Diagnosis the computed tomography images (CT-images) is one of the images that may take a lot of time in diagnosis by the radiologist and may miss some of cancerous nodules in these images. Therefore, in this paper a new novel enhancement and detection cancerous nodule algorithm is proposed to diagnose a CT-images. The novel algorithm is divided into three main stages. In first stage, suspicious regions are enhanced using modified LoG algorithm. Then in stage two, a potential cancerous nodule was detected based on visual appearance in lung. Finally, five texture features analysis algorithm is implemented to reduce number of detected FP regions. This algorithm is evaluated using 60 cases (normal and cancerous cases), and it shows a high sensitivity in detecting the cancerous lung nodules with TP ration 97% and with FP ratio 25 cluster/image.
A novel CAD system to automatically detect cancerous lung nodules using wav...IJECEIAES
A novel cancerous nodules detection algorithm for computed tomography images (CT-images) is presented in this paper. CT-images are large size images with high resolution. In some cases, number of cancerous lung nodule lesions may missed by the radiologist due to fatigue. A CAD system that is proposed in this paper can help the radiologist in detecting cancerous nodules in CT- images. The proposed algorithm is divided to four stages. In the first stage, an enhancement algorithm is implement to highlight the suspicious regions. Then in the second stage, the region of interest will be detected. The adaptive SVM and wavelet transform techniques are used to reduce the detected false positive regions. This algorithm is evaluated using 60 cases (normal and cancerous cases), and it shows a high sensitivity in detecting the cancerous lung nodules with TP ration 94.5% and with FP ratio 7 cluster/image.
Image segmentation is still an active reason of research, a relevant research area
in computer vision and hundreds of image segmentation techniques have been proposed by
the researchers. All proposed techniques have their own usability and accuracy. In this paper
we are going present a review of some best lung nodule existing detection and segmentation
techniques. Finally, we conclude by focusing one of the best methods that may have high
level accuracy and can be used in detection of lung very small nodules accurately.
Lung Cancer Detection on CT Images by using Image Processingijtsrd
This project is mainly based on image processing technique. In this work MATLAB have been used through every procedure made. Image processing techniques are widely use in bio-medical sector. The objective of our work is noise removal operation, thresholding, gray scale imaging, histogram equalization, texture segmentation, and morphological operation. Detection of lung cancer from computed tomography (CT) images is done by using MATLAB software. By using these methods the work has been done on CT images and the final tumor area has been shown with pixel values. Bindiya Patel | Dr. Pankaj Kumar Mishra | Prof. Amit Kolhe"Lung Cancer Detection on CT Images by using Image Processing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11674.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/11674/lung-cancer-detection-on-ct-images-by-using-image-processing/bindiya-patel
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
Mass Segmentation Techniques For Lung Cancer CT Imagesrahulmonikasharma
Mass segmentation methods are commonly used nowadays in modern diagnostic centers and research centers working in the field of lung cancer detection and diagnosis. We have implemented k-means and fuzzy cluster means (FCM) techniques for mass segmentation of lung CT images. The methods were compared in terms of area, perimeter and diameter. FCM outperforms K-means in terms of better detection of lung cancer area and effective values of dimensional features of lung cancer as compared to K-means method.
IRJET- Computer Aided Detection Scheme to Improve the Prognosis Assessment of...IRJET Journal
This document describes a computer-aided detection scheme to predict the risk of cancer recurrence in early-stage lung cancer patients after surgery. The scheme uses chest CT images taken before surgery to automatically segment lung tumors and extract morphological and texture-based image features. A naive Bayesian classifier is trained on six image features to predict recurrence risk. A separate artificial neural network classifier is trained on two genomic biomarkers to predict risk. The results from the two classifiers are then combined using a fusion method to produce the overall risk prediction. The goal is to more accurately assess prognosis and help doctors better manage early-stage non-small cell lung cancer patients after surgery.
A novel CAD system to automatically detect cancerous lung nodules using wav...IJECEIAES
A novel cancerous nodules detection algorithm for computed tomography images (CT-images) is presented in this paper. CT-images are large size images with high resolution. In some cases, number of cancerous lung nodule lesions may missed by the radiologist due to fatigue. A CAD system that is proposed in this paper can help the radiologist in detecting cancerous nodules in CT- images. The proposed algorithm is divided to four stages. In the first stage, an enhancement algorithm is implement to highlight the suspicious regions. Then in the second stage, the region of interest will be detected. The adaptive SVM and wavelet transform techniques are used to reduce the detected false positive regions. This algorithm is evaluated using 60 cases (normal and cancerous cases), and it shows a high sensitivity in detecting the cancerous lung nodules with TP ration 94.5% and with FP ratio 7 cluster/image.
Image segmentation is still an active reason of research, a relevant research area
in computer vision and hundreds of image segmentation techniques have been proposed by
the researchers. All proposed techniques have their own usability and accuracy. In this paper
we are going present a review of some best lung nodule existing detection and segmentation
techniques. Finally, we conclude by focusing one of the best methods that may have high
level accuracy and can be used in detection of lung very small nodules accurately.
Lung Cancer Detection on CT Images by using Image Processingijtsrd
This project is mainly based on image processing technique. In this work MATLAB have been used through every procedure made. Image processing techniques are widely use in bio-medical sector. The objective of our work is noise removal operation, thresholding, gray scale imaging, histogram equalization, texture segmentation, and morphological operation. Detection of lung cancer from computed tomography (CT) images is done by using MATLAB software. By using these methods the work has been done on CT images and the final tumor area has been shown with pixel values. Bindiya Patel | Dr. Pankaj Kumar Mishra | Prof. Amit Kolhe"Lung Cancer Detection on CT Images by using Image Processing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11674.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/11674/lung-cancer-detection-on-ct-images-by-using-image-processing/bindiya-patel
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Lung Cancer Detection using Machine Learningijtsrd
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors that’s why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
Mass Segmentation Techniques For Lung Cancer CT Imagesrahulmonikasharma
Mass segmentation methods are commonly used nowadays in modern diagnostic centers and research centers working in the field of lung cancer detection and diagnosis. We have implemented k-means and fuzzy cluster means (FCM) techniques for mass segmentation of lung CT images. The methods were compared in terms of area, perimeter and diameter. FCM outperforms K-means in terms of better detection of lung cancer area and effective values of dimensional features of lung cancer as compared to K-means method.
