind the problem( In real life):
39. The signs of breast cancer are:
Masses
Calcifications
Tumor
Lesion
Lump
Individual Research Areas
Problem Statement
41. Motivation to the research: Goal
Better Cancer Survival Rates
(Facilitate Early Detection ).
Provide “second opinion” : Computerized decision
support systems
Fast,
Reliable, and
Cost-effective
QUICKLY AND ACCURATELY :
Overcome the development of breast cancer
43. Develop a logistic model:
Feature extraction Challenge:
-To determine the likelihood of CANCEROUS AREA
-- From the image values of mammograms
Challenge:
Occur in clusters
The clusters may vary in size
from 0.05mm to 1mm in diameter.
Variation in signal intensity and contrast.
May located in dense tissue
Difficult to detect.
Challenges
45. Class of
Abnormality
Severity of
Abnormality
The Location
of The
Center of
The
Abnormality
and It’s
Diameter.
1 Calcification
(25)
1.Benign
(Calc-12)
2 Circumscribed
Masses
3 Speculated Masses
4 Ill-defined Masses
5 Architectural
Distortion
2.Malignant
(Cancerous)
(Calc-13)
6 Asymmetry
7
Normal
mdb223.jpg mdb226.jpg
mdb239.jpg mdb249.jpg
Figure01:X-ray image form mini-MIAS
database
Database: Mini-MIAS Databasehttp://peipa.essex.ac.uk/pix/mias/
Mammography Image Analysis Society (MIAS)
-An organization of UK research groups
46. • Consists of 322 images
-- Contains left and right breast images for 161 patients
• Every image is 1024 X 1024 pixels in size
• Represents each pixel with an 8-bit word
• Reduced in resolution
(Is not good enough for MC to be detectable)
•Very Poor Quality with .jpg compression effects
(Original MIAS doesn’t have such artifacts)
Mini-MIAS Database
Mammography Image Analysis Society (MIAS)
-An organization of UK research groups
Database: http://peipa.essex.ac.uk/pix/mias/
http://see.xidian.edu.cn/vipsl/database_Mammo.html
48. Chart 01: Gantt Chart of this M.Sc thesis
Showing the duration of task against the progression of time
Where Are We?
Our Current Research Stage
Thesis Semester
M-3
54. Partitioning a digital image into multiple regions (sets of pixels).
GOAL OF SEGMENTATION:
• To locate objects and boundaries (lines, curves, etc.) in
images.
• Result of image segmentation
-A set of regions that collectively cover the entire image. (a)
-A set of contours extracted from the image. (C)
• Each of the pixels in a region(1, 2, 3) are similar with respect to some
characteristic or computed property, such as color, intensity, or texture.
• Adjacent regions(1, 2, 3) are significantly different with respect to the
same characteristic(s).
Image Segmentation K-means Clustering
Goal: Removing X-ray Labeling And Pectoral muscles
(intensity <130) (intensity >200)
1
2
3
(a) Segmentation Part
(C) Final Segmented Image
(b)Original image
Why Segmentation?
55. Image Segmentation K-means Clustering
Goal: Removing X-ray Labeling And Pectoral muscles
Proposed framework for breast profile segmentation
56. Plan of Action:
1. Original Image 2. Segmentation Part
3. Final Segmented Image
4. Binary Image
Lactiferous Sinus, Ducts, lobule
(After removing pectoral muscles, fatty tissues, Ligaments)
(intensity <130) (intensity >200)
Separating the
Pectoral muscle
Image Segmentation K-means Clustering
Goal: Removing X-ray Labeling And Pectoral muscles
Keeping the
biggest Cluster
(K-means clustering)(mdb256.jpg)
(a)Without Noise (b)With Noise
5.Final Segmented Image
BINARY
Thresholding
For Two Different Ranges
57. 1.Morphological Analysis:
The Basic Operations are -
I.EROSION
II.DILATION
Using the basic operations we can perform -
a)OPENING
b)CLOSING
Advanced Morphological Operation can then be implemented using
Combinations Of All Of These
2.Image Smoothing/Filtering(Low pass):
-Averaging (Drawback: Can vanish interesting details)
Lactiferous Sinus, Ducts, lobule
(After removing pectoral muscles,
fatty tissues, Ligaments)
5.Final Segmented Image
(a)Without Noise (b)With Noise
Techniques:
Noise Removing
More On Image Morphology Later
Image Morphology:
-Deals with the shape (or morphology)
of features in an image
-Operate on bi-level images
58. Structuring Elements, Hits & Fits
B
A
C
Structuring Element
Fit: All on pixels in the structuring
element cover on pixels in the
image
Hit: Any on pixel in the structuring
element covers an on pixel in the
image
All morphological processing operations are based on these simple
ideas
Image Morphology Noise Removing
59. Structuring elements can be any size and make
any shape
However, for simplicity we will use rectangular
structuring elements with their origin at the
middle pixel
1 1 1
1 1 1
1 1 1
0 0 1 0 0
0 1 1 1 0
1 1 1 1 1
0 1 1 1 0
0 0 1 0 0
0 1 0
1 1 1
0 1 0
Structuring Elements, Hits & Fits
Image Morphology Noise Removing
61. •The structuring element is moved across every pixel in the original
image to give a pixel in a new processed image(very like spatial
filtering)
•The value of this new pixel depends on the operation performed
•There are two basic morphological operations:
Erosion and Dilation
Structuring Elements, Hits & Fits
Image Morphology Noise Removing
62. Erosion of image f by structuring element s is
given by f s
The structuring element s is positioned with its
origin at (x, y) and the new pixel value is
determined using the rule:
Erosion
=
otherwise0
fitsif1
),(
fs
yxg
Structuring Elements, Hits & Fits
A morphological opening of an image is an erosion followed by a dilation
Noise Removing 1. Morphological Analysis
63. What Is Erosion For?
Erosion can split apart joined objects
Erosion can split apart
%noise removing
se = strel('disk',25);
for i=1:19
erode_bolb =
imerode(largest_bolb,se);
end
Original image
Erosion by
3*3
square
structuring
element
Erosion by
5*5
square
structuring
element
Noise Removing 1. Morphological Analysis
Watch out: Erosion shrinks objects
66. Dilation
Image Morphology X-ray Label Removing
Dilation of image f by structuring element s is
given by f s
The structuring element s is positioned with its
origin at (x, y) and the new pixel value is
determined using the rule:
⊕
=
otherwise0
hitsif1
),(
fs
yxg
A morphological closing of an image is a dilation followed by an erosion
bw_image = im2bw(Binary_image);
imtool(bw_image)
se1 = strel ('line', 3,0);
se2 = strel ('line', 3,90);
for i=1:9
BW2= imdilate (bw_image, [se1
se2], 'full')
BW2 = imfill(BW2,'holes');
end
Noise Removing 1. Morphological Analysis
Structuring Elements, Hits & Fits
69. Dilation Example
Original image
Hole filling
Inside the blob(dilation)
Result image
(Label Removed)
mdb240.jpg
Binary image
A morphological closing of an image is an dilation followed by a erosion
%hole filling with in the bolb
se = strel('disk',39);
for i=1:19
closeBW_largest_bolb = imclose(largest_bolb,se);
70. After Removing Some NoiseImage Containing Noise
(mdb041.jpg)
Noise Removing 2.Image Smoothing/Filtering(Low pass):
71. After Removing Some NoiseImage Containing Noise(mdb041.jpg)
Noise Removing
Chosen Technique 2D MEDIAN FILTERING FOR SALT AND PEPPER NOISE
I = medfilt2(I, [1 5]);
Median filtering is a nonlinear operation often used in image processing to reduce "salt and pepper" noise. A median filter
is more effective than convolution when the goal is to simultaneously reduce noise and preserve edges. Since all the
mammograms are in high quality images, there is no need to perform median filtering
