This document provides a comprehensive review and analysis of mammogram enhancement and segmentation techniques. It categorizes mammogram enhancement methods into four groups: conventional, region-based, feature-based, and fuzzy enhancement. Region-based and feature-based techniques can be used to enhance masses, while feature-based and fuzzy methods can also enhance micro-calcifications. The document also categorizes mammogram segmentation into breast region segmentation and region of interest segmentation. Region of interest segmentation using a single view is further divided into unsupervised techniques like region-based, contour-based, and clustering segmentation. The document evaluates and compares various enhancement and segmentation algorithms and highlights the best available approaches.
Objective Quality Assessment of Image Enhancement Methods in Digital Mammogra...sipij
Breast cancer is the most common cancer among women worldwide constituting more than 25%
of all cancer incidences occurring in the world [1]. Statistics show that US, India and China
account for more than one third of all breast cancer cases [2]. Also, there has been a steady
increase in the breast cancer incidence among young generation in the world. In India, one out of
two women die after being detected with breast cancer where as in China it is one in four and in
USA it is one in eight [2]. Therefore, the statistics show that cancer mortality is highest in India
among all other nations in the world. In US, though the number of women diagnosed with cancer
is more than that in India, their mortality
A New Approach to the Detection of Mammogram Boundary IJECEIAES
Mammography is a method used for the detection of breast cancer. computer-aided diagnostic (CAD) systems help the radiologist in the detection and interpretation of mass in breast mammography. One of the important information of a mass is its contour and its form because it provides valuable information about the abnormality of a mass. The accuracy in the recognition of the shape of a mass is related to the accuracy of the detected mass contours. In this work we propose a new approach for detecting the boundaries of lesion in mammography images based on region growing algorithm without using the threshold, the proposed method requires an initial rectangle surrounding the lesion selected manually by the radiologist (Region Of Interest), where the region growing algorithm applies on lines segments that attach each pixel of this rectangle with the seed point, such as the ends (seeds) of each line segment grow in a direction towards one another. The proposed approach is evaluated on a set of data with 20 masses of the MIAS base whose contours are annotated manually by expert radiologists. The performance of the method is evaluated in terms of specificity, sensitivity, accuracy and overlap. All the findings and details of approach are presented in detail.
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
Role of Tomosynthesis in Assessing the Size of the Breast LesionApollo Hospitals
To assess the role of 3D tomosynthesis in the evaluation of the size of malignant breast lesions and to compare it with the size in 2D, Ultrasound and final Histopatholgy.
Objective Quality Assessment of Image Enhancement Methods in Digital Mammogra...sipij
Breast cancer is the most common cancer among women worldwide constituting more than 25%
of all cancer incidences occurring in the world [1]. Statistics show that US, India and China
account for more than one third of all breast cancer cases [2]. Also, there has been a steady
increase in the breast cancer incidence among young generation in the world. In India, one out of
two women die after being detected with breast cancer where as in China it is one in four and in
USA it is one in eight [2]. Therefore, the statistics show that cancer mortality is highest in India
among all other nations in the world. In US, though the number of women diagnosed with cancer
is more than that in India, their mortality
A New Approach to the Detection of Mammogram Boundary IJECEIAES
Mammography is a method used for the detection of breast cancer. computer-aided diagnostic (CAD) systems help the radiologist in the detection and interpretation of mass in breast mammography. One of the important information of a mass is its contour and its form because it provides valuable information about the abnormality of a mass. The accuracy in the recognition of the shape of a mass is related to the accuracy of the detected mass contours. In this work we propose a new approach for detecting the boundaries of lesion in mammography images based on region growing algorithm without using the threshold, the proposed method requires an initial rectangle surrounding the lesion selected manually by the radiologist (Region Of Interest), where the region growing algorithm applies on lines segments that attach each pixel of this rectangle with the seed point, such as the ends (seeds) of each line segment grow in a direction towards one another. The proposed approach is evaluated on a set of data with 20 masses of the MIAS base whose contours are annotated manually by expert radiologists. The performance of the method is evaluated in terms of specificity, sensitivity, accuracy and overlap. All the findings and details of approach are presented in detail.
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
Role of Tomosynthesis in Assessing the Size of the Breast LesionApollo Hospitals
To assess the role of 3D tomosynthesis in the evaluation of the size of malignant breast lesions and to compare it with the size in 2D, Ultrasound and final Histopatholgy.
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
Computer-aided diagnosis system for breast cancer based on the Gabor filter ...IJECEIAES
The most prominent reason for the death of women all over the world is breast cancer. Early detection of cancer helps to lower the death rate. Mammography scans determine breast tumors in the first stage. As the mammograms have slight contrast, thus, it is a blur to the radiologist to recognize micro growths. A computer-aided diagnostic system is a powerful tool for understanding mammograms. Also, the specialist helps determine the presence of the breast lesion and distinguish between the normal area and the mass. In this paper, the Gabor filter is presented as a key step in building a diagnostic system. It is considered a sufficient method to extract the features. That helps us to avoid tumor classification difficulties and false-positive reduction. The linear support vector machine technique is used in this system for results classification. To improve the results, adaptive histogram equalization pre-processing procedure is employed. Mini-MIAS database utilized to evaluate this method. The highest accuracy, sensitivity, and specificity achieved are 98.7%, 98%, 99%, respectively, at the region of interest (30×30). The results have demonstrated the efficacy and accuracy of the proposed method of helping the radiologist on diagnosing breast cancer.
A magnetic resonance spectroscopy driven initialization scheme for active sha...TRS Telehealth Services
Segmentation of the prostate boundary on clinical images is useful in a large number of applications including cal
culation of prostate volume pre- and post-treatment, to detect extra-capsular spread, and for creating patient-specific
anatomical models. Manual segmentation of the prostate boundary is, however, time consuming and subject to inter
and intra-reader variability.
A Magnetic Resonance Spectroscopy Driven Initialization Scheme
for Active Shape Model Based Prostate Segmentation.
Robert Toth1, Pallavi Tiwari1, Mark Rosen2, Galen Reed3, John Kurhanewicz3,
Arjun Kalyanpur4, Sona Pungavkar5, and Anant Madabhushi1
1Rutgers, The State University of New Jersey,
Department of Biomedical Engineering, Piscataway, NJ 08854, USA.
2 University of Pennsylvania,
Department of Radiology, Philadelphia, PA 19104, USA.
3 University of California,
San Francisco, CA, USA.