IRJET- Computer Aided Detection Scheme to Improve the Prognosis Assessment of...IRJET Journal
This document describes a computer-aided detection scheme to predict the risk of cancer recurrence in early-stage lung cancer patients after surgery. The scheme uses chest CT images taken before surgery to automatically segment lung tumors and extract morphological and texture-based image features. A naive Bayesian classifier is trained on six image features to predict recurrence risk. A separate artificial neural network classifier is trained on two genomic biomarkers to predict risk. The results from the two classifiers are then combined using a fusion method to produce the overall risk prediction. The goal is to more accurately assess prognosis and help doctors better manage early-stage non-small cell lung cancer patients after surgery.
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.
Detection of Lung Cancer using SVM ClassificationIRJET Journal
This document presents a method for detecting lung cancer using support vector machine (SVM) classification of sputum cell images. The authors first extract features from sputum cell images such as nucleus-cytoplasm ratio, perimeter, density, curvature, and circularity. They then use these extracted features to train an SVM classifier to classify sputum cells as cancerous or normal. The authors test their proposed method on 100 sputum cell images and evaluate the technique's performance using metrics like sensitivity, precision, specificity, and accuracy. Their results indicate the SVM classification approach shows potential for early detection of lung cancer from sputum cell analysis.
automatic detection of pulmonary nodules in lung ct imagesWookjin Choi
The document discusses lung cancer detection using CT scans and pulmonary nodule detection systems. It describes how CT scans are used to detect lung nodules early and increase survival rates. It then discusses the challenges of evaluating large CT data sets and the use of pulmonary nodule detection CAD systems to assist radiologists. The document goes on to describe a proposed CAD system that includes lung segmentation, nodule candidate detection using multi-thresholding and feature extraction, and a genetic programming based classifier to analyze features and detect nodules. Experimental results on a publicly available lung image database show the system achieved over 80% accuracy on test data for nodule detection.
Lung Cancer Detection using Image Processing TechniquesIRJET Journal
This document presents a technique for detecting lung cancer in x-ray images using image processing. It involves enhancing images using Gabor filtering, segmenting images using marker-controlled watershed segmentation, and extracting features using binarization and masking. The key steps are collecting lung x-ray images, enhancing quality using Gabor filtering, segmenting regions of interest using watershed segmentation, extracting pixel counts and mask features, and classifying images as normal or abnormal based on these features. The goal is to enable early detection of lung cancer through automated analysis of medical images.
IRJET- Simulation Measurement for Detection of the Breast Tumors by using Ult...IRJET Journal
The document describes a simulation study that used ultra-wideband radar to detect breast tumors. A hemispherical breast phantom with different dielectric properties for healthy and tumor tissue was modeled. A directional antenna was used to illuminate the phantom and measure scattering parameters. Simulation results showed scattering increased when a tumor was present and increased further as the antenna approached the tumor, indicating the method could successfully detect tumors.
IRJET- Image Processing based Lung Tumor Detection System for CT ImagesIRJET Journal
This document presents a method for detecting lung tumors in CT scan images using image processing techniques. The proposed method involves preprocessing images using median filtering for noise removal and contrast adjustment for enhancement. The lungs are then segmented from the images using mathematical morphology. Geometric and textural features are extracted from the segmented region of interest and used as input for an SVM classifier to detect lung cancer. The methodology was tested on a dataset from The Cancer Imaging Archive and was able to successfully detect lung tumors in CT images.
IRJET- Lung Diseases using Deep Learning: A Review PaperIRJET Journal
This document reviews research on using deep learning techniques to detect lung diseases from medical images. It first provides background on lung anatomy and common lung diseases like pneumonia, tuberculosis, and lung cancer. Feature extraction and classification algorithms are important for automated detection. Several studies are summarized that used techniques like convolutional neural networks (CNNs) and support vector machines (SVMs) with shape, texture, and focal features to classify chest x-rays and CT scans as normal or abnormal. Deep learning approaches achieved higher accuracy than traditional "bag of features" methods. Overall, CNNs showed potential for developing high-performance automated lung disease detection systems.
Robust breathing signal extraction from cone beam CT projections based on ada...Wookjin Choi
This document summarizes a research paper that proposes a novel method for extracting breathing signals from cone beam CT projections without using external markers. The method uses an adaptive filtering technique to enhance weak oscillating structures in the Amsterdam Shroud image generated from the projections. A two-step optimization approach is then used to reveal the large-scale regularity of the breathing signals. Evaluation on 5 patient data sets found the new algorithm outperformed existing methods by extracting less noisy signals with errors of only -0.07±1.58 breaths per minute compared to reference signals. While results are promising, the study had a small data set and image quality remains limited.
Lung Nodule Segmentation in CT Images using Rotation Invariant Local Binary P...IDES Editor
As the lung cancer is the leading cause of cancer
death in the medical field, Computed Tomography (CT) scan
of the thorax is widely applied in diagnoses for identifying
the lung cancer. In this paper, a technique of rotation invariant
with Local Binary Pattern (LBP) for segmentation of various
lung nodules from the Lung CT cancer data sets is used. This
is tested on various lung data sets from teaching files of
Casimage database and National Cancer Institute (NCI) of
National Biomedical Imaging Archive (NBIA). The results
show the segmented nodules with clear boundaries, which is
helpful in diagnosis of lung cancer. Further, the results are
compared with the watershed segmentation method, which
shows that LBP based method yields better segmentation
accuracy.
Features of new installed linac Trilogy At Dr Ziauddin Hospital KarachiRahim Gohar
This document provides an overview of the features and capabilities of a new Trilogy linear accelerator installed at Dr. Ziauddin Hospital's radiation oncology department. It describes the linac's engineering, its ability to perform treatments using 3D-CRT, IMRT, VMAT, electrons, and SRS. It also discusses the linac's imaging modalities for treatment planning and verification. Key treatment techniques like IMRT, VMAT, and IGRT are summarized in terms of how they are planned and delivered using the linac and its integrated treatment planning system.