72. Why choosing?
2.Image Smoothing/Filtering(Low pass):
1. Morphological Analysis
OVER
-Does not work will on all the image [I = medfilt2(I, [1 5]);]
•No effect most of the time
•Absence of salt and peeper noise
-Tendency of loosing interesting details
75. (b)Segmentation Part (c) Final Segmented Image(a)Main Image (e)Image containing
only Pectoral muscle
Image segmentation K-means Clustering
Goal: Removing X-ray Labeling And Pectoral muscles
(d)Binary Image
What we need
mdb212.jpg
Issues 1.Biggest Cluster Does Not Contain Breast
Produce Artifacts In Pectoral muscle And Breast Region
What we have
Class: Benign
76. (b)Segmentation Part (c) Final Segmented Image(a)Main Image
(e)Image Containing
Only Pectoral muscle
mdb001.jpg
Image segmentation K-means Clustering
Goal: Removing X-ray Labeling And Pectoral muscles
(d)Binary Image
mdb214.jpg
mdb218.jpg
What We Need What We Have
Issues 1.Biggest Cluster Does Not Contain Breast
Produce Artifacts In Pectoral muscle And Breast Region
Class: Benign
77. (b)Segmentation Part (c) Final Segmented Image(a)Main Image
(e)Image containing duct, lobules,
sinus & Pectoral muscle
mdb001.jpg
Image segmentation K-means Clustering
Goal: Removing X-ray Labeling And Pectoral muscles
(d)Binary Image
mdb222.jpg
mdb223jpg
mdb226jpg
Whatwewant
WhatweHave
Issues 1.Biggest Cluster Does Not Contain Breast
Produce Artifacts In Pectoral muscle And Breast Region
Class: Benign
78. (b)Segmentation Part (c) Final Segmented Image(a)Main Image
mdb001.jpg
Image segmentation K-means Clustering
Goal: Removing X-ray Labeling And Pectoral muscles
(d)Binary Image
mdb240.jpg
mdb248.jpg
mdb252.jpg
Whatwewant
WhatweHave
(e)Image containing
duct, lobules, sinus & Pectoral musc
Issues 1.Biggest Cluster Does Not Contain Breast
Produce Artifacts In Pectoral muscle And Breast Region
Class: Benign
81. (a)Main Image
Image segmentation K-means Clustering
Goal: Removing X-ray Labeling And Pectoral muscles
mdb212.jpg
Issues Level Remain In The Image
Produce Artifacts In Pectoral muscle And Breast Region
Class: Malignant
mdb209.jpg
(b)Segmentation Part
(c) Final Segmented Image
(d)Binary Image
(e)Image
Containing
only label
82. Image segmentation K-means Clustering
Goal: Removing X-ray Labeling And Pectoral muscles
mdb212.jpg
Issues Pectoral muscle Remain In The Image
Produce Artifacts In Breast Region
Class: Malignant
(b)Segmentation Part (c) Final Segmented Image(a)Main Image
(e)Image containing
only pectoral muscle
(d)Binary Image
mdb216.jpg
mdb213.jpg
(b)Segmentation Part (c) Final Segmented Image(a)Main Image
(e)Image containing
only pectoral muscle
(d)Binary Image
88. X-ray Label Removing Finding The Big BLOB
The types of noise :
High Intensity Rectangular Label
Low Intensity Label
Tape Artifacts
89. 1.Binarizatin of original image.
2.Find the biggest blob.
Plan of Action:
function [outim] = bwlargestblob( im,connectivity)
if size(im,3)>1,
error('bwlargestblob accepts only 2 dimensional images');
end
[imlabel totalLabels] = bwlabel(im,connectivity);
sizeBlob = zeros(1,totalLabels);
for i=1:totalLabels,
sizeblob(i) = length(find(imlabel==i));
end
[maxno largestBlobNo] = max(sizeblob);
outim = zeros(size(im),'uint8');
outim(find(imlabel==largestBlobNo)) = 1;
end
img=im2bw(img);
(threshold luminance level-=0.5)
X-ray Label Removing Finding The Big BLOB
90. 1.Binarizatin of original image.
2.Find the biggest blob.
Plan of Action:
(threshold luminance level-=0.5)
Original image Binary Image
mdb219.jpg
(a) Artifacts (Hole) in ROI
(b)Absence of Ligaments and fatty tissue
mdb231.jpgmdb253.jpg
(c) Absence of pectoral muscles
Original image Binary Image
Label successfully removed Issues
X-ray Label Removing Finding The Big BLOB
91. Original image Binary Image
(threshold luminance level-=0.5)
mdb212.jpg
mdb214.jpg
mdb218.jpg
Original image Binary Image
(threshold luminance level-=0.5)
mdb219.jpg
mdb222.jpg
mdb223.jpg
Class: Benign Issue with fatty tissues and ligaments existence
X-ray Label Removing Finding The Big BLOB
92. Original image Binary Image
(threshold luminance level-=0.5)
Original image Binary Image
(threshold luminance level-=0.5)
mdb226.jpg
mdb227.jpg
mdb236.jpg
mdb240.jpg
mdb248.jpg
mdb252.jpg
Class: Benign Issue with fatty tissues and ligaments existence
X-ray Label Removing Finding The Big BLOB
93. Original image
Binary Image
(threshold luminance level-=0.5) Original image Binary Image
(threshold luminance level-=0.5)
Issue with fatty tissues and ligaments existenceClass: Malignant
mdb209.jpg
mdb211.jpg
mdb213.jpg
mdb216.jpg
mdb231.jpg
mdb233.jpg
X-ray Label Removing Finding The Big BLOB
94. Original image Binary Image
(threshold luminance level-=0.5)
Original image Binary Image
(threshold luminance level-=0.5)
Issue with fatty tissues and ligaments existenceClass: Malignant
mdb238.jpg
mdb239.jpg
mdb241.jpg
mdb245.jpg
mdb249.jpg
mdb253.jpg
mdb256.jpg
X-ray Label Removing Finding The Big BLOB
96. Plan of Action:
1.Binarize the image
2.Fill inside the hole region of the binary image
3.Finding the largest Blob:
4.Keep the Largest Blob and discard other blobs(to remove X-ray level)
function [outim] = bwlargestblob( im,connectivity)
if size(im,3)>1,
error('bwlargestblob accepts only 2 dimensional images');
end
[imlabel totalLabels] = bwlabel(im,connectivity);
sizeBlob = zeros(1,totalLabels);
for i=1:totalLabels,
sizeblob(i) = length(find(imlabel==i));
end
[maxno largestBlobNo] = max(sizeblob);
outim = zeros(size(im),'uint8');
outim(find(imlabel==largestBlobNo)) = 1;
X-ray Label Removing
97. Image Morphology
Experimental
results:
Goal: Region filling(Region inside the blob)
Original image
Finding biggest blob
(Level removed)
Hole filling
Inside the blob(dialation)
Result image
(Label Removed)
mdb240.jpg
mdb219.jpg
mdb231.jpg
Binary image
Direct Binarization Without Image enhancement
X-ray Label Removing
98. Experimental
results:
Original image
Result image
(Label Removed)
mdb240.jpg
Issues
mdb219.jpg
mdb231.jpg
Binary image
1.Does not always produce
appealing output
2.Some details are missing
(Details around Edge region )
Image Morphology
Goal: Region filling(Region inside the blob)
X-ray Label Removing
Direct Binarization Without Image enhancement
99. Original image Result image (Label Removed)
mdb240.jpg
mdb219.jpg
mdb231.jpg
Issues
1.Does not always produce
appealing output
2.Some details are missing
(Details around Edge region )
mdb212.jpg
mdb214.jpg
mdb219.jpg
mdb226.jpg
Experimental
results:
Image Morphology
Goal: Region filling(Region inside the blob)
X-ray Label Removing
Direct Binarization Without Image enhancement
100. -To find largest blob
Use -Otsu’s thresholding technique (graytrash) [20]
-Finding Bi-level the image(im2bw)
To Achieve The Desired Final Result:
-Apply
A Range Of Techniques on original image
[20] Otsu, N., "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on
Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.