4 Teleradiology Solutions,
Bangalore, 560048, India.
5 Dr. Balabhai Nanavati Hospital,
Mumbai, 400056, India.
PERFORMANCE EVALUATION OF TUMOR DETECTION TECHNIQUES ijcsa
Automatic segmentation of tumor plays a vital role in diagnosis and surgical planning. This paper deals
with techniques which providing solution for detecting hepatic tumor in Computed Tomography (CT)
images. The main aim of this work is to analyze performance of tumor detection techniques like Knowledge
Based Constraints, Graph Cut Method and Gradient Vector Flow active contour. These three techniques
are computed using sensitivity, specificity and accuracy. From the evaluated result, knowledge based
constraints method is better than other graph cut method and gradient vector flow active contour.
Image processing and machine learning techniques used in computer-aided dete...IJECEIAES
This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Qualitative assessment of image enhancement algorithms for mammograms based o...TELKOMNIKA JOURNAL
Breast cancer is one of the leading reason of death among women. Nevertheless, medications for this fatal disease are still away of ambitions. Patients (thought to have breast cancer) should go through several advanced medical diagnostic procedures like mammography, biopsy analysis, ultrasound imaging, etc. Mammography is one of the medical imaging techniques used for detecting breast cancer. However, its resulted images may not be clear enough or helpful for physician to diagnose each case correctly. This fact has pushed researchers towards developing effective ways to enhance images throughout using various enhancement algorithms. In this paper, a comparison amongst these applied algorithms was done to evaluate the optimum enhancement technique. A morphology enhancement, which is resulted from combining top-hat operation and bottom-hat operation, was used as a proposed enhancement algorithm. The proposed enhancement algorithm was compared to three other well-known enhancement algorithms, specifically histogram equalization, logarithmic transform, and gamma correction with different gamma values. Twenty-five mammographic images were taken from the mammography image analysis society (MIAS) database samples. The minimum entropy difference value (EDV) was used as metric to evaluate the best enhancement algorithm. Results has approved that the proposed enhancement algorithm gave the best-enhanced images in comparison to the aforementioned algorithms.
PSO-SVM hybrid system for melanoma detection from histo-pathological imagesIJECEIAES
This paper introduces an automated system for skin cancer (melanoma) detection from Histo-pathological images sampled from microscopic slides of skin biopsy. The proposed system is a hybrid system based on Particle Swarm Optimization and Support Vector Machine (PSO-SVM). The features used are extracted from the grayscale image histogram, the co-occurrence matrix and the energy of the wavelet coefficients resulting from the wavelet packet decomposition. The PSO-SVM system selects the best feature set and the best values for the SVM parameters (C and γ) that optimize the performance of the SVM classifier. The system performance is tested on a real dataset obtained from the Southern Pathology Laboratory in Wollongong NSW, Australia. Evaluation results show a classification accuracy of 87.13%, a sensitivity of 94.1% and a specificity of 80.22%.The sensitivity and specificity results are comparable to those obtained by dermatologists.
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
Computer-aided diagnosis system for breast cancer based on the Gabor filter ...IJECEIAES
The most prominent reason for the death of women all over the world is breast cancer. Early detection of cancer helps to lower the death rate. Mammography scans determine breast tumors in the first stage. As the mammograms have slight contrast, thus, it is a blur to the radiologist to recognize micro growths. A computer-aided diagnostic system is a powerful tool for understanding mammograms. Also, the specialist helps determine the presence of the breast lesion and distinguish between the normal area and the mass. In this paper, the Gabor filter is presented as a key step in building a diagnostic system. It is considered a sufficient method to extract the features. That helps us to avoid tumor classification difficulties and false-positive reduction. The linear support vector machine technique is used in this system for results classification. To improve the results, adaptive histogram equalization pre-processing procedure is employed. Mini-MIAS database utilized to evaluate this method. The highest accuracy, sensitivity, and specificity achieved are 98.7%, 98%, 99%, respectively, at the region of interest (30×30). The results have demonstrated the efficacy and accuracy of the proposed method of helping the radiologist on diagnosing breast cancer.
A magnetic resonance spectroscopy driven initialization scheme for active sha...TRS Telehealth Services
Segmentation of the prostate boundary on clinical images is useful in a large number of applications including cal
culation of prostate volume pre- and post-treatment, to detect extra-capsular spread, and for creating patient-specific
anatomical models. Manual segmentation of the prostate boundary is, however, time consuming and subject to inter
and intra-reader variability.
A Magnetic Resonance Spectroscopy Driven Initialization Scheme
for Active Shape Model Based Prostate Segmentation.
Robert Toth1, Pallavi Tiwari1, Mark Rosen2, Galen Reed3, John Kurhanewicz3,
Arjun Kalyanpur4, Sona Pungavkar5, and Anant Madabhushi1
1Rutgers, The State University of New Jersey,
Department of Biomedical Engineering, Piscataway, NJ 08854, USA.
2 University of Pennsylvania,
Department of Radiology, Philadelphia, PA 19104, USA.
3 University of California,
San Francisco, CA, USA.
4 Teleradiology Solutions,
Bangalore, 560048, India.
5 Dr. Balabhai Nanavati Hospital,
Mumbai, 400056, India.
PERFORMANCE EVALUATION OF TUMOR DETECTION TECHNIQUES ijcsa
Automatic segmentation of tumor plays a vital role in diagnosis and surgical planning. This paper deals
with techniques which providing solution for detecting hepatic tumor in Computed Tomography (CT)
images. The main aim of this work is to analyze performance of tumor detection techniques like Knowledge
Based Constraints, Graph Cut Method and Gradient Vector Flow active contour. These three techniques
are computed using sensitivity, specificity and accuracy. From the evaluated result, knowledge based
constraints method is better than other graph cut method and gradient vector flow active contour.