Image processing in lung cancer screening and treatmentWookjin Choi
The document discusses image processing techniques for lung cancer screening and treatment. It covers topics like lung segmentation, nodule detection, computer-aided diagnosis, image-guided radiotherapy, and quantitative assessment of tumor response. Lung segmentation is used to isolate the lungs from other organs in CT images. Nodule detection algorithms then aim to find potential cancerous nodules. Computer-aided diagnosis systems analyze extracted features of nodules to determine if they are malignant or benign. Image-guided radiotherapy utilizes 4D CT and gating to account for tumor motion during treatment. Quantitative metrics like standardized uptake value are used to assess tumor response in PET imaging.
Detection of Prostate Cancer Using Radial/Axial Scanning of 2D Trans-rectal U...journalBEEI
The search for improvement in the result of segmentation of regions of interest in medical images has continued to be a source of challenge to researchers. Several research efforts have gone in to delineate regions of interest in the prostate gland from Trans-rectal ultrasound (TRUS) 2D-images. In this work, we develop a fast algorithm based on radial/axial scanning of the pixels of the prostate gland image with the goal of detecting hyper-echoic pixels that are bound within the boundaries of the gland TRUS 2D-images. The algorithm implements expert knowledge and utilizes the features extracted from the intensity of the TRUS images, primarily the relative intensity and gradient to delineate region of interest. It employs radial/axial scanning of the image from common seed point automatically selected to detect the region of the gland and subsequently hyper-echoic pixels which indicate suspected cancerous tissue cites. Evaluation of the algorithm performance was done by comparing detection result with that of expert radiologists. The detection algorithm gave an average accuracy of 88.55% and sensitivity of 71.65%.
This document discusses preliminary dosimetric analysis of target motion effects in 4D tomotherapy and outlines several challenges and potential solutions:
1) Contouring targets across multiple respiratory phases is time-consuming; research consoles can help by propagating contours and creating average images.
2) Planning and dose computation across phases is complex; multiple plans must be evaluated to assess potential underdosing.
3) Initial QA using dynamic phantoms shows dose shifts near targets, underscoring the need for 4D evaluations and potentially larger margins.
4) Further investigations of 4D imaging, planning, dose computation and adaptive techniques are needed to fully account for respiratory motion effects in tomotherapy.
CANCER CELL DETECTION USING DIGITAL IMAGE PROCESSINGkajikho9
The document presents a lung cancer detection system using digital image processing techniques. It discusses lung anatomy and types of lung cancer. The system involves image capture, pre-processing using enhancement filters like Gabor and FFT, segmentation using thresholding and watershed approaches. Feature extraction is done using binarization and masking to detect cancer presence. The system helps in early detection of lung cancer to reduce mortality.
This document discusses brain tumor segmentation from MRI images using fuzzy c-means clustering. It begins with an introduction to brain tumors and MRI imaging. Next, it reviews existing methods for brain tumor segmentation such as thresholding, region growing, and clustering. It then discusses preprocessing MRI images, including converting images to grayscale and filtering. Finally, it describes fuzzy c-means clustering, which is an unsupervised learning technique used to segment and classify pixels in MRI images to detect tumor regions. The goal is to develop an accurate and automated method for brain tumor segmentation to assist medical experts.
IRJET- Content based Medical Image Retrieval for Diagnosing Lung Diseases usi...IRJET Journal
This document summarizes research on content-based medical image retrieval (CBMIR) systems for diagnosing lung diseases using CT images. It discusses how CBMIR systems can help radiologists retrieve similar lung nodule images from large databases to aid in diagnosis. The document reviews several studies on developing CBMIR approaches for retrieving common CT imaging signs of lung diseases. These approaches aim to reduce user intervention and improve retrieval accuracy by utilizing features like shape, texture, and context-sensitive similarity measures. The goal is to assist radiologists, especially less experienced ones, in diagnosis and increase early detection of lung conditions like cancer.
IRJET - Lung Cancer Detection using GLCM and Convolutional Neural NetworkIRJET Journal
This document summarizes a research paper that proposes a method for detecting lung cancer using CT scan images with convolutional neural networks. The method involves preprocessing images using median filtering to remove noise, segmenting images using k-means clustering, extracting features using gray-level co-occurrence matrix, and classifying images using convolutional neural networks. The researchers achieved 96% accuracy in classifying tumors as malignant or benign, which is more accurate than traditional neural network methods.
computer aided detection of pulmonary nodules in ct scansWookjin Choi
The document discusses computer aided detection of pulmonary nodules in CT scans. It introduces lung cancer as a major health problem and describes how detecting nodules early can improve survival rates. It then provides an overview of pulmonary nodule detection CAD systems, describing their general structure and evaluating various approaches in the literature. Key contributions are genetic programming and shape-based classifiers and a hierarchical block analysis method that achieved high performance on a publicly available lung image database.
Neutrosophic sets and fuzzy c means clustering for improving ct liver image s...Aboul Ella Hassanien
The document proposes a hybrid method using neutrosophic sets and fuzzy c-means clustering to improve liver segmentation in CT images. It transforms the image into neutrosophic domains of truth, indeterminacy, and falsity. Thresholds are adapted using fuzzy c-means to binarize the domains. Experimental results on 30 abdominal CT images found 88% accuracy by Jaccard index and 94% by Dice coefficient, outperforming other methods. The approach effectively handles noise and uncertainty to produce clear liver boundaries.
This document proposes a computer-aided lung cancer classification system using curvelet features and an ensemble classifier. It first pre-processes CT images using adaptive histogram equalization to improve contrast. Then it segments the images using kernelized fuzzy c-means clustering. Curvelet features are extracted from the segmented regions and an ensemble classifier is applied to classify regions as benign or malignant. The proposed approach achieves reliable and accurate classification results compared to existing methods, with better performance metrics like accuracy, sensitivity and specificity.
IRJET- Analysis of Lung Cancer using Multilayer Perceptron ClassifierIRJET Journal
The document describes a study that analyzed lung cancer using a multilayer perceptron classifier. The researchers collected CT scan images from a database and segmented the lungs using watershed segmentation. They extracted features from the segmented lungs using gray level co-occurrence matrix (GLCM) analysis. These features were then used to train a multilayer perceptron neural network classifier to predict the cancer stage. The proposed method achieved 90% accuracy in lung cancer prediction, which could help identify the disease at an early stage and improve treatment outcomes.
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.