X-ray Label Removing
101. X-ray Label Removing
1. Histogram equalization of the original X-ray image
2. Adjust image contrast
3. Apply Otsu's Thresholding Method [20] and
find bi-level the image which has several blobs in it.
4. Finding the Largest blob (Bwlargest.bolb)
5. Hole filling within the blob region
6. Keep the true pixel value covering only the area of largest
blob and discard other features from the original image
7. X-ray label is successfully removed
Plan of Action
[20] Otsu, N., "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on
Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.
102. 1.Original image
2.Histogram
Equalization
3.Contrast Image
4.Binary Image
mdb239.jpg
Combining Range of techniques
J = histeq(I); %histogram equalization
contrast_image = imadjust(J, stretchlim(J), [0 1]); %high contrast image
%Apply Thresholding to the Image
level = graythresh(contrast_image);
%GRAYTHRESH Global image threshold using %Otsu's method
bw_image = im2bw(contrast_image, level);%getting binary image
X-ray Label Removing
103. 5.Finding biggest blob
6.Hole filling
Inside the blob
7.Result image
(Label Removed)
Combining Range of techniquesX-ray Label Removing
113. Removing pectoral muscle
Keeping fatty tissues and ligaments
mdb212.jpg
(a)Main Image (b)Result Image
mdb213.jpg
(a)Main Image (b)Pectoral Muscle
mdb214.jpg
Main Image
Result Image
114. o Fatty tissue area
o Duct
o Lobules
o Sinus
o ligaments
Extraction of ROIRemoving pectoral muscle
Why removing pectoral muscle?
o Pectoral muscle will never contain micro-calcification
o Less Computational Time And Cost
-Operation on small image area
Existence of micro-calcification:
ROI
115. Edge Detection of
pectoral muscle
Removing pectoral muscle
Possible Approach To Edge-detection:
1.Scanning pixel value intensity at each points
2.find out the sudden big intensity change at the edge location
3.Mark the pixels at edge location
4.Estimate a straight line depending on the marked edge points
Approach-01:
Problem faced in Approach-01:
-Finding appropriate Thresholding value is an unsupervised method,
which will work on every image
-The threshold value must be found in an unsupervised manner
-Any predefined threshold value will not produce desired output for all image
mdb212 150 200
mdb214 130 205
mdb218 150 210
mdb219 120 200
mdb222 150 210
mdb223 150 225
mdb226 110 210
mdb227 150 230
mdb236 160 210
mdb240 150 200
mdb248 150 210
mdb252 140 210
116. Edge Detection of
pectoral muscle
Removing pectoral muscle
Possible Approach To Edge-detection:
1.Segment the image
2.Separate the pectoral muscle form the Duct, Lobules, Sinus region
Making all the pixels black(zero)resides in the fatty tissue and ligament area
3.Find the binary image of image found in step 2(it will be used as outer image)
4.Erode the image found in step-3 (it will be used as inner image)
5.Subsract the inner image from the outer image to get the edge
Approach-02:
Visualization in next slide
117. Edge Detection of
pectoral muscle
Removing pectoral muscle
1.Original image
mdb212.jpg
2.Segmentation Part
3.Fatty tissue
& Ligament removed
Possible Approach To Edge-detection(Approach-02):
118. Edge Detection of
pectoral muscle
Removing pectoral muscle
4.Binary Version(outer)
5.Binary Version(inner)
6.Edge(outer-inner)
Possible Approach To Edge-detection(Approach-02):
122. Edge Detection of
pectoral muscle
Removing pectoral muscle
1.Pectoral muscle and ligaments in fatty tissue area got merged
mdb218.jpg
mdb240.jpg
1.Original image 2.Segmentation Part
3.Fatty tissue
& Ligament removed4.Binary Version(outer)
5.Binary Version(inner) 6.Edge(outer-inner)
Problems faced in (Approach-02):
123. Edge Detection of
pectoral muscle
Removing pectoral muscle
2.Discontinuity in Pectoral muscle edge
mdb252.jpg
mdb226.jpg
mdb252.jpg
mdb248.jpg
1.Original image 2.Segmentation Part
3.Fatty tissue
& Ligament removed4.Binary Version(outer)
5.Binary Version(inner) 6.Edge(outer-inner)
Problems faced in (Approach-02):
124. Edge Detection of
pectoral muscle
Removing pectoral muscle
Problems faced in (Approach-02):
3.Same thresholding value(i.e.,130-210,) does not work well on all the images and
Produce improper output(complete black image as output)
1.Original image 2.Segmentation Part
3.Fatty tissue
& Ligament removed
4.Binary Version(outer)
5.Binary Version(inner)
6.Edge(outer-inner)
125. Edge Detection of
pectoral muscle
Removing pectoral muscle
Points to be noted from approach-2:
-Pectoral muscle a Triangular area
mdb212.jpg
mdb214.jpg
Based on this point:
Moving on to approach -03
mdb209.jpg
(2)Binary Image(1)Original Image
126. Triangle Detection
of pectoral muscle
Removing pectoral muscle
1.Fing the triangular area of the pectoral muscle region
I. Finding white seeding point
II. Finding the 1st black point of 1st row after getting a white seeding point
III. Draw a horizontal line in these two points.
IV. finding the 1st black point of 1st column after getting a white seeding point
V. Draw a vertical line and angular line.
2.Making all the pixels black(zero)resides in the pectoral muscle area
Approach-03(Triangle Detection of pectoral muscle):
Visualization in next slide
127. Triangle Detection
of pectoral muscle
Removing pectoral muscle
Approach-03(Triangle Detection of pectoral muscle):
mdb212.jpg
1.Original image
2.Contrast stretching
3.Binary of contrast image
stratching_in_range=uint8(imadjust(I,[0.01 0.7],[1 0]));
BW=~stratching_in_range;
128. Triangle Detection
of pectoral muscle
Removing pectoral muscle
Approach-03(Triangle Detection of pectoral muscle):
4.Triangle
5.Triangle Filled
6.muscle removed
131. Triangle Detection
of pectoral muscle
Removing pectoral muscle
mdb222.jpg
mdb226.jpg
mdb227.jpg
2.Contrast stretching1.Original image 3.Binary of contrast image 4.Triangle
Problems faced in (Approach-03):
5.Triangle Filled 6.muscle removed
The triangle does not always indicates the proper pectoral muscle area.
Reason: Discontinuity in edges (First 3 or 4 rows and columns)
it is caused by artifacts in mammogram
Class: Benign
132. Triangle Detection
of pectoral muscle
Removing pectoral muscle
Problems faced in (Approach-03):
mdb218.jpg
1.Original image 2.Contrast stretching3.Binary of contrast image 4.Triangle
mdb219.jpg
5.Triangle Filled 6.muscle removed
mdb218.jpg
The triangle does not always indicates the proper pectoral muscle area.
Reason: Discontinuity in edges (First 3 or 4 rows and columns)
Class: Benign
133. Triangle Detection
of pectoral muscle
Removing pectoral muscle
Problems faced in (Approach-03):
1.Original image 2.Contrast stretching3.Binary of contrast image 4.Triangle
mdb222.jpg
mdb219.jpg
5.Triangle Filled 6.muscle removed
The triangle does not always indicates the proper pectoral muscle area.