Image processing and machine learning techniques used in computer-aided dete...IJECEIAES
This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Qualitative assessment of image enhancement algorithms for mammograms based o...TELKOMNIKA JOURNAL
Breast cancer is one of the leading reason of death among women. Nevertheless, medications for this fatal disease are still away of ambitions. Patients (thought to have breast cancer) should go through several advanced medical diagnostic procedures like mammography, biopsy analysis, ultrasound imaging, etc. Mammography is one of the medical imaging techniques used for detecting breast cancer. However, its resulted images may not be clear enough or helpful for physician to diagnose each case correctly. This fact has pushed researchers towards developing effective ways to enhance images throughout using various enhancement algorithms. In this paper, a comparison amongst these applied algorithms was done to evaluate the optimum enhancement technique. A morphology enhancement, which is resulted from combining top-hat operation and bottom-hat operation, was used as a proposed enhancement algorithm. The proposed enhancement algorithm was compared to three other well-known enhancement algorithms, specifically histogram equalization, logarithmic transform, and gamma correction with different gamma values. Twenty-five mammographic images were taken from the mammography image analysis society (MIAS) database samples. The minimum entropy difference value (EDV) was used as metric to evaluate the best enhancement algorithm. Results has approved that the proposed enhancement algorithm gave the best-enhanced images in comparison to the aforementioned algorithms.
PSO-SVM hybrid system for melanoma detection from histo-pathological imagesIJECEIAES
This paper introduces an automated system for skin cancer (melanoma) detection from Histo-pathological images sampled from microscopic slides of skin biopsy. The proposed system is a hybrid system based on Particle Swarm Optimization and Support Vector Machine (PSO-SVM). The features used are extracted from the grayscale image histogram, the co-occurrence matrix and the energy of the wavelet coefficients resulting from the wavelet packet decomposition. The PSO-SVM system selects the best feature set and the best values for the SVM parameters (C and γ) that optimize the performance of the SVM classifier. The system performance is tested on a real dataset obtained from the Southern Pathology Laboratory in Wollongong NSW, Australia. Evaluation results show a classification accuracy of 87.13%, a sensitivity of 94.1% and a specificity of 80.22%.The sensitivity and specificity results are comparable to those obtained by dermatologists.
https://jst.org.in/index.html
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Classification AlgorithmBased Analysis of Breast Cancer DataIIRindia
The classification algorithms are very frequently used algorithms for analyzing various kinds of data available in different repositories which have real world applications. The main objective of this research work is to find the performance of classification algorithms in analyzing Breast Cancer data via analyzing the mammogram images based its characteristics.Different attribute values of cancer affected mammogram images are considered for analysis in this work. The Patients food habits, age of the patients, their life styles, occupation, their problem about the diseases and other information are taken into account for classification. Finally, performance of classification algorithms J48, CART and ADTree are given with its accuracy. The accuracy of taken algorithms is measured by various measures like specificity, sensitivity and kappa statistics (Errors).
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Comparative Study on the Methods Used for the Detection of Breast Cancerrahulmonikasharma
Among women in the world, the death caused by the Breast cancer has become the leading role. At an initial stage, the tumor in the breast is hard to detect. Manual attempt have proven to be time consuming and inefficient in many cases. Hence there is a need for efficient methods that diagnoses the cancerous cell without human involvement with high accuracy. Mammography is a special case of CT scan which adopts X-ray method with high resolution film. so that it can detect well the tumors in the breast. This paper describes the comparative study of the various data mining methods on the detection of the breast cancer by using image processing techniques.
Possibilistic Fuzzy C Means Algorithm For Mass classificaion In Digital Mammo...IJERA Editor
Mammography is an effective imaging modality of breast cancer abnormalities detection. Survival rate of breast cancer treatment can be increased via early detection of mammography. However detecting the mass in the early stage is a tough task for radiologist. Detection of suspicious abnormalities is a continual task. Out of thousand cases only 3 to 4 are analyzed as cancerous by a radiologist and thus abnormality may be left out. 10-30% of cancers are failed to detect by radiologist. Computer Aided Diagnosis helps the radiologists to detect abnormalities earlier than traditional procedures. Because of some negligence in capturing device, the image may be affected by noise this leads to fault diagnosis. Preprocessing can remove this unwanted noise. In this paper features such as entropy, circularity, edge detection, and correlation are extracted from the image to distinguish normal and abnormal regions of a mammogram. Classification and detection of mammogram can be done by Possibilistic Fuzzy C Means algorithm and Support Vector Machine using extracted features.
Modified fuzzy rough set technique with stacked autoencoder model for magneti...IJECEIAES
Breast cancer is the common cancer in women, where early detection reduces the mortality rate. The magnetic resonance imaging (MRI) images are efficient in analyzing breast cancer, but it is hard to identify the abnormalities. The manual breast cancer detection in MRI images is inefficient; therefore, a deep learning-based system is implemented in this manuscript. Initially, the visual quality improvement is done using region growing and adaptive histogram equalization (AHE), and then, the breast lesion is segmented by Otsu thresholding with morphological transform. Next, the features are extracted from the segmented lesion, and a modified fuzzy rough set technique is proposed to reduce the dimensions of the extracted features that decreases the system complexity and computational time. The active features are fed to the stacked autoencoder for classifying the benign and malignant classes. The results demonstrated that the proposed model attained 99% and 99.22% of classification accuracy on the benchmark datasets, which are higher related to the comparative classifiers: decision tree, naïve Bayes, random forest and k-nearest neighbor (KNN). The obtained results state that the proposed model superiorly screens and detects the breast lesions that assists clinicians in effective therapeutic intervention and timely treatment.
On Predicting and Analyzing Breast Cancer using Data Mining ApproachMasud Rana Basunia
Breast Cancer is one of the crucial and burning diseases that has invaded women. Predicting breast cancer manually takes a lot of time and it is difficult for the physician to classification. So, detecting cancer through various automatic diagnostic techniques is very necessary. Data mining is the process of running powerful classification techniques that extract useful information from data. The uses and potentials of these techniques have found its scope in medical data. Classification techniques tend to simplify the prediction segment.