Detection of Lung Cancer using SVM ClassificationIRJET Journal
This document presents a method for detecting lung cancer using support vector machine (SVM) classification of sputum cell images. The authors first extract features from sputum cell images such as nucleus-cytoplasm ratio, perimeter, density, curvature, and circularity. They then use these extracted features to train an SVM classifier to classify sputum cells as cancerous or normal. The authors test their proposed method on 100 sputum cell images and evaluate the technique's performance using metrics like sensitivity, precision, specificity, and accuracy. Their results indicate the SVM classification approach shows potential for early detection of lung cancer from sputum cell analysis.
automatic detection of pulmonary nodules in lung ct imagesWookjin Choi
The document discusses lung cancer detection using CT scans and pulmonary nodule detection systems. It describes how CT scans are used to detect lung nodules early and increase survival rates. It then discusses the challenges of evaluating large CT data sets and the use of pulmonary nodule detection CAD systems to assist radiologists. The document goes on to describe a proposed CAD system that includes lung segmentation, nodule candidate detection using multi-thresholding and feature extraction, and a genetic programming based classifier to analyze features and detect nodules. Experimental results on a publicly available lung image database show the system achieved over 80% accuracy on test data for nodule detection.
Lung Cancer Detection using Image Processing TechniquesIRJET Journal
This document presents a technique for detecting lung cancer in x-ray images using image processing. It involves enhancing images using Gabor filtering, segmenting images using marker-controlled watershed segmentation, and extracting features using binarization and masking. The key steps are collecting lung x-ray images, enhancing quality using Gabor filtering, segmenting regions of interest using watershed segmentation, extracting pixel counts and mask features, and classifying images as normal or abnormal based on these features. The goal is to enable early detection of lung cancer through automated analysis of medical images.
IRJET- Simulation Measurement for Detection of the Breast Tumors by using Ult...IRJET Journal
The document describes a simulation study that used ultra-wideband radar to detect breast tumors. A hemispherical breast phantom with different dielectric properties for healthy and tumor tissue was modeled. A directional antenna was used to illuminate the phantom and measure scattering parameters. Simulation results showed scattering increased when a tumor was present and increased further as the antenna approached the tumor, indicating the method could successfully detect tumors.
IRJET- Image Processing based Lung Tumor Detection System for CT ImagesIRJET Journal
This document presents a method for detecting lung tumors in CT scan images using image processing techniques. The proposed method involves preprocessing images using median filtering for noise removal and contrast adjustment for enhancement. The lungs are then segmented from the images using mathematical morphology. Geometric and textural features are extracted from the segmented region of interest and used as input for an SVM classifier to detect lung cancer. The methodology was tested on a dataset from The Cancer Imaging Archive and was able to successfully detect lung tumors in CT images.
IRJET- Lung Diseases using Deep Learning: A Review PaperIRJET Journal
This document reviews research on using deep learning techniques to detect lung diseases from medical images. It first provides background on lung anatomy and common lung diseases like pneumonia, tuberculosis, and lung cancer. Feature extraction and classification algorithms are important for automated detection. Several studies are summarized that used techniques like convolutional neural networks (CNNs) and support vector machines (SVMs) with shape, texture, and focal features to classify chest x-rays and CT scans as normal or abnormal. Deep learning approaches achieved higher accuracy than traditional "bag of features" methods. Overall, CNNs showed potential for developing high-performance automated lung disease detection systems.
Robust breathing signal extraction from cone beam CT projections based on ada...Wookjin Choi
This document summarizes a research paper that proposes a novel method for extracting breathing signals from cone beam CT projections without using external markers. The method uses an adaptive filtering technique to enhance weak oscillating structures in the Amsterdam Shroud image generated from the projections. A two-step optimization approach is then used to reveal the large-scale regularity of the breathing signals. Evaluation on 5 patient data sets found the new algorithm outperformed existing methods by extracting less noisy signals with errors of only -0.07±1.58 breaths per minute compared to reference signals. While results are promising, the study had a small data set and image quality remains limited.
Lung Nodule Segmentation in CT Images using Rotation Invariant Local Binary P...IDES Editor
As the lung cancer is the leading cause of cancer
death in the medical field, Computed Tomography (CT) scan
of the thorax is widely applied in diagnoses for identifying
the lung cancer. In this paper, a technique of rotation invariant
with Local Binary Pattern (LBP) for segmentation of various
lung nodules from the Lung CT cancer data sets is used. This
is tested on various lung data sets from teaching files of
Casimage database and National Cancer Institute (NCI) of
National Biomedical Imaging Archive (NBIA). The results
show the segmented nodules with clear boundaries, which is
helpful in diagnosis of lung cancer. Further, the results are
compared with the watershed segmentation method, which
shows that LBP based method yields better segmentation
accuracy.
Features of new installed linac Trilogy At Dr Ziauddin Hospital KarachiRahim Gohar
This document provides an overview of the features and capabilities of a new Trilogy linear accelerator installed at Dr. Ziauddin Hospital's radiation oncology department. It describes the linac's engineering, its ability to perform treatments using 3D-CRT, IMRT, VMAT, electrons, and SRS. It also discusses the linac's imaging modalities for treatment planning and verification. Key treatment techniques like IMRT, VMAT, and IGRT are summarized in terms of how they are planned and delivered using the linac and its integrated treatment planning system.
Image processing in lung cancer screening and treatmentWookjin Choi
The document discusses image processing techniques for lung cancer screening and treatment. It covers topics like lung segmentation, nodule detection, computer-aided diagnosis, image-guided radiotherapy, and quantitative assessment of tumor response. Lung segmentation is used to isolate the lungs from other organs in CT images. Nodule detection algorithms then aim to find potential cancerous nodules. Computer-aided diagnosis systems analyze extracted features of nodules to determine if they are malignant or benign. Image-guided radiotherapy utilizes 4D CT and gating to account for tumor motion during treatment. Quantitative metrics like standardized uptake value are used to assess tumor response in PET imaging.