Reason: Discontinuity in edges (First 3 or 4 rows and columns)
Class: Benign
134. mdb223.jpg
2.Contrast stretching1.Original image 3.Binary of contrast image 4.Triangle 5.Triangle Filled 6.muscle removed
Triangle Detection
of pectoral muscle
Removing pectoral muscle
Problems faced in (Approach-03):
Defects in mammogram
Class: Benign
135. 2.Contrast stretching1.Original image
3.Binary of contrast image 4.Triangle
5.Triangle Filled 6.muscle removed
Triangle Detection
of pectoral muscle
Removing pectoral muscle
Problems faced in (Approach-03):
Defects in mammogram
mdb227.jpg
Class: Benign
136. Triangle Detection
of pectoral muscle
Removing pectoral muscle
1.Original image 2.Contrast stretching 3.Binary of contrast image 4.Triangle 5.Triangle Filled 6.muscle removed
Class: Malignant
mdb241jpg
Mdb249.jpg
mdb211.jpg
Problems faced in (Approach-03): 2.Discontinuity in edge lines causes false output
142. Main Novelty
-Contourlet Transform
- Specific Edge Filter (Prewitt Filter):
To enhance the directional structures of the image in
the contourlet domain.
- Recover an approximation of the mammogram
(with the microcalcifications enhanced):
Inverse contourlet transform is applied
Details in upcoming slides
143. Based on the classical approach used in transform methods for image processing.
1. Input mammogram
2. Forward CT
3. Subband Processing
4. Inverse CT
5. Enhanced Mammogram
Schematic representation of the system
144. Contourlet transformation
Implementation Based On :
• A Laplacian Pyramid decomposition
followed by -
• Directional filter banks applied on
each band pass sub-band.
The Result Extracts:
-Geometric information of images.
Details in upcoming slides
Main Novelty
145. Enhancement of the Directional Subbands
The Contourlet Transform
Laplacian Pyramid: 3 level
Decomposition
Frequency partitioning of a directional filter bank
Decomposition level l=3
The real wedge-shape frequency band is 23=8.
horizontal directions are corresponded by
sub-bands 0-3
Vertical directions are represented by
sub-bands 4-7
Details in upcoming slides
146. Enhancement of the Directional Subbands
The Contourlet Transform
Laplacian Pyramid: 3 level
Decomposition
Laplacian Pyramid Level-1
Laplacian Pyramid Level-2
Laplacian Pyramid Level-3
8 Direction
4 Direction
4 Direction
(mdb252.jpg)
147. Enhancement of the Directional Subbands
The Contourlet Transform
Laplacian Pyramid: 3 level
Decomposition
Wedge-shape frequency band is 23=8.
Horizontal directions are corresponded by
sub-bands 0-3
(1) sub-band 0
(2) sub-band 1
(3) sub-band 2
(4) sub-band 3
Contourlet coefficient at level 4
148. Enhancement of the Directional Subbands
The Contourlet Transform
Laplacian Pyramid: 3 level
Decomposition
Contourlet coefficient at level 4
Wedge-shape frequency band is 23=8.
Vertical directions are represented by
sub-bands 4-7
(5) sub-band 4
(6) sub-band 5
(7) sub-band 6
(8) sub-band 7
149. Enhancement of the Directional Subbands
The Contourlet Transform
Laplacian Pyramid: 3 level
Decomposition
(a) Main Image
(mdb252.jpg)
(b) Enhanced Image
(Average in all 8 direction)
150. (a) Main image
(Toy Image)
Contourlet Transform Example
(b) Horizontal Direction
(c) Vertical Direction
Directional filter banks: Horizontal and Vertical
151. Contourlet Transform Example
Directional filter banks
Horizontal directions are corresponded by
sub-bands 0-3
(1) sub-band 0
(2) sub-band 1
(3) sub-band 2
(4) sub-band 3
152. Contourlet Transform Example
Directional filter banks
Vertical directions are represented by
sub-bands 4-7
(5) sub-band 4
(6) sub-band 5
(7) sub-band 6
(8) sub-band 7
154. Why Contourlet?
•Decompose the mammographic image:
-Into directional components:
To easily capture the geometry of the image features.
Details in upcoming slides
Target
155. Details in upcoming slides
• This decomposition offers:
-Multiscale localization(Laplacian Pyramid) and
-A high degree of directionality and anisotropy.
Why Contourlet? Usefulness of Contourlet
Directionality:
Having basis elements
Defined in variety of directions
Anisotrophy:
Basis Elements having
Different aspect ration
156. Contourlet Transform Concept
(a)Wavelet
(Require a lot of dot for fine resolution)
(b)Contourlet
(Requires few different elongated shapes
in a variety of direction following the counter)
3 Different Size of Square Shape brush stroke
(Smallest, Medium, Largest) to provide Multiresolution Image
Example: Painter Scenario
157. Why Contourlet?
2-D Contourlet Transform (2D-CT) Discrete WT
Handles singularities such as edges in a
more powerful way
Has basis functions at many orientations has basis functions at three
orientations
Basis functions appear a several aspect
ratios
the aspect ratio of DWT is 1
CT similar as DWT can be
implemented using iterative filter banks.
Advantage of using 2D-CT over DWT:
Details in upcoming slides
159. Art-of-Action
An edge Prewitt
filter to enhance the
directional structures
in the image.
Contourlet transform allows
decomposing the image in
multidirectional
and multiscale subbands[21].
21. Laine A.F., Schuler S., Fan J., Huda W.: Mammographic feature enhancement by multiscale
analysis, IEEE Transactions on Medical Imaging, 1994, vol. 13, no. 4,(1994) pp. 7250-7260
This allows finding
• A better set of edges,
• Recovering an enhanced mammogram
with better visual characteristics.
Microcalcifications have a very small size
a denoising stage is not implemented
in order to preserve the integrity of the injuries.
Decompose the
digital mammogram
Using
Contourlet transform
(b) Enhanced image
(mdb238.jpg)
(a) Original image
(mdb238.jpg)
160. Method
CT is implemented in two stages:
1. Subband decomposition stage
2. Directional decomposition stages.
Details in upcoming slides
161. Method
1. Subband decomposition stage
For the subband decomposition:
- The Laplacian pyramid is used [22]
Decomposition at each step:
-Generates a sampled low pass version of the original
-The difference between :
The original image and the prediction.
22. Park S.-I., Smith M. J. T., and Mersereau R. M.: A new directional Filter bank for image analysis and
classification, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '99), vol.
3, (1999) pp. 1417-1420
Details ……..
162. Method
1. Subband decomposition stage
Details ……..
1. The input image is first low pass filtered
2. Filtered image is then decimated to get a coarse(rough) approximation.
3. The resulting image is interpolated and passed through Synthesis
filter.
4. The obtained image is subtracted from the original image :
To get a bandpass image.
5. The process is then iterated on the coarser version (high resolution)
of the image.
Plan of Action
163. Method
2.Directional Filter Bank (DFB)
Details ……..
Implemented by using an L-level binary tree decomposition :
resulting in 2L subbands
The desired frequency partitioning is obtained by :
Following a tree expanding rule
- For finer directional subbands [22].
22. Park S.-I., Smith M. J. T., and Mersereau R. M.: A new directional Filter bank for image analysis and
classification, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '99), vol.
3, (1999) pp. 1417-1420
164. The Contourlet Transform
The CT is implemented by:
Laplacian pyramid followed by directional filter banks (Fig-01)
Input image
Bandpass
Directional
subbands
Bandpass
Directional
subbands
Figure 01: Structure of the Laplacian pyramid together with the directional filter bank
The concept of wavelet:
University of Heidelburg
The CASCADE STRUCTURE allows:
- The multiscale and
directional decomposition to be
independent
- Makes possible to:
Decompose each scale into
any arbitrary power of two's number of
directions(4,8,16…)
Figure 01
Details ………….