A Novel Approach for Cancer Detection in MRI Mammogram Using Decision Tree In...CSCJournals
An intelligent computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing microcalcifications’ patterns earlier and faster than typical screening programs. In this paper, we present a system based on fuzzy-C Means clustering and feature extraction techniques using texture based segmentation and genetic algorithm for detecting and diagnosing micro calcifications’ patterns in digital mammograms.We have investigated and analyzed a number of feature extraction techniques and found that a combination of three features, such as entropy, standard deviation, and number of pixels, is the best combination to distinguish a benign micro calcification pattern from one that is malignant. A fuzzy C Means technique in conjunction with three features was used to detect a micro calcification pattern and a neural network to classify it into benign/malignant. The system was developed on a Windows platform. It is an easy to use intelligent system that gives the user options to diagnose, detect, enlarge, zoom, and measure distances of areas in digital mammograms. The present study focused on the investigation of the application of artificial intelligence and data mining techniques to the prediction models of breast cancer. The artificial neural network, decision tree,Fuzzy C Means, and genetic algorithm were used for the comparative studies and the accuracy and positive predictive value of each algorithm were used as the evaluation indicators. 699 records acquired from the breast cancer patients at the MIAS database, 9 predictor variables, and 1 outcome variable were incorporated for the data analysis followed by the 10-fold cross-validation. The results revealed that the accuracies of Fuzzy C Means were 0.9534 (sensitivity 0.98716 and specificity 0.9582), the decision tree model 0.9634 (sensitivity 0.98615, specificity 0.9305), the neural network model 0.96502 (sensitivity 0.98628, specificity 0.9473), the genetic algorithm model 0.9878 (sensitivity 1, specificity 0.9802). The accuracy of the genetic algorithm was significantly higher than the average predicted accuracy of 0.9612. The predicted outcome of the Fuzzy C Means model was higher than that of the neural network model but no significant difference was observed. The average predicted accuracy of the decision tree model was 0.9635 which was the lowest of all 4 predictive models. The standard deviation of the 10-fold cross-validation was rather unreliable. The results showed that the genetic algorithm described in the present study was able to produce accurate results in the classification of breast cancer data and the classification rule identified was more acceptable and comprehensible. Keywords: Fuzzy C Means, Decision Tree Induction, Genetic algorithm, data mining, breast cancer, rule discovery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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JMeter webinar - integration with InfluxDB and GrafanaRTTS
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JMeter webinar - integration with InfluxDB and Grafana
7103
1. 1
MAMMOGRAM ENHANCEMENT AND SEGMENTATION METHODS:
CLASSIFICATION, ANALYSIS, AND EVALUATION
MARIAM BILTAWI1
, NIJAD AL-NAJDAWI2
, SARA TEDMORI3
,
1
Department of Computer Science, The King Hussein School for Information Technology, Princess Sumaya University for
Technology, Al-Jubaiha 11941, Jordan, 00962(6)5359949 Ext 276, maryam@psut.edu.jo
2
Department of Computer Science, Prince Abdullah Ben Ghazi Faculty of Information Technology, Al-Balqa Applied
University, Al-Salt 19117, Jordan, 00962(5)3552519 Ext 3016, n.al-najdawi@bau.edu.jo
3
Department of Computer Science, The King Hussein School for Information Technology, Princess Sumaya University for
Technology, Al-Jubaiha 11941, Jordan, 00962(6)5359949 Ext 238, s.tedmori@psut.edu.jo
Abstract
Breast cancer is the leading cause of deaths among female cancer patients. Mammography is the most effective technique for
breast cancer screening and detection of abnormalities. However, early detection of breast cancer is dependent on both the
radiologist’s ability to read mammograms and the quality of mammogram images. The aim of this paper is to conduct a
comprehensive survey of existing mammogram enhancement and segmentation techniques. Each method is classified, analyzed,
and compared against other approaches. To examine the accuracy of the mammogram enhancement and segmentation
techniques, the sensitivity and specificity of the approaches is presented and compared where applicable. Finally, this research
provides taxonomy for the available approaches and highlights the best available enhancement and segmentation methods.
Keywords: Mammogram enhancement, Mammogram segmentation, Breast mass detection, Image Calcification, detection
1. INTRODUCTION
Globally, breast cancer is ranked first among the
leading causes of cancer affecting females. Statistics have
shown that 1 out of 10 women are affected by breast cancer
in their lifetime. There are several ways in which breast
cancer can be diagnosed, including breast self examination
(BSE), clinical breast exam (CBE), imaging or
mammography, and surgery. A mammogram is the most
effective technique for breast cancer screening and early
detection of masses or abnormalities; it can detect 85 to 90
percent of all breast cancers. The most common said
abnormalities that indicate breast cancer are masses and
calcifications.
Depending on its shape, a mass screened on a
mammogram can be either benign or malignant. Usually
benign tumors have round or oval shapes, while malignant
tumors have a partially rounded shape with a spiked or
irregular outline. Noncancerous or benign tumors include
cysts, fibro adenomas, and breast hematomas. A cancerous
or malignant tumor in the breast is a mass of breast tissue
that grows in an abnormal and uncontrolled way [8]. The
malignant mass will appear whiter than any tissue
surrounding it. Calcifications (both macro-calcification and
micro-calcification), the second abnormality that can be
seen on mammogram images, are most of the time not
malignant and not a sign of cancer. Successful diagnosis in
mammography is dependent on detecting cancer in its
earliest and most treatable stage. The challenge is to employ
computer aided detection (CAD) techniques for the purpose
of assisting radiologists in the early detection of cancer, by
processing and analyzing mammogram images. The
majority of the proposed cancer detection techniques in
literature share the common steps of image enhancement,
segmentation, quantification, registration, visualization [1],
[44]. These techniques can be differentiated by the varying
algorithms employed at each step. One of the challenges
faced by the current mammogram image detection
techniques lies in the difficulty of analyzing dense tissues.
This difficulty can be attributed to the breast region which
appears white in the mammogram images making masses
and specifically micro-calcification highly invisible
intermixed with the background tissue. Mammogram image
enhancement is the process of manipulating mammogram
images to increase their contrast and decrease the noise
present in order to aid radiologists in the detection of
abnormalities. Mammogram image segmentation is the
process of partitioning mutually homogeneous regions into
meaningful regions of interest.
A few papers have been written surveying the
techniques used in cancer detection of mammogram images.
Cheng et al., [14], summarized the enhancement,
segmentation, and detection techniques of mammogram
images. The authors categorized the micro-calcification
enhancement techniques into three categories: conventional
enhancement techniques, region-based enhancement, and
feature based enhancement. The conventional enhancement
techniques are in turn divided into contrast stretching,
histogram equalization, convolution mask enhancement, and
fixed and adaptive neighboring enhancement. Oliver et al.,
[39] reviewed the existing techniques for cancerous mass
detection and segmentation. In their paper, the authors
categorized mammogram segmentation techniques into mass
detection using a single view and mass detection using
multiple views. The mass detection using single view
segmentation in turn is divided into four categories: (1)
model-based methods, (2) region-based methods, (3)
contour-based methods, and (4) clustering methods. Only
477
The 13th International Arab Conference on Information Technology ACIT'2012 Dec.10-13
ISSN : 1812-0857
2. 2
the model based methods are considered supervised
segmentation methods, while the remaining three methods
are considered unsupervised segmentation methods. The
image segmentation techniques using multiple views are
divided into three categories: left and right mammograms,
two mammographic views (CC and MLO) of the same
breast, and same view mammograms taken at different
times. Bandyopadhyay [6] surveyed some commonly used
mass segmentation methods. Raba et al., [42] reviewed
breast region segmentation methods developed and
proposed in the 80’s, 90’s and 2000. The techniques that
were surveyed included: histogram based techniques,
gradient based techniques, polynomial modeling based
techniques, active contour based techniques, and classifiers
based techniques. In this paper, the authors’ review the
algorithms that have been proposed in the literature to
enhance and segment mammogram images that contain both
masses and micro-calcifications.