Detection of Prostate Cancer Using Radial/Axial Scanning of 2D Trans-rectal U...journalBEEI
The search for improvement in the result of segmentation of regions of interest in medical images has continued to be a source of challenge to researchers. Several research efforts have gone in to delineate regions of interest in the prostate gland from Trans-rectal ultrasound (TRUS) 2D-images. In this work, we develop a fast algorithm based on radial/axial scanning of the pixels of the prostate gland image with the goal of detecting hyper-echoic pixels that are bound within the boundaries of the gland TRUS 2D-images. The algorithm implements expert knowledge and utilizes the features extracted from the intensity of the TRUS images, primarily the relative intensity and gradient to delineate region of interest. It employs radial/axial scanning of the image from common seed point automatically selected to detect the region of the gland and subsequently hyper-echoic pixels which indicate suspected cancerous tissue cites. Evaluation of the algorithm performance was done by comparing detection result with that of expert radiologists. The detection algorithm gave an average accuracy of 88.55% and sensitivity of 71.65%.
This document discusses preliminary dosimetric analysis of target motion effects in 4D tomotherapy and outlines several challenges and potential solutions:
1) Contouring targets across multiple respiratory phases is time-consuming; research consoles can help by propagating contours and creating average images.
2) Planning and dose computation across phases is complex; multiple plans must be evaluated to assess potential underdosing.
3) Initial QA using dynamic phantoms shows dose shifts near targets, underscoring the need for 4D evaluations and potentially larger margins.
4) Further investigations of 4D imaging, planning, dose computation and adaptive techniques are needed to fully account for respiratory motion effects in tomotherapy.
CANCER CELL DETECTION USING DIGITAL IMAGE PROCESSINGkajikho9
The document presents a lung cancer detection system using digital image processing techniques. It discusses lung anatomy and types of lung cancer. The system involves image capture, pre-processing using enhancement filters like Gabor and FFT, segmentation using thresholding and watershed approaches. Feature extraction is done using binarization and masking to detect cancer presence. The system helps in early detection of lung cancer to reduce mortality.
This document discusses brain tumor segmentation from MRI images using fuzzy c-means clustering. It begins with an introduction to brain tumors and MRI imaging. Next, it reviews existing methods for brain tumor segmentation such as thresholding, region growing, and clustering. It then discusses preprocessing MRI images, including converting images to grayscale and filtering. Finally, it describes fuzzy c-means clustering, which is an unsupervised learning technique used to segment and classify pixels in MRI images to detect tumor regions. The goal is to develop an accurate and automated method for brain tumor segmentation to assist medical experts.
IRJET- Content based Medical Image Retrieval for Diagnosing Lung Diseases usi...IRJET Journal
This document summarizes research on content-based medical image retrieval (CBMIR) systems for diagnosing lung diseases using CT images. It discusses how CBMIR systems can help radiologists retrieve similar lung nodule images from large databases to aid in diagnosis. The document reviews several studies on developing CBMIR approaches for retrieving common CT imaging signs of lung diseases. These approaches aim to reduce user intervention and improve retrieval accuracy by utilizing features like shape, texture, and context-sensitive similarity measures. The goal is to assist radiologists, especially less experienced ones, in diagnosis and increase early detection of lung conditions like cancer.
IRJET - Lung Cancer Detection using GLCM and Convolutional Neural NetworkIRJET Journal
This document summarizes a research paper that proposes a method for detecting lung cancer using CT scan images with convolutional neural networks. The method involves preprocessing images using median filtering to remove noise, segmenting images using k-means clustering, extracting features using gray-level co-occurrence matrix, and classifying images using convolutional neural networks. The researchers achieved 96% accuracy in classifying tumors as malignant or benign, which is more accurate than traditional neural network methods.
computer aided detection of pulmonary nodules in ct scansWookjin Choi
The document discusses computer aided detection of pulmonary nodules in CT scans. It introduces lung cancer as a major health problem and describes how detecting nodules early can improve survival rates. It then provides an overview of pulmonary nodule detection CAD systems, describing their general structure and evaluating various approaches in the literature. Key contributions are genetic programming and shape-based classifiers and a hierarchical block analysis method that achieved high performance on a publicly available lung image database.
Neutrosophic sets and fuzzy c means clustering for improving ct liver image s...Aboul Ella Hassanien
The document proposes a hybrid method using neutrosophic sets and fuzzy c-means clustering to improve liver segmentation in CT images. It transforms the image into neutrosophic domains of truth, indeterminacy, and falsity. Thresholds are adapted using fuzzy c-means to binarize the domains. Experimental results on 30 abdominal CT images found 88% accuracy by Jaccard index and 94% by Dice coefficient, outperforming other methods. The approach effectively handles noise and uncertainty to produce clear liver boundaries.
This document proposes a computer-aided lung cancer classification system using curvelet features and an ensemble classifier. It first pre-processes CT images using adaptive histogram equalization to improve contrast. Then it segments the images using kernelized fuzzy c-means clustering. Curvelet features are extracted from the segmented regions and an ensemble classifier is applied to classify regions as benign or malignant. The proposed approach achieves reliable and accurate classification results compared to existing methods, with better performance metrics like accuracy, sensitivity and specificity.
IRJET- Analysis of Lung Cancer using Multilayer Perceptron ClassifierIRJET Journal
The document describes a study that analyzed lung cancer using a multilayer perceptron classifier. The researchers collected CT scan images from a database and segmented the lungs using watershed segmentation. They extracted features from the segmented lungs using gray level co-occurrence matrix (GLCM) analysis. These features were then used to train a multilayer perceptron neural network classifier to predict the cancer stage. The proposed method achieved 90% accuracy in lung cancer prediction, which could help identify the disease at an early stage and improve treatment outcomes.
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...ijcsit
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields
such as satellite, remote sensing, object identification, face tracking and most importantly in medical field.
In radiology, magnetic resonance imaging (MRI) is used to investigate the human body processes and
functions of organisms. In hospitals, this technique has been using widely for medical diagnosis, to find the
disease stage and follow-up without exposure to ionizing radiation.Here in this paper, we proposed a novel
MR brain image segmentation method for detecting the tumor and finding the tumor area with improved
performance over conventional segmentation techniques such as fuzzy c means (FCM), K-means and even
that of manual segmentation in terms of precision time and accuracy. Simulation performance shows that
the proposed scheme has performed superior to the existing segmentation methods.
A Review On Lung Cancer Detection From CT Scan Images Using CNNDon Dooley
The document reviews various methodologies for detecting lung cancer from CT scan images, finding that convolutional neural networks along with image processing provide the most suitable approach. It discusses related work applying techniques like image enhancement, segmentation, and machine learning classification to identify cancerous nodules. Recent approaches using deep learning, specifically 3D convolutional neural networks, achieve high accuracy rates of 94-96% for cancer detection and classification.