Decomposes The Image Into Several Directional Subbands And Multiple Scales
165. Figure 02: (a)Structure of the Laplacian pyramid together with the directional filter bank
(b) frequency partitioning by the contourlet transform
(c) Decomposition levels and directions.
(a) (b)
Input
image
Bandpass
Directional
subbands
Bandpass
Directional
subbands
Details….
(c)
Denote
Each subband by yi,j
Where
i =decomposition level and
J=direction
The Contourlet Transform
Decomposes The Image Into Several Directional Subbands And Multiple Scales
166. The processing of an image consists on:
-Applying a function to enhance the regions of
interest.
In multiscale analysis:
Calculating function f for each subband :
-To emphasize the features of interest
-In order to get a new set y' of enhanced subbands:
Each of the resulting enhanced subbands can be
expressed using equation 1.
)('
, , jiyfjiy = ………………..(1)
-After the enhanced subbands are obtained, the inverse
transform is performed to obtain an enhanced image.
Enhancement of the Directional Subbands
The Contourlet Transform
Denote
Each subband by yi,j
Where
i =decomposition level and
J=direction Details….
167. Enhancement of the Directional Subbands
The Contourlet Transform
Details….
The directional subbands are enhanced using equation 2.
=)( , jiyf
)2,1(,1 nnW jiy
)2,1(,2 nnW jiy
If bi,j(n1,n2)=0
If bi,j(n1,n2)=1
………..(2)
Denote
Each subband by yi,j
Where
i =decomposition level and
J=direction
W1= weight factors for detecting the surrounding tissue
W2= weight factors for detecting microcalcifications
(n1,n2) are the spatial coordinates.
bi;j = a binary image containing the edges of the subband
Weight and threshold selection techniques are presented on upcoming slides
168. Enhancement of the Directional Subbands
The Contourlet Transform
The directional subbands are enhanced using equation 2.
=)( , jiyf
)2,1(,1 nnW jiy
)2,1(,2 nnW jiy
If bi,j(n1,n2)=0
If bi,j(n1,n2)=1
………..(2)
Binary edge image bi,j is obtained :
-by applying an operator (prewitt edge detector)
-to detect edges on each directional subband.
In order to obtain a binary image:
A threshold Ti,j for each subband is calculated.
Details….
Weight and threshold selection techniques are presented on upcoming slides
169. Threshold Selection
The Contourlet Transform
Details….
The microcalcifications
appear :
On each subband
Over a very
homogeneous background.
Most of the transform coefficients:
-The coefficients corresponding to the
injuries are far from background value.
A conservative threshold of 3σi;j is selected:
where σi;j is the standard deviation of the corresponding subband y I,j .
170. Weight Selection
The Contourlet Transform
Exhaustive tests:
-Consist on evaluating subjectively a set of 322 different mammograms
-With Different combinations of values,
The weights W1, and W2 are determined:
-Selected as W1 = 3 σi;j and W2 = 4 σi;j
These weights are chosen to:
keep the relationship W1 < W2:
-Because the W factor is a gain
-More gain at the edges are wanted.
196. Metrics
To compare the ability of :
Enhancement achieved by the proposed method
Why?
1. Measurement of distributed separation (MDS)
2. Contrast enhancement of background against target (CEBT) and
3. Entropy-based contrast enhancement of background against target (ECEBT) [23].
Measures used to compare:
23. Sameer S. and Keit B.: An Evaluation on Contrast Enhancement Techniques for Mammographic Breast Masses, IEEE
Transactions on Information Technology in Biomedicine, vol. 9, (2005) pp. 109-119
197. Metrics
1. Measurement of Distributed Separation
(MDS)
Measures used to compare:
The MDS represents :
How separated are the distributions of each mammogram
…………………………(3)MDS = |µucalcE -µtissueE |- |µucalc0 -µtissue0 |
µucalcE = Mean of the microcalcification region of the enhanced image
µucalc0 = Mean of the microcalcification region of the original image
µtissueE = Mean of the surrounding tissue of the enhanced image
µtissue0 = Mean of the surrounding tissue of the enhanced image
Defined by:
Where:
198. Metrics
2. Contrast enhancement of background against
target (CEBT)
Measures used to compare:
The CEBT Quantifies :
The improvement in difference between the background and the target(MC).
…………………………(4)
0µucalc
Eµucalc
0µtissue
0µucalc
Eµtissue
Eµucalc
CEBT
σ
σ
−
=
Defined by:
Where:
Eµucalcσ
0µucalcσ
= Standard deviations of the microcalcifications region in the enhanced image
= Standard deviations of the microcalcifications region in the original image
199. Metrics
3. Entropy-based contrast enhancement of
background against target (ECEBT)
Measures used to compare:
The ECEBT Measures :
- An extension of the TBC metric
- Based on the entropy of the regions rather
than in the standard deviations
Defined by:
Where:
…………………………(5)
0µucalc
Eµucalc
0µtissue
0µucalc
Eµtissue
Eµucalc
ECEBT
ε
ζ
−
=
= Entropy of the microcalcifications region in the enhanced image
= Entropy of the microcalcifications region in the original image
Eµucalcζ
0µucalcε
205. Experimental Results Analysis
Mesh plot of a ROI containing microcalcifications
(a)The original
mammogram
(mdb252.bmp)
(b) The enhanced
mammogram
using CT
208. More peaks corresponding to microcalcifications are enhanced
The background has a less magnitude with respect to the peaks:
-The microcalcifications are more visible.
Observation:
Experimental Results Analysis
209. Experimental Results
(a)Original image (b)CT method (c)The DWT Method
These regions contain :
• Clusters of microcalcifications (target)
• surrounding tissue (background).
For visualization purposes :
The ROI in the original mammogram
are marked with a square.
ACHIEVEMENT
Improved Computer Assisted
screen of mammogram
210. Achievements!
Enhancement of MC in digitized mammogram
for diagnostic support system
Figure: Diagnostic support system
MC
Suspected
Digital mammography systems :
Presents images to the Radiologist
with properly image processing applied.
211. Achievements!
(b) Enhanced image
(mdb238.jpg)
(a) Original image
ROI
(mdb238.jpg)
(a) Original image
WHOLE IMAGE
(mdb238.jpg)
Digital mammography systems :
Presents images to the Radiologist
with properly image processing applied.
Hard to find MC Easy to find MC
While
physicians
interact with
The information in an image
During interpretation process
212. Achievements!!
Enhancement of MC in digitized mammogram
With improved visual understanding, we can develop :
ways to further improve :
o Decision making and
o Provide better patient care
Improved
Computer Assisted Screening
Goal Accomplished
215. Why Feature Extraction?
Finding a feature:
That has the most
discriminative information
The objective of feature selection:
Differs from its immediate surroundings by texture
color
intensity
Fig: MC features (Extracted Using Human Visual Perception)
216. Why Feature Extraction?
Finding a feature:
That has the most
discriminative information
The objective of feature selection:
Differs from its immediate surroundings by texture
color
intensity
Fig: MC (Irregular in shape and size)
(Extracted Using Human Visual Perception)
More
Features:
Shape
Size
217. Why Feature Extraction?
Problems With MC Features:
Irregular in shape and size
No definite pattern
Low Contrast -
Located in dense tissue
Hardly any color intensity variation
MC Feature
Fig: MC (Irregular in shape and size)
(Extracted Using Human Visual Perception)
218. Why Feature Extraction? MC Feature
How radiologist deals with feature Detection/Recognition issue ?