The goal of this paper is to provide a
comprehensive review, comparison, and analysis of the
available mammogram enhancement and segmentation
algorithms. The rest of this paper is organized as follows:
section 2 presents details of mammogram image
enhancement techniques, section 3 presents the
mammogram image segmentation techniques, section 4
evaluates the proposed approaches. Finally, section 5
presents the conclusion and the future work.
2. MAMMOGRAM IMAGE
ENHANCEMENT TECHNIQUES
Mammogram image enhancement is the process of
manipulating mammogram images to increase their contrast
and decrease the noise present in order to aid radiologists in
the detection of abnormalities. The methods used to
manipulate mammogram images can be categorized into
four main categories; the conventional enhancement
techniques, the region-based enhancement techniques, the
feature-based enhancement techniques, and the fuzzy
enhancement techniques as shown in Figure 1. Conventional
enhancing techniques are fixed neighborhood techniques
and they are used to modify images based on global
properties. Although region-based methods are used in
segmentation, they are also used for enhancing the contrast
of mammogram features according to the surroundings. On
the other hand, feature based enhancement methods are
those methods that are based on wavelet domain
enhancement. And the fuzzy enhancement techniques are
methods that apply fuzzy operators and properties to
enhance mammogram features. Table 1 shows what each of
the four mammogram image enhancement categories is
primarily used for.
Enhancement
Category
Used for the
enhancement of
masses
Used for the
enhancement of
calcifications
Conventional
Enhancement
√ x
Region-based
Enhancement
√ x
Feature-based
Enhancement
√ √
Fuzzy Enhancement √ √
Table 1: Main usage of mammogram enhancement categories
2.1 CONVENTIONAL ENHANCEMENT
TECHNIQUES
The conventional enhancement techniques are
mostly used to enhance masses in mammogram images; as
an example, Bovis and Singh [9] and Antonie et al., [3] used
histogram equalization to enhance the mammogram images
before segmentation and mass detection. However, Schiabel
et al., [47] used the histogram equalization technique
accompanied with other techniques and as a part of a pre-
processing step for mammogram enhancement. Whereas,
Pisano et al., [40] used the contrast limited adaptive
histogram equalization (CLAHE) in order to determine
whether such a method can improve the detection of
stimulated speculations in dense mammograms.
Hemminger et al., [26] compared between contrast-
limited adaptive histogram equalization (CLAHE) and
histogram-based intensity windowing (HIW) in order to
determine which of them outperforms the other in the
detection of simulated masses in dense mammograms. Yu
and Bajaj [64] described a fast approach based on localized
contrast manipulation to enhance image contrast. This
method is based on fast computation of local minimum,
maximum, and average maps using a propagation scheme.
Kom et al., [29] presented a linear transformation filter for
mammogram enhancement. Their algorithm modifies the
local contrast of each pixel according to two linear functions
and two constant values.
2.2 REGION-BASED ENHANCEMENT
Similar to the conventional enhancement methods,
the region-based enhancement methods are mostly used for
the enhancement of masses. An example of this is the work
conducted by Dominguez and Nandi [23] who presented an
enhancement algorithm as a part of an automatic detection
of mammogram masses method. Sampat and Bovik [45]
propose a filtering algorithm that enhances speculations (i.e.
linear features of masses) in mammograms as a part of a
speculated mass detection technique. of the image is
computed to obtain the enhanced image.
2.3 FEATURE BASED ENHANCEMENT:
Figure 1: Categorizations of mammogram enhancement techniques
478
3. 3
Feature based enhancement methods can be used to
enhance both masses and micro-calcifications. Gagnon et
al., [24] proposed a simple multi-scale sharpening
enhancement algorithm based on the hidden zero-crossing
property of the complex symmetric Daubechies wavelets.
The algorithm was tested using low contrast digitized
mammograms. Chang and Laine [12] presented an
enhancement algorithm based on over-complete multi-scale
wavelet analysis. Dabour [19] introduced an algorithm
based on wavelet analysis and mathematical morphology for
digital mammograms enhancement. The authors tested this
algorithm on several mammograms from the MIAS
database. The algorithm was also compared with various
algorithms and the experimental results and showed a better
contrast improvement index. Rodz et al., [4] used the
wavelet-based sharpening algorithm to enhance the contrast
of mammogram images. Laine et al., [30] introduced a
method for mammographic feature analysis by multi-
resolution representations of the dyadic wavelet transform.
Scharcanski and Jung [46] described an approach for noise
suppression and enhancement of mammogram images and
that can be effective in screening dense regions of the
mammograms. Stefanou et al., [50] compared between the
adaptive enhancement algorithm and the typical method of
enhancement.
2.4 FUZZY ENHANCEMENT METHODS
Fuzzy enhancement methods can be used to
enhance masses and micro-calcifications, and as an example
of enhancing mammograms containing masses, Singh and
Al-Mansoori [49] compared between fuzzy enhancement
techniques and histogram equalization. Mohanalin et al.,
[35] presented fuzzy algorithm based on Normalized Tsallis
entropy to enhance the contrast of micro-classifications in
mammograms. Jiang et al., [28] described a combined
approach of fuzzy logic and structure tensor, to enhance
micro-calcifications in digital mammograms. Cheng and Xu
[16] proposed an adaptive fuzzy logic contrast enhancement
method to enhance mammogram features. Table 2
summarizes the mammogram enhancement methods
previously reviewed, specifying the year of the research,
enhancement category, database used, and the number of
mammograms used for testing.