A new procedure for lung region segmentation from computed tomography imagesIJECEIAES
Lung cancer is the leading cause of cancer death among people worldwide. The primary aim of this research is to establish an image processing method for lung cancer detection. This paper focuses on lung region segmentation from computed tomography (CT) scan images. In this work, a new procedure for lung region segmentation is proposed. First, the lung CT scan images will undergo an image thresholding stage before going through two morphological reconstruction and masking stages. In between morphological and masking stages, object extraction, border change, and object elimination will occur. Finally, the lung field will be annotated. The outcomes of the proposed procedure and previous lung segmentation methods i.e., the modified watershed segmentation method is compared with the ground truth images for performance evaluation that will be carried out both in qualitative and quantitative manners. Based on the analyses, the new proposed procedure for lung segmentation, denotes better performance, an increment by 0.02% to 3.5% in quantitative analysis. The proposed procedure produced better-segmented images for qualitative analysis and became the most frequently selected method by the 22 experts. This study shows that the outcome from the proposed method outperforms the existing modified watershed segmentation method.
A REVIEW PAPER ON PULMONARY NODULE DETECTIONIRJET Journal
This document reviews different techniques for pulmonary nodule detection in CT scans using deep learning. It summarizes several papers that have used techniques like convolutional neural networks (CNNs), 3D CNNs, and customized mixed link networks to develop computer-aided diagnosis systems for detecting and classifying lung nodules. These papers report accuracy rates from 85.7% to 98.7% and sensitivities from 80.06% to 94% depending on the specific deep learning approach and dataset used. The document concludes by comparing the performance of these different papers.
Early Detection of Lung Cancer Using Neural Network TechniquesIJERA Editor
Effective identification of lung cancer at an initial stage is an important and crucial aspect of image processing. Several data mining methods have been used to detect lung cancer at early stage. In this paper, an approach has been presented which will diagnose lung cancer at an initial stage using CT scan images which are in Dicom (DCM) format. One of the key challenges is to remove white Gaussian noise from the CT scan image, which is done using non local mean filter and to segment the lung Otsu’s thresholding is used. The textural and structural features are extracted from the processed image to form feature vector. In this paper, three classifiers namely SVM, ANN, and k-NN are applied for the detection of lung cancer to find the severity of disease (stage I or stage II) and comparison is made with ANN, and k-NN classifier with respect to different quality attributes such as accuracy, sensitivity(recall), precision and specificity. It has been found from results that SVM achieves higher accuracy of 95.12% while ANN achieves 92.68% accuracy on the given data set and k-NN shows least accuracy of 85.37%. SVM algorithm which achieves 95.12% accuracy helps patients to take remedial action on time and reduces mortality rate from this deadly disease.
Using Distance Measure based Classification in Automatic Extraction of Lungs ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
Using Distance Measure based Classification in Automatic Extraction of Lungs ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
Using Distance Measure based Classification in Automatic Extraction of Lungs ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
USING DISTANCE MEASURE BASED CLASSIFICATION IN AUTOMATIC EXTRACTION OF LUNGS ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
An Enhanced ILD Diagnosis Method using DWTIOSR Journals
1. The document describes an enhanced method for diagnosing Interstitial Lung Disease (ILD) using Discrete Wavelet Transform (DWT).
2. The method involves acquiring CT lung images, enhancing edges using DWT, segmenting the lung region, segmenting blood vessels, extracting texture features from vessels, and classifying images as normal or ILD using Fuzzy Support Vector Machines (FSVM).
3. Wavelet edge enhancement improves segmentation of vessels. Feature extraction using co-occurrence matrices and discriminant analysis reduces dimensions before FSVM classification. The method achieves accurate ILD diagnosis compared to existing approaches.
IRJET- Review Paper on a Review on Lung Cancer Detection using Digital Image ...IRJET Journal
This document reviews techniques for detecting lung cancer from digital chest images. It discusses how digital image processing techniques like preprocessing, segmentation, feature extraction and neural networks can be used to analyze CT scan images and detect lung cancers. Preprocessing steps include grayscale conversion, normalization, noise reduction and binary conversion. Segmentation methods like thresholding are used to isolate the lungs. Features like size and shape are extracted and analyzed by neural networks to classify lesions and detect cancers. The paper suggests this automated detection system could help address limitations of manual radiologist review like missed cancers.
Iaetsd classification of lung tumour usingIaetsd Iaetsd
This document describes a study that aims to classify lung tumors using geometric and texture features extracted from chest x-ray images. The study uses 75 chest x-ray images (25 from small-cell lung cancer, 25 from non-small cell lung cancer, and 25 from tuberculosis) to extract geometric features like area, shape, and distance from texture features calculated using gray level co-occurrence matrices. Active shape models are used to segment the lung fields for feature extraction. The extracted features are then analyzed to determine the optimal features for classifying different types of lung abnormalities.
IRJET- Lung Segmentation and Nodule Detection based on CT Images using Image ...IRJET Journal
This document presents a method for lung nodule detection and segmentation from CT images using image processing techniques. The proposed method involves pre-processing the CT images, segmenting the lung region, extracting potential nodules, and classifying nodules. In pre-processing, filters are applied to reduce noise. Region growing and thresholding are used to segment the lungs. Potential nodules are extracted based on size thresholds. Finally, nodules are classified as benign or malignant using features and an artificial neural network algorithm. Experimental results on sample CT images demonstrate each step of the proposed lung nodule detection and segmentation method.
IRJET- A New Strategy to Detect Lung Cancer on CT ImagesIRJET Journal
This document presents a new strategy for detecting lung cancer on CT images using image processing techniques. It involves acquiring CT scan images, preprocessing the images through techniques like grayscale conversion and Gabor filtering, segmenting the images using adaptive thresholding, extracting regions of interest through feature extraction methods like GLCM, and classifying images as cancerous or normal using support vector machines (SVM) and backpropagation neural networks (BPNN). The methodology achieves 96.32% accuracy for SVM and 83.07% accuracy for BPNN in detecting lung cancer from CT images.