Using Human Visual Perception
219. Why Feature Extraction? MC Feature
How Radiologist (Using Human Eye) deals with feature
detection/Recognition issue ?
Using Human Visual Perception
Humans are equipped with sense organs e.g. eye
-Eye receives sensory inputs and
-Transmits sensory information to the brain
http://www.simplypsychology.org/perception-theories.html
220. Why Feature Extraction? MC Feature
Teach the machine to see like just we doObjective:
Irregular in shape and size
No definite pattern
Low Contrast -
Located in dense tissue
Hardly any color intensity variation
Machine Vision Challenges:
-To make sense of what it sees
In Real:
MC is Extracted Using Human Visual
Perception
222. Improving the prediction performance of CAD
Providing a faster, reliable and cost-effective prediction
Features will facilitate:
Fig: MC Point features (Extracted Using SURF point feature algorithm)
Point feature algorithm (SURF)Approach:
223. SURF point algorithm
Detect a specific object
Speeded-Up Robust Features (SURF) algorithm to find blob features.
Objective
based on
Finding point correspondences
between .
The reference and the target image
Reference Image Target Image
224. Feature Extraction
Context in using the features:
I. Finding Key points
II. Matching key points
III. Classification
Strongest feature point
(Reference Image)
Strongest feature point
(Target Image)
SURF point algorithm
Speeded-Up Robust Features (SURF) algorithm to find blob features.
225. Feature Extraction
Strongest feature point
(Reference Image)
Strongest feature point
(Target Image)
SURF point algorithm
Speeded-Up Robust Features (SURF) algorithm to find blob features.
Code Fragment (Detect and visualize feature points.)
%Detect feature points in the reference image
elephantPoints = detectSURFFeatures(elephantImage);
%Detect feature points in the target image
scenePoints = detectSURFFeatures(sceneImage);
% visualize feature points in the reference image.
figure;
imshow(elephantImage);
hold on;
plot(selectStrongest(elephantPoints, 100));
title('100 Strongest Feature Points from Elephant
Image');
% Extract Feature Points
% Extract feature descriptors at the interest points in
both images.
[elephantFeatures, elephantPoints] =
extractFeatures(elephantImage, elephantPoints);
[sceneFeatures, scenePoints] =
extractFeatures(sceneImage, scenePoints);
226. Context in using the features:
Feature ExtractionSURF point algorithm
Speeded-Up Robust Features (SURF) algorithm to find blob features.
I. Finding Key points
II. Matching key points
III. Classification
Fig. Putatively Matched Points (Including Outliers )
228. Context in using the features:
Feature ExtractionSURF point algorithm
Speeded-Up Robust Features (SURF) algorithm to find blob features.
I. Finding Key points
II. Matching key points
III. Classification
Estimate Geometric Transformation and Eliminate Outliers
229. Context in using the features:
Feature ExtractionSURF point algorithm
Speeded-Up Robust Features (SURF) algorithm to find blob features.
I. Finding Key points
II. Matching key points
III. Classification
230. Feature ExtractionSURF point algorithm
Speeded-Up Robust Features (SURF) algorithm to find blob features.
% Estimate Geometric Transformation and Eliminate Outliers
% estimateGeometricTransform calculates the transformation relating the matched points,
% while eliminating outliers. This transformation allows us to localize the object in the scene
[tform, inlierElephantPoints, inlierScenePoints] = ...
estimateGeometricTransform(matchedElephantPoints, matchedScenePoints, 'affine');
figure;
% Display the matching point pairs with the outliers removed
showMatchedFeatures(elephantImage, sceneImage, inlierElephantPoints, ...
inlierScenePoints, 'montage');
title('Matched Points (Inliers Only)');
% Get the bounding polygon of the reference image.
elephantPolygon = [1, 1;... % top-left
size(elephantImage, 2), 1;... % top-right
size(elephantImage, 2), size(elephantImage, 1);... % bottom-right
1, size(elephantImage, 1);... % bottom-left
1,1]; % top-left again to close the polygon
newElephantPolygon = transformPointsForward(tform, elephantPolygon);
figure;
imshow(sceneImage);
hold on;
line(newElephantPolygon(:, 1), newElephantPolygon(:, 2), 'Color', 'g');
title('Detected Elephant');
Code
Fragment
233. Local Feature Detection and Extraction
Local features :
A pattern or structure :
Point, edge, or small image patch.
- A pattern or structure found in an image,
Differs from its immediate surroundings by
texture
color
intensity
- Associated with an image patch that:
Fig.1 : Some Image Patch We used for Feature Point Detection Purpose
234. Local Feature Detection and Extraction
Applications:
Image registration
Object detection and classification
Tracking
Motion estimation
Using local features
facilitates:
handle scale changes
rotation
occlusion
Detectors /Methods :
• FAST
• Harris
• Shi & Tomasi
• MSER
• SURF
Feature Descriptors:
SURF
FREAK
BRISK
HOG descriptors
Detecting corner features
detecting blob/point features.
Speeded-Up Robust Features (SURF) algorithm to find blob features.
235. Detector Feature Type Scale Independent
FAST [24] Corner No
Minimum eigen value
algorithm[25]
Corner No
Corner detector [26] Corner No
SURF [27] Blob/ Point Yes
BRISK [28] Corner Yes
MSER [29] Region with uniform
intensity
Yes
Local Feature Detection and Extraction
Why Using SURF Feature?
Trying to identify MC cluster Blob
Speeded-Up Robust Features (SURF) algorithm to find blob features.
236. detectSURFFeatures(boxImage);
selectStrongest(boxPoints,100)
extractFeatures(boxImage,boxPoints)
matchFeatures(boxFeatures,sceneFeatures);
Speeded-Up Robust Features (SURF) algorithm to find blob features.
Read the reference image
containing the object of interest
Read the target image containing a
cluttered scene.
Detect feature points in both
images.
Select the strongest feature points
found in the reference image.
Select the strongest feature points
found in the target image.
Extract feature descriptors at the
interest points in both images.
Find Putative Point Matches using
their descriptors
Display putatively matched
features.
Locate the Object in the Scene
Using Putative Matches
Start
End
237. SURF Point Detection
1.Read the reference
image
containing MC cluster
2.Target image containing MC.
2.Strongest feature
point
in MC cluster
2. Strongest Feature point in Target Image
3. No match point Found
Speeded-Up Robust Features (SURF) algorithm to find blob features.