Author
Enhancement
category
Used
database
used
mammogram
s
Yu and Bajaj [64] Conventional N/A N/A
Kom et al [29] Conventional YGOPH 61
Dominguez and
Nandi [23]
Region-based
Mini-
MIAS
N/A
Sampat and Bovik
[45]
Region-based DDSM N/A
Gagnon et al [24] Feature-based N/A N/A
Dabour [19] Feature-based MIAS N/A
Scharacanski and
Jung [46]
Feature-based MIAS N/A
Mohanalin et al
[35]
Fuzzy
MIAS
UCSF
50
197
Jiang et al [28] Fuzzy DDSM 2000
Cheng and
Huijuan Xu [16]
Fuzzy N/A N/A
Table 2: Results of mammogram enhancement techniques
3. MAMMOGRAM IMAGE
SEGMENTATION TECHNIQUES
Mammogram image segmentation is the process of
partitioning mutually homogeneous regions of a
mammogram image into meaningful regions of interest. The
algorithms used for segmentation can be categorized into
two distinct categories according to the regions to be
segmented; breast region segmentation and region of interest
(ROI) segmentation. Breast region segmentation is the
process of splitting the mammogram image into a breast
region and a background in order to focus and limit the
search for abnormalities on the breast region without the
effect of the background on the results resulting in better
detection. On the other hand, region of interest segmentation
is the process of segmenting the suspicious regions to be
analyzed for abnormalities.
Figure 2: Categorizations of mammogram image segmentation techniques
479
4. 4
While segmentation using multiple views can be
categorized into left and right mammograms, two
mammographic views (cranio-caudal (CC) and medio lateral
oblique (MLO)) of the same breast, and same view
mammograms taken at different times. Unsupervised
segmentation using a single view can in turn be categorized
into six classes, region-based segmentation, contour-based
segmentation, clustering segmentation, pseudo-color
segmentation, graph segmentation, and variant-feature
transformation. Figure2 illustrates this categorization.
3.1 BREAST REGION SEGMENTATION
Breast segmentation techniques set the focus of the
search for abnormalities on the region of the breast
excluding its background. The techniques used for
segmenting are similar to those used in the regions of
interest segmentation and can also be categorized with the
same perspectives though it’s not the interest of this paper.
As an example of approaches that can be listed under the
clustering segmentation method is the novel approach
proposed by Shahedi et al., [48] for breast region
segmentation based on local threshold. The Ojala et al., [38]
which is based on histogram thresholding, morphological
filtering, and contour modeling. This approach is applied by
Wang et al., [59] as the first step in their automatic
framework to identify differences between corresponding
mammographic images. Raba et al., [42] proposed an
automatic technique for segmenting a digital mammogram
into a breast region and a background based on a "two-
phase" approach. Bovis and Singh [9]applied the technique
proposed by Chandrasekhar and Yttikiouzel [12] adding to it
an additional step, in order to segment the breast region
from its background. Wirth and Stapinski [61] described an
approach that automatically segments the breast region and
extracts the breast contour in mammogram images using
snakes or active contours. Wei et al., [60] presented a novel
approach that extracts the contour of a region of interest in
mammogram images. Chen and Zwiggelaar [13], which is a
histogram thresholding, edge detection in scale space,
contour growing and polynomial fitting base technique for
segmenting the breast region. Yapa and Harada [63]
presented a breast skin-line estimation and breast
segmentation algorithm using fast marching approach. Table
3 summarizes the breast region segmentation methods
described above, providing the year of the research, the
database used, the number of mammograms used, the
accuracy of segmenting the breast boundary, the pectoral
muscle extraction accuracy, and the ability of the method to
extract the nipple.
3.2 REGIONS OF INTEREST
SEGMENTATION
Regions of interest segmentation is divided into
two main categories, as mentioned previously in this paper,
segmentation using a single view and segmentation using
multiple views. This section lists some algorithms under
each category.
3.2.1 Segmentation using single view
Regions of interest segmentation using a single
view are divided into supervised and unsupervised
segmentation. This section will mainly list the unsupervised
segmentation algorithms under the six categories described
previously. Each category of unsupervised segmentation is
Author
Segmentation
category
Used database
used
mammogr
am
Masses Calcifications Sensitivity Specificity
Schiable et al [47] Region-based UNESP 130 √ X 90% 92%
Cascio et al [11] Contour-based MAGIC-5 N/A √ X 82% N/A
Kom et al., [29] Clustering YGOPH 61 √ X 95.91% N/A
Zheng et al [66] Variant feature N/A 510 √ √ N/A N/A
Mencattini et al [34] Region-based DDSM 200 √ X N/A N/A
Zhang et al [65] Contour-based DDSM N/A √ X N/A N/A
Muralidhar et al [37] Contour-based DDSM N/A √ X N/A N/A
Cao et al [10] Clustering MIAS N/A √ X N/A N/A
Comer et al [18] Clustering U.South Florida N/A √ √ N/A N/A
Anguh and Silva [2] Pseudo-color U. of South Florida 50 √ √ N/A N/A
Bajger et al [5] Graph Mini-MIAS 55 √ X N/A N/A
Guan et al [25] Variant feature MIAS N/A X √ N/A N/A
Stojic et al [51] Variant feature Mini-MIAS N/A X √ N/A N/A
Bhattacharya and Das [7] Variant feature MIAS 65 X √ N/A N/A
Table-4: comprehensive summary of the previously mentioned unsupervised ROI segmentation methods using a single view
Author Year Used database
Number of used
mammograms
Breast
boundary
accuracy
Pectoral muscle
extraction accuracy
Ability of
nipple
extraction
Shahedi et al [48] 2007 Mini-MIAS 66 86% 94% √
Raba et al [42] 2005 Mini-MIAS 320 98% 86% N/A
Wirth and Stapinski [61] 2004 MIAS 32 N/A N/A N/A
Wei et al., [60] 2008 MIAS 322 N/A N/A √
Chen and Zwiggelaar [13] 2010 EPIC 240 98.4% 93.5% N/A
Yapa and Harada [63] 2008 Mini-MIAS 100 99.1% N/A √
Table 3: Results of breast region segmentation techniques
480
5. 5
specialized in segmenting abnormalities such as masses and
calcification.
3.2.1.1 Region-based methods:
Region growing segmentation techniques are used
to segment both masses and calcifications. As an example of
mass segmentation methods, Mencattini et al., [34] proposed
a mass segmentation module for a CAD system
implemented using a new region growing algorithm.
Schiabel et al., [47] described a methodology for
segmenting suspicious masses in dense mammograms. This
methodology is based on the Watershed transformation.
Wang and Karayiannis [58] applied the watershed algorithm
to segment micro-calcification in the segmentation phase of
the approach proposed by the authors to detect micro-
calcifications employing wavelet-based sub-band image
decomposition.