The document describes research on improving lung nodule detection accuracy using an effective 3D CNN framework. The proposed MR3DCNN-KT model aims to capture contextual information between slices using 3D CNN. It also aims to reduce false positives and negatives through an iteratively optimized deep learning method and reduce 3D CNN complexity. Experimental results on a lung CT dataset show the MR3DCNN-KT model achieves higher accuracy, precision, recall, and F-measure than existing methods, demonstrating its effectiveness in automatic lung nodule detection.
This document summarizes key aspects of low-dose CT (LDCT) techniques, reading methods, and image interpretation for lung cancer screening. It discusses the LDCT technique, noting parameters like multidetector CT scanners, dose levels around 2 mSv, and iterative reconstruction methods. It also covers reading methods like double reading and CAD to improve nodule detection rates. Further, it distinguishes solid versus subsolid nodule types and challenges in their measurement, given implications for malignancy rates and growth patterns.
This document describes a proposed computer-aided detection system for identifying lung nodules in computed tomography scans. It begins with an introduction to lung cancer and the need for automated detection systems. It then discusses previous research on lung nodule detection methods and their limitations. The document proposes a new rule-based algorithm utilizing segmentation, enhancement, filtering, component labeling, and feature extraction to detect nodules and reduce false positives. It describes applying this algorithm to CT scans to evaluate the system's performance.
Enhancing Lung Cancer Detection with Deep Learning: A CT Image Classification...IRJET Journal
This document presents a study that aims to enhance lung cancer detection through deep learning techniques. The proposed framework includes image preprocessing, segmentation of lung CT images, and classification of images using deep learning models. Three classification models were evaluated: DCNN, DCDNN, and ANN. The DCNN model achieved the best accuracy at 99.41% in detecting lung cancer. Future work could focus on early cancer detection and integration with other medical data to improve predictive capabilities. Deep learning shows promise for accurate lung cancer analysis and enables personalized treatment.
Similar to Cancerous lung nodule detection in computed tomography images (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
This document describes using a snake optimization algorithm to tune the gains of an enhanced proportional-integral controller for congestion avoidance in a TCP/AQM system. The controller aims to maintain a stable and desired queue size without noise or transmission problems. A linearized model of the TCP/AQM system is presented. An enhanced PI controller combining nonlinear gain and original PI gains is proposed. The snake optimization algorithm is then used to tune the parameters of the enhanced PI controller to achieve optimal system performance and response. Simulation results are discussed showing the proposed controller provides a stable and robust behavior for congestion control.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
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Cancerous lung nodule detection in computed tomography images
1. TELKOMNIKA Telecommunication, Computing, Electronics and Control
Vol. 18, No. 5, October 2020, pp. 2432~2438
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v18i5.15523 2432
Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA
Cancerous lung nodule detection in
computed tomography images
Ayman Abu Baker1
, Yazeed Ghadi 2
1
Department of Electrical Engineering, Applied Science Private University, Jordan
2
Department of Electrical Engineering, Al Ain University of Science and Technology, United Arab Emirates
Article Info ABSTRACT
Article history:
Received Jan 20, 2020
Revised Mar 28, 2020
Accepted Jun 12, 2020
Diagnosis the computed tomography images (CT-images) is one of
the images that may take a lot of time in diagnosis by the radiologist and may
miss some of cancerous nodules in these images. Therefore, in this paper
a new novel enhancement and detection cancerous nodule algorithm is
proposed to diagnose a CT-images. The novel algorithm is divided into three
main stages. In first stage, suspicious regions are enhanced using modified
LoG algorithm. Then in stage two, a potential cancerous nodule was detected
based on visual appearance in lung. Finally, five texture features analysis
algorithm is implemented to reduce number of detected FP regions. This
algorithm is evaluated using 60 cases (normal and cancerous cases), and it
shows a high sensitivity in detecting the cancerous lung nodules with TP
ration 97% and with FP ratio 25 cluster/image.
Keywords:
Cancer detection
Computed tomography
Lung cancer
Texture features
Laplacian filter This is an open access article under the CC BY-SA license.
Corresponding Author:
Ayman Abu Baker,
Department of Electrical and Computer Engineering,
Applied Science Private University,
Amman, Jordan.
Email: a_abubaker@asu.edu.jo
1. INTRODUCTION
Lung cancer is one of the most relevant public health issues in United states, Europe and Middle
East [1, 2]. Early Detection and treatment of this types of cancer is require to effectively overcome this
burden. Chest X-ray considered a cheapest method as an initial detection of lung cancer. Computed
tomography (CT) as a second diagnosis stage is the best imaging modality for the detection of small
pulmonary nodules, particularly since the introduction of the helical technology [3, 4]. The CT images are
a high resolution images with high amount of data storage. Therefore, researchers tries to help the radiologist
to easily process these huge image and automatically detected the potential nodule lung cancers using
computer aided diagnosis system (CAD) [5, 6]. Detection nodule lung cancer is one of the most difficult
cases for the radiologist specially in CT images since the visual appearance of tumors is not clear or
surrounding with parenchymal tissue in CT-Images [7, 8]. Therefore, the visual appearance for cancerous
nodules have similar visual characteristics of normal tissues [9]. Therefore, this paper proposed a novel
detection and classification method for cancerous cells. Three main stages are used to accurately detect
the cancerous nodules in the CT lung images [10]. The paper present a brief description about the lung CT
image in section 2. Then literature review section which presented in section 3. Proposed detection and
classification algorithm is introduced in Section 4. Finally, discussion and conclusion are presented in
section 5 and section 6 respectively.
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Cancerous lung nodule detection in computed tomography images (Ayman Abu Baker)
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2. DATABASE
The database that are used in this paper in downloaded from Cancer Imaging Archive (TCIA), which is
organized into purpose-built collections of subjects [11]. This database has different kind of high resolution images
of cancer type and/or anatomical site (lung, brain, etc.) in common. Moreover, the database include a huge dataset
of MRI, CT, and X-Ray images that are stored as DICOM file format [12].
3. LITERATURE REVIEW
Many authors proposed different CAD techniques to help the radiologist in diagnoses lung cancer in
CT images. One of these techniques is using Laplacian of Gaussian filter as in [13]. Their algorithm is
divided to two main stages, transmission of high intensity and LOG Filter. These stages are applied calculate
the contrast differences inside and outside region of interest. Snake algorithm is another enhancement and
segmentation tool used to find the internal energy in CT image like Vivekanandan D. et al [14].