238. Are we getting less feature points?
Figure: No match point Found
239. No. of SURF feature points: 2 No. of SURF feature points: 47
Image Size
256*256
Image Size
549*623
Image
mdb238.jpg
More features from the image extracted
(most points are mismatched)
To extract relevant feature point from the image
Case 1:
Consider Big Reference Image
To get more feature points
240. To extract relevant feature point from the image
Case 2: Consider A bigger Reference Image and
Whole mammogram as Target Image
1. Image of MC Cluster(mdb238.jpg)
(256*256)
2. Main mammogram (mdb238.jpg) 1024*1024
3. 100 strongest point of ROI) (256*256) 4. 300 strongest point of
Main mammogram (mdb238.jpg) 1024*1024
To get more feature points
241. What we finally have? No putative match Point
To extract relevant feature point from the image
Case 2: Consider A bigger Reference Image and
Whole mammogram as Target Image
To get more feature points
242. 1. Image of an Microcalcification Cluster
Too small ROI will cause less feature points to match
2. 23 strongest points
Among 100 Strongest Feature Points
from reference image
Reference image: mdb248.jpg
Image size: 256 *256
detectSURFFeatures(mc_cluster);
Problem 1: less number of feature points to match
SURF Feature Point
243. 4. Only 1 strongest points
Among 300 Strongest Feature Points
from Scene Image
Too small ROI will cause less feature points to match
3. Image of a Cluttered Scene
Scene image: mdb248.jpg
Image size: 427*588
detectSURFFeatures(sceneImage)
Problem 1: less number of feature points to match
SURF Feature Point
244. Result of small ROI (256*256):
No Putative Point Matches
[mcFeatures, mc_Points] = extractFeatures(mc_cluster, mc_Points);
[sceneFeatures, scenePoints] = extractFeatures(sceneImage, scenePoints);
mcPairs = matchFeatures(mcFeatures, sceneFeatures);
matchedmcPoints = mc_Points(mcPairs(:, 1), :);
matchedScenePoints = scenePoints(mcPairs(:, 2), :);
showMatchedFeatures(mc_cluster, sceneImage, matchedmcPoints, ... matchedScenePoints, 'montage');
Problem 1: less number of feature points to match
SURF Feature Point
245. Image Image Size Number of feature points
1190*589 15
588*427 23
256*256 1
541*520 86
Varying image size to see the effect to get SURF feature points
246. Approach-01 to solve:
Considering the Whole image(Label and Pectoral Muscle)
Image size No. of SURF
feature points
1024*1024 63
Target:
To acquire more feature
247. 2. Irrelevant Feature Points
Image size No. of SURF
feature points
1024*1024 63
1. Less Feature points
Approach-01 to solve:
Considering the Whole image(Label and Pectoral Muscle)
Target:
To acquire more feature
Result:
248. Image size No. of SURF
feature points
255*256 2
Approach-02 :
Detect feature from the cropped image
Target:
To acquire more feature
249. Image size No. of SURF
feature points
256*256 2
Target:
To acquire more feature
2. Relevant Feature Points
1. Less Feature pointsResult:
Approach-02 :
Detect feature from the cropped image
250. Observation from approach 1 and 2
1. Image Size does not affect
The number of Feature Points
2. Zooming an image may
help to extract relevant features
from the image
(very few points to match)
mdb238.jpg
Image Size: 1024*1024
mdb238.jpg
Image Size: 256*256
251. Observation:
Varying image size is not helping to get feature points
Image of an Microcalcification Cluster
23 strongest points
Among 100 Strongest Feature Points
from reference image
Reference image: mdb248.jpg
Image size: 256 *256
Only 1 strongest points
Among 300 Strongest Feature Points
from Scene Image
Scene image: mdb248.jpg
Image size: 427*588
252. Observing SURF Drawback
This method works best for :
-- Detecting a specific object
(for example, the elephant in the reference image,
rather than any elephant.)
-- Non-repeating texture patterns
-- Unique feature
This technique is not likely to work well for:
-- Uniformly-colored objects
-- Objects containing repeating patterns.
detecting blob /point features.AIM Failed
Speeded-Up Robust Features (SURF) algorithm to find blob features.
268. Using Gabor Filter
• Make Gabor patch:
2; 2; 0.7854
2; 0.5; 0.7854 2; 2; 1.5708
5; 0.5; 1.5708
5; 2; 0.7854
2; 0.5; 1.5708
5; 0.5; 0.7854
5; 2; 1.5708
• Correlate the patch with image
-To extract features of MC
⊗ =
269. 0 10 20 30 40 50 60 70 80 90 100
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Creating Gabor Mask
1. Linear RAMP
2. Linear RAMP values across:
Columns Xm (left) and Rows Ym (Right)
3. Linear RAMP values across
- Columns(Xm)
The result in the spatial domain
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Xm (Across Columns)
Ym- (Across rows)
270. 4. Across Columns, Xm :
a) Increase frequency
b )Use gray color map
6. Adding Xm and Ym
together in
different proportions
5. Across Rows, Ym :
a) Increase frequency
b )Use gray color map
Creating Gabor Mask
283. mini-MIAS drawbacks
Benign mdb218
Original Enhanced
Are these really enhanced?
-There is more detail,
but could be noise.
-Enhanced version
seems to contain
compression artifacts.
Question Arise?
Gabor Effects
284. mini-MIAS drawbacks
Enhanced version can contain Noise
Experimental Realization
1.Very Poor Quality with
.jpg compression effects
a) Original image b) Enhanced image b) Enhanced imagea) Original image
mdb209
mdb213
mdb219
mdb249
285. mini-MIAS drawbacks
Not good enough for MC to be detectable
Experimental Realization
2. Reduced in resolution
Benign mdb218
Original Enhanced
286. mini-MIAS drawbacks
Not good enough for MC to be detectable
Experimental Realization
2. Reduced in resolution
Benign mdb218
Original
Enhanced
Where is MC?
OBSERVATION:
-There is more detail,
but could be noise.
-Enhanced version
seems to contain
compression artifacts.
303. More Evaluation (Gabor)
Malignant mdb239.jpg
OBSERVATION:
-Image Smoothing
to remove edge will
Vanish the existence
of MC
-No definite feature of MC
- Noise dominant
304. More Evaluation (Gabor)
Malignant mdb241.jpg
OBSERVATION:
-Image Smoothing
to remove edge will
Vanish the existence
of MC
-No definite feature of MC
- Noise dominant
305. More Evaluation (Gabor)
Malignant mdb249.jpg
OBSERVATION:
-Image Smoothing
to remove edge will
Vanish the existence
of MC
-No definite feature of MC
- Noise dominant
309. Observation & Drawing Conclusion Feature Detection
• Reduced in resolution
(Is not good enough for MC to be detectable)
• Very Poor Quality with .jpeg compression effects
(Original MIAS doesn’t have such artifacts)
Limitations of mini-MIAS:
What can be done using mini-MIAS ?
• Can be used for big object detection
(Pectoral Muscle, X-ray Label, Tumor, Mass detection)
Conclusion: mini-MIAS is not a good choice for:
MC feature extraction
311. Observation & Drawing Conclusion Feature Detection
Database Name Authority
MIAS ( Mammographic Image Analysis Society Digital
Mammogram Database)
Mammography Image
Analysis Society- an
organization of UK
research groups
DDSM (Digital Database for Screening Mammogram) University Of South
Florida
NDM (National Mammography Database) American College Of
Radiology
LLNL/UCSF Database
Lawrence Livermore
National Laboratories
(LLNL),
University of California
at San Fransisco (UCSF)
Radiology Dept.
312. Observation & Drawing Conclusion Feature Detection
Database Name Authority
Washington University Digital Mammography Database Department of
Radiology at the
University of
Washington
Nijmegen Database Department of
Radiology at the
University of
Nijmegen, the
Netherlands
Málaga mammographic database University of Malaga
Central Research
Service (SCAI) ,Spain
BancoWeb LAPIMO Database Electrical Engineering
Department at
Universidad de São
Paulo, Brazil
315. Research Findings
Improved computer assisted
screening of mammogram
Detection and removal of big objects:
- Pectoral Muscle
- X-ray level
MC
Suspected
316. Observation & Drawing Conclusion On
Feature Detection
• Reduced in resolution
(Is not good enough for MC to be detectable)
• Very Poor Quality with .jpeg compression effects
(Original MIAS doesn’t have such artifacts)
Limitations of mini-MIAS:
What can be done using mini-MIAS ?