3.2.1.2 Contour-based methods:
Most of the research conducted using the contour-
based methods segmented masses rather than segmenting
calcification. An example of such a method, is the work
conducted by, Zhang et al., [65] proposed a contour-based
segmentation method for mass segmentation. The authors
tested their approach on ROI marked mammograms from
the digital database for screening mammography (DDSM).
Cascio et al., [11] proposed a contour based mass
segmentation algorithm, that was tested on the
mammograms from the Medical Applications on a Grid
Infrastructure Connection (MAGIC-5 collaboration) which
consists of 3762 mammograms. Singh and Al-Mansoori [49]
compared between region growing and gradient-based
segmentation techniques. Muralidhar et al., [37] describe a
mass classification method, based on the snakules
segmentation method which is an evidence active contour
algorithm developed by the authors. The authors tested this
method using mammograms from DDSM database and the
results of using snakules are promising.
3.2.1.3 Clustering methods:
Clustering segmentation methods can segment both
masses and calcifications. And as an example of segmenting
masses using clustering, Kom et al., [29] developed a local
adaptive thresholding technique for mass segmentation.
Velthuizen [56] developed a segmentation method based on
an initial unsupervised clustering. Dominguez and Nandi
[23] applied density slicing technique proposed by
Mudigonda et al., [36] to segment masses. This technique
was used as a part of a method the authors presented for
mass detection in mammograms. Cao et al., [10] proposed
an information based algorithm for mass segmentation on
digital mammograms. This algorithm is called cshells based
on deterministic annealing (CSDA).
Cheng et al., [15] who used the iterative threshold
selection method [43] to implement the segmentation
process as a part of a novel approach based on fuzzy logic
for micro-calcification detection. Moreover, clustering can
be used for the detection of masses and micro-calcifications
together, as shown by the statistical algorithm for
mammogram segmentation presented by Comer et al., [18].
3.2.1.4 Pseudo-color segmentation
Pseudo-color segmentation methods can be used to
detect masses and micro-calcifications together. Such an
algorithm is used by the approach presented by Anguh and
Silva [2].
3.2.1.5 Graph segmentation:
Graph segmentation methods can be used to
segment masses. Bajger et al., [5] employed a graph
segmentation method in his approach to automatically
segment mammogram masses using minimum spanning
trees (MST). The authors tested their approach using two
sets of mammograms. The first set consists of 55
mammograms from the Mini-MIAS database, and the
second set consists of 37 mammograms from a local
database. Ma et al., [31] presented a method based on the
adaptive pyramid (AP) segmentation and sublevel set
analysis of mammograms. In another work, Ma et al., [32]
compared between the performance of the minimum
spanning trees (MST) based segmentation method and that
of the adaptive pyramid (AP) based segmentation method in
terms of robustness. Graph segmentation methods can also
be used to segment micro-calcifications. D’Elia et al., [20]
and D’Elia et al., [21] used the tree-structured markov
random field (TS-MRF) based segmentation method (D’Elia
el al., [22]) to segment mammograms in order to detect
abnormalities.
Author Year Segmentation category Used database
Number of used
mammograms
Bovis and Singh [9] 2000 Left and right mammograms MIAS 144
Xu et al [62] 2010 Left and right mammograms DDSM 60
Wang et al [59] 2006 Left and right mammograms MIAS N/A
Marti et al [33] 2006 Left and right mammograms N/A 64
Velikova et al [55] 2009 Two mammographic views (CC and MLO) of the same breast N/A 1063
Pu et al [41] 2008 Two mammographic views (CC and MLO) of the same breast N/A 200
Sun et al [52] 2004 Two mammographic views (CC and MLO) of the same breast N/A 100
Altrichter et al [1] 2005 Two mammographic views (CC and MLO) of the same breast DDSM N/A
Timp et al [54] 2005 Same view of mammograms taken at different times N/A 389
Timp and Karssemeijer [53] 2006 Same view of mammograms taken at different times DBSP 2873
Wirth et al [67] 2002 Same view of mammograms taken at different times MIAS N/A
Mudigonda et al [36] 2001 Same view of mammograms taken at different times N/A N/A
Table-5: ROI segmentation techniques using multiple views
481
6. 6
3.2.1.6 Variant feature transformation
Variant feature transformation can be used in
mammogram mass segmentation. Zheng et al., [66] used a
technique of single-image segmentation with Gaussian
band-pass filtering. This technique is used as a segmentation
part of a CAD system for mammogram mass detection. The
authors tested their CAD system on 510 mammograms and
the results showed that single-image segmentation method
have a high sensitivity. However, variant feature
transformation methods are mostly effective in segmenting
micro-calcification along with mammogram masses. And as
an example of segmenting micro-calcification, Guan et al.,
[25] proposed an approach based on scale invariant feature
transform (SIFT) to segment micro-calcifications
automatically in mammograms. Stojic et al., [51] modified
the multi-fractal (MF) segmentation method to improve the
segmentation of micro-calcification. Bhattacharya and Das
[7] presented a novel approach for segmenting
mammograms in order to accurately detect micro-
calcification clusters. Table-4 provides a comprehensive
summary of the previously mentioned unsupervised ROI
segmentation methods using a single view, specifying the
year of the research, the segmentation category, the database
used, the number of mammograms used, the ability of the
method to segment masses, and the ability of the method to
segment micro-calcifications, sensitivity, and specificity.
3.2.2 Segmentation using multiple views
Breast images can be taken from different angles,
the most common views are; mediolateral oblique (MLO)
view which is the most important and the cranio-caudal
view (CC). According to the previous image views, image
segmentation techniques using multiple views can be
divided into three categories: left and right mammograms,
two mammographic views (CC and MLO) of the same
breast, and same view mammograms taken at different
times. In the left and right mammograms, the evaluation is
done by checking the symmetry of the fibroglandular tissue
in the two breasts. In the two mammographic views (CC and
MLO) of the same breast, the evaluation is done by
checking the fibroglandular tissue in CC and MLO images
of the same breast. However, in the same view
mammograms taken at different times, the evaluation is
done by checking the changes of the fibroglandular tissue of
the breast at different times.