Messay et al. [15] implement local contrast enhancement filter that follow the nodule enhancement method to
detect the chest radiographs. Dots, lines, and planes enhancement techniques are used by Li et al. [16]. Such
these enhancement techniques can be implemented on specific shapes and suppress other objects. So, as an
initial step the CT-Image is blurred then a Gaussian kernel filter (GKF) of a nodule size is implemented to
detect the nodule. As final stage, multi-scales GKF are used as to find a match with the nodule size.
Yamamoto et al. [17] used a statistical enhancement filter to enhance CT images. They implemented
the Quoit Filter that has large ring and disk filters. Differential intensity value procedure is implemented in
this algorithm between the internal and external disks in order to find a potential region of interest. Another
segmentation algorithm is presented by Maciej Dajnowiec et al. [18]. Initially, an optimum threshold value
was calculated based on different image data sets. Threshold algorithm then is implemented to segment lunge
region from CT image. After that, connecting component labeling (CCL) is applied to detect remaining
regions in the image. Therefore, lung nodule can successfully segmented but have lobes with nodules region.
On the other hand, CAD system is a powerful tool in detecting cancerous nodules in the CT images.
Another CAD system to detect cancerous lung region is proposed by Lee et al. [19]. Authors implement GA
to set a target position and also to match the best template image from previous data sets. Four generations
every time are established based on grey level of Gaussian distribution. Algorithm sensitivity was 85% in
detecting cancerous nodules in the CT-images. Other author like Opfer et al. [20] used distance
transformation technique to improve the performance of CAD system. The distance transformations of
various thresholds and subsequent crest line extraction are used enhance the CAD sensitivity. CAD system
performance is slightly improved when using this technique since it produce large number of FP regions.
Another CAD system is used by Moreover, Golosio et al. [21] to detect cancerous nodules in CT images.
They used multi-adaptive threshold surface triangulation approach in the detection algorithm. The proposed
algorithm show good sensitivity in detection cancer nodules but many detected FP are presents.
K. Devaki et al. [22] develop a technique to accurately segment lung region from CT- images. The proposed
algorithm can efficiently segment the lung regions from CT-images.
4. RESEARCH METHOD
Enhancement and detection of the cancerous nodules technique is proposed in this paper.
The proposed method is mainly divided to three stages. Initially, the cancerous lung nodules will be enhanced
using Laplacian filter. Then in the second stage, the average detection algorithm will be used to detect
a potential cancerous lung nodule (PCLN) regions in CT-images. Final, five texture features are used to help
in reducing number of detected FP regions. Figure 1, show the proposed algorithm stages.
4.1. Cancerous lung nodule enhancement
The fundamental operation needed to assist cancerous nodule in CT-image is contrast enhancement.
In many image processing applications, the Laplacian filter is one of the simplest and effective techniques for
intensity enhancement that presented in as (1). Laplacian filter improves contrast of the cancerous nodules in
CT-image by applying the Laplacian mask of size 9 × 9.
𝑆𝑆(𝑥𝑥, 𝑦𝑦) = 𝑓𝑓(𝑥𝑥, 𝑦𝑦) + 𝑐𝑐[𝛻𝛻2
𝑓𝑓(𝑥𝑥, 𝑦𝑦)] (1)
where S(x, y) is the intensity value for the processed image, f(x, y) the intensity value for the input image and
c is consider as one in this paper.
Cancerous nodules appear on digitized CT as small regions, with intensity values higher than their
surrounding background. The size of cancerous nodules is usually less than 4 mm [23]. So it is not easy to
enhance the cancerous nodules regions since surrounding lung tissue makes the abnormality areas almost
3. ISSN: 1693-6930
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invisible. Therefore, the modified average filter is implemented to smooth the edges of the processed
Laplacian enhancement image. This in case will slightly enhance the cancerous nodules regions to be easily
detected in the next stage.
After extensive analysis of 60 cancerous CT images, we concluded that all cancerous nodules have
grey scale values in the range from 80 to 230 [24, 25]. In accordance with these observations, each CT-image
is processed using the modified average filter that presented on (2),
𝑆𝑆𝐾𝐾 =
1
𝑚𝑚𝑚𝑚
� 𝑟𝑟𝑗𝑗
230
𝑗𝑗=80
(2)
where Sk is the intensity value for the processed image, rj the intensity value for the input image and m and n
are the mask size. After processing the Laplacian and modified average filter, the cancerous nodule regions
become slightly brighter corresponding to the neighbor regions as shown in Figure 2. This will assist in
detection the region of interest that will be discussed in the next section.
Figure 1. MC detection and classification Figure 2. Cancerous nodule enhancement result
4.2. Potential Cancerous lung nodules
Cancerous nodules appear on digitized CT-images as small regions, with intensity values higher
than their surrounding background. So in order to detect these region, two concentric circular masks are used
as shown in Figure 3. When centered on the cancerous nodule, the inner masked region included
the cancerous nodule while the outer masked region included the surrounding region. The inner and outer
concentric circular mask’s size are design based on CT-Image resolution. These masks were tested on 60
CT-image and they were effective in detecting all the suspicious regions in CT-Images. Detection
the potential cancerous lung nodules (PCLN) cluster is designed based on the fact that the cancerous nodules
are brighter than the neighbor pixels. Therefore, in order to select PCLN two conditions should be satisfied,
average value for the inner mask should be greater than outer mask and the intensity pixel value of the center
of the inner mask should be the highest intensity in the mask. After processing 60 CT-images using PCLN
algorithm, it was noticed that all cancerous nodules in the CT-images are detected but many detected false
positive (FP) regions as shown in Figure 4. This in case will reduce the sensitivity of the proposed CAD
system. Therefore, texture feature analysis will be applied to reduce number of detected FP clusters and
increase the sensitivity of this CAD system.
4. TELKOMNIKA Telecommun Comput El Control
Cancerous lung nodule detection in computed tomography images (Ayman Abu Baker)
2435
Figure. 3. Two concentric circular masks
Figure 4. PCLN processing result
4.3. Texture feature extraction
Cancerous nodules are a small size less than 4 mm with a bright region comparing with
the surrounding regions in the lung. So, in order to have a platform that can help radiologist in diagnosis
these types of tumors, we need to focus on the view characteristics of cancerous nodules in CT-Images.
As first step, the cancerous nodules are enhanced and a potential cancerous nodules algorithm is implemented