• Can be used for big object detection
(Pectoral Muscle, X-ray Label, Tumor, Mass detection)
Conclusion: mini-MIAS is not a good choice for:
MC feature extraction
Beside
Research Findings…
322. 1. Find Attribute/Feature From the enhanced mammogram:
To train the machine:
-ANN (Artificial Neural Network)
-SVM (Support Vector Machine)
- GentleBoost Classifier [30]
2. Based on feature(size/shape), will move on to classification
( benign or malignant)
Microcalcification
Identification
Microcalcification
Classification
Plan of action as follows:
Further Research Scope
There is always more to work on..In Research:
323. Future Plan
1. Segment the image
2. Find out the feature from
the segmented image
3. Train the machine with features:
-ANN (Artificial Neural Network)
-SVM (Support Vector Machine)
- GentleBoost Classifier [30]
4. Identify the MC
5. Classify the MC
Available
options
324. [2]D.Narain Ponraj, M.Evangelin Jenifer, P. Poongodi, J.Samuel Manoharan “A Survey on the
Preprocessing Techniques of Mammogram for the Detection of Breast Cancer”, Journal of
Emerging Trends in Computing and Information Sciences, Volume 2, Issue 12, pp. 656-664,
2011
[1]K. Santle Camilus , V. K. Govindan, P.S. Sathidevi,” Pectoral muscle identification in
mammograms”, Journal of Applied Clinical Medical Physics , Vol. 12 , Issue No. 3 , 2011
[3]Arnau Oliver, Albert Torrent, Meritxell Tortajada, Xavier Llad´o,Marta Peracaula,
Lidia Tortajada, Melcior Sent´ıs, and Jordi Freixenet,” Automatic microcalcification and cluster
detection for digital and digitised mammograms”, Springer-Verlag Berlin Heidelberg, 36,
pp. 251–258, 2010
Reference
[4]Arnau Olivera, Albert Torrenta , Xavier Lladóa , Meritxell Tortajada, Lidia Tortajadab,
Melcior Sentísb, Jordi Freixeneta, Reyer Zwiggelaarc,” Automatic microcalcification and cluster
detection for digital and digitised mammograms”,Elsevier:Knowledge-Based Systems,
Volume 28, pp. 68–75, April 2012.
325. [5]A.Papadopoulos, D.I . Fotiadis, L.Costrrido,” Improvement of microcalcification cluster
detection in mammogaphy utilizing image enhancement techniques”.Comput.Bio.Med.10,
Vol 38,Issue 38,pp.1045-1055,2008
[6]N.R.Pal,B.Bhowmik, S.K.Patel, S.Pal, J.Das,”A multi-stage nural network aided system for
detection of microcalcification in digitized mammogeams”,Neurocomputing, Vol 11,
pp.2625-2634,2008
[7]M.Rizzi, M.D’Aloia, B.Castagnolo,” Computer aided detection of microcalcification in digital
Mammograms adopting a wavelet decomposition ”,Integr.Comput.-Aided Eng.,Vol 16,Issue 2,pp.
91-103,2009
Reference
[8]S.N.Yu, Y.K. Huang,” Detection of microcalcifications on digital mammograms using combined
Model-based and statistical textural features”, Expert Syst.Appl. , Vol 37,Issue 7,pp.5461-5469,
2010
326. [9]Wang T. C and Karayiannis N. B.: Detection of Microcalci¯cations in Digital Mammograms
Using Wavelets, IEEE Transaction on Medical Imaging, vol. 17, no. 4,(1989) pp. 498-509
[10]. Daubechies I.: Ten Lectures on Wavelets, Philadelphia, PA, SIAM, (1992)
[11] Strickland R.N. and Hahn H.: Wavelet transforms for detecting microcalcifications
in mammograms, IEEE Transactions on Medical Imaging, vol. 15, (1996) pp. 218-229
[12]Heinlein P., Drexl J. and Schneider Wilfried: Integrated Wavelets for Enhancement of
Microcalcifications in Digital Mammography, IEEE Transactions on Medical Imaging, Vol.
22, (2003) pp. 402-413
[13]. Zhibo Lu, Tianzi Jiang, Guoen Hu, Xin Wang: Contourlet based mammographic
image enhancement, Proc. of SPIE, vol. 6534, (2007) pp. 65340M-1 - 65340M-8
Reference
[14]Fatemeh Moayedi, Zohreh Azimifar, Reza Boostani, and Serajodin Katebi: Contourlet-
based mammography mass classification, ICIAR 2007, LNCS 4633,(2007) pp. 923-934
327. [15] Balakumaran T., Vennila ILA, Shankar C.G: Detection of Microcalcification in
Mammograms Using Wavelet Transform and Fuzzy Shell Clustering, International Journal
of Computer Science and Information Security, Vol 7,Issue 1,pp.121-125,2010
[16] Zhang X., Homma N., Goto S.,Kawasumi Y., Ihibashi T.,Abe M.,Sugita N.,Yoshizawa M: A
Hybrid Image Filtering Method for Computer-Aided Detection of Microcalcification
Clusters in Mammograms, Journal of Medical Engineering, Vol 3,Issue 1,pp.111-119,2013
[17] Lu J., Ikehara T., Zhang Y,Mihara T., Itoh T.,Maeda R:High quality factor silicon cantilever
driven by piezoelectric thin film actuator for resonant based mass detection, Micro system
Technologies , Vol 15, Issue 8, pp:1163-1169., 2009
[18] Leeuw H.D., Stehouwer BL, Bakker CJ, Klomp DW, Diest PV, Luijten PR, Seevinck PR,
Bosch MA, Viergever MA, Veldhuis WB:Detecting breast microcalcifications with high-field
MRI, NMR in Biomedicine,Vol 27, Issue 5, pages 539–546,2014
Reference
328. [19] Shankla V, David D. P, Susan P. Weinstein; Michael D., Tuite C, Roth R., Emily F:
Automatic insertion of simulated microcalcification clusters in a software breast
phantom, , Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 2014
[20] Otsu, N., "A Threshold Selection Method from Gray-Level Histograms," IEEE
Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.
[21]Laine A.F., Schuler S., Fan J., Huda W.: Mammographic feature enhancement by
multiscale analysis, IEEE Transactions on Medical Imaging, 1994, vol. 13, no. 4,(1994)
pp. 7250-7260
22. Park S.-I., Smith M. J. T., and Mersereau R. M.: A new directional Filter bank for
image analysis and classification, Proceedings of IEEE International Conference on
Acoustics, Speech, and Signal Processing (ICASSP '99), vol. 3, (1999) pp. 1417-1420
23. Sameer S. and Keit B.: An Evaluation on Contrast Enhancement Techniques for
Mammographic Breast Masses, IEEE Transactions on Information Technology in
Biomedicine, vol. 9, (2005) pp. 109-119
Reference
329. 24. Rosten, E., and T. Drummond. "Machine Learning for High-Speed Corner Detection." 9th
European Conference on Computer Vision. Vol. 1, 2006, pp. 430–443.
25. Shi, J., and C. Tomasi. "Good Features to Track." Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition. June 1994, pp. 593–600.
26. Harris, C., and M. J. Stephens. "A Combined Corner and Edge Detector." Proceedings of
the 4th Alvey Vision Conference. August 1988, pp. 147–152.
27 Bay, H., A. Ess, T. Tuytelaars, and L. Van Gool. "SURF: Speeded Up Robust
Features." Computer Vision and Image Understanding (CVIU). Vol. 110, No. 3, 2008, pp.
346–359.
28.Leutenegger, S., M. Chli, and R. Siegwart. "BRISK: Binary Robust Invariant Scalable
29.Matas, J., O. Chum, M. Urba, and T. Pajdla. "Robust wide-baseline stereo from maximally
stable extremal regions."Proceedings of British Machine Vision Conference. 2002, pp. 384–
396.
Reference
330. 30. Oliver A.; Torrent A. , Tortajada M, Liado X, R., Preacaula M , Tortajada L., Srntis M.,
Ferixenet J: A Boosting based approach for automatic Microcalcification Detection,
Springer-Verlag Berlin Heldelberg,Lecture notes on Computer Science (LNCS 6136), (2010)
pp. 251- 258
Reference