3.2.2.1 Left and right mammograms:
Bovis and Singh [9] applied the region splitting
technique on the two images obtained from a bilateral
subtraction technique. While, Xu et al., [62] presented a
CAD algorithm for mass detection using bilateral
asymmetry. In this algorithm, the left and right
mammograms are aligned, then a bilateral subtraction
technique is applied. Wang et al., [59] proposed an
automatic registration framework, in order to identify the
differences between corresponding mammograms. The
authors used mammograms from MIAS database to test
their framework. Marti et al., [33] presented a novel method
for mammogram image registration. The authors tested this
method using 64 mammograms containing malignant
masses.
3.2.2.2 Two mammographic views (CC and MLO) of
the same breast:
Velikova et al., [55] proposed and applied the
Bayesian network multi-view system for the detection of
abnormalities. Pu et al [41] developed a computer scheme
based on an ellipse-fitting algorithm. This scheme is used to
build a Cartesian coordinate systemin order to match the
breast masses depicted in the two mammograms. Sun et al
[52] presented ipsilateral multi-view CAD scheme to detect
masses in digital mammograms. Altrichter et al., [1]
proposed a procedure for joint analysis of breast’s two
views, by combining the results of algorithms detecting
mass and micro-calcifications detection. The authors tested
their algorithm using mammograms from the DDSM
database. Results showed that this technique can reduce the
number of false negatives significantly.
3.2.2.3 Same view of mammograms taken at different
times:
Timp et al [54] developed an automatic regional
registration method for mass detection in mammograms; the
authors tested this method using 389 mammograms. Wai
and Brady [57] presented a landmark-based registration
framework of mammograms. However, Timp and
Karssemeijer [53] developed a regional registration
technique to link a suspicious location on prior and current
mammograms. Wirth et al., [67] proposed a non-rigid
mammogram registration approach to match mammograms.
The authors tested the proposed approach using
mammograms from the MIAS database. Table-5
summarizes the ROI segmentation techniques using multiple
views discussed above, specifying the year of the research,
the segmentation category, the database used, and the
number of mammograms used.
4. EVALUATION
In this research, the performance of the
Mammogram enhancement and segmentation methods is
evaluated using different databases. However, most of the
methods were evaluated using two main databases, the
MIAS and the DDSM. The MIAS database is a digital
mammography database produced by the UK research
group. The MIAS database contains left and right digital
mammograms for 161 patients with a total of 322 images of
50X50 micron resolution, and with 8-bit pixel depth. The
images included all types of breast tissues; normal, benign
and malignant. The DDSM database contains 2500 studies;
each study includes two images of each breast. An
abnormality in a mammogram is diagnosed either positive,
i.e. predict that the person has cancer, or negative, i.e.
predict that a person does not have cancer. To aid this
diagnosis in CAD systems, mammogram enhancement and
segmentation techniques should be applied. These
enhancement and segmentation algorithms can be evaluated
482
7. 7
using different techniques. The most popular technique is
dependent on the naked eye and is also known as the visual
perception and still remains a challenging task. In order to
set a standardized benchmark for subjective visual quality
assessment, the International Telecommunications Union
has proposed a set of test procedures defined in the
International Telecommunications Union Recommendation
(ITU-R [27]).This recommendation sets the guidelines for
the subjective assessment test conditions such as the
viewing distance, the test duration, and the observers’
recruitment.
Other techniques can be employed to evaluate the
enhancement and segmentation algorithms such as,
sensitivity and specificity, receiver operating characteristic
(ROC) and free-ROC (FROC). The results of diagnosing a
mammogram can be classified as follows:
True positive (TP): sick people correctly diagnosed sick.
False positive (FP): healthy people incorrectly diagnosed sick.
True negative (TN):healthy people correctly diagnosed
healthy.
False negative (FN): sick people incorrectly diagnosed
healthy.
Sensitivity and specificity are performance evaluation
statistical measures [17], where sensitivity (true positive
fraction (TPF)) is the fraction of sick people who are
correctly diagnosed as positive, and specificity (true
negative fraction (TNF)) is the fraction of healthy people
who are correctly diagnosed as negative. These measures
specify the accuracy of the system for identifying the actual
positive and actual negative patients respectively. However,
the false-positive-fraction (FPF) and the false-negative-
fraction represent the frequencies of incorrect diagnoses,
therefore: False-negative-fraction = 1 - TPF, and False-
positive-fraction = 1 - TNF. There is an interrelationship
between these measures which makes it important and
meaningful to specify a single pair, either sensitivity and
specificity, or TPF and FPF, but it is inadequate to use only
one measure alone. Equation 1 shows sensitivity measure,
while equation 2 shows specificity measure.
Sensitivity = number of TP / (number of TP + number of
FN)
Specificity = number of TN / (number of TN + number of
FP)
The disadvantage of using a pair of either
sensitivity and specificity, or TPF and FPF for a test is to
have higher sensitivity (higher TPF) but lower specificity
(higher FPF), i.e. one test is more accurate for actually
positive patients and other is more accurate for actually
negative patients, which makes it difficult to indicate which
test is better. This limitation can be resolved using ROC
curves. ROC analysis is used to provide a comprehensive
description of diagnostic accuracy by estimating and
reporting all combinations of sensitivity and specificity.
ROC curves are presented by plotting sensitivity as a
function of FPF (1-specificity). On the other hand, ROC
curves depend on the skill of the radiologist, and, the
imaging procedure’s technical aspects such as, spatial
resolution, noise and contrast (Swets, 1979). However,
FROC analysis accommodates multiple lesions on each
image by allowing multiple reports, and is represented by
plotting the fraction of lesions detected as the vertical axis
and the average number of false-positive detections per
image as the horizontal axis which is not normalized,
instead, it is extended to an arbitrary large number of FP
reports per image. FROC analysis provides greater statistical
power than conventional ROC analysis, and the results of its
analysis depend on the number of locations allowed by the
data analyst. Table-3 through Table-5 summarizes the
results.
5. CONCLUSION
This paper presented a comprehensive
classification and evaluation of mammogram enhancement
and segmentation algorithms. The enhancement techniques
were categorized into four distinct techniques. From the
review, it is obvious that the results produced from the fuzzy
enhancement techniques are best suited for enhancing both
masses and micro-calcifications. On the other hand, the
mammogram segmentation techniques were categorized into
two distinct categories, and the regions of interest
segmentation techniques were further categorized into sub-
categories. From the review of the literature, it can be
inferred that the variant feature transformation category
listed under the unsupervised single view segmentation
techniques are better suited for the segmentation of masses
and micro-calcifications. However, segmentation using
multiple views can also give good results.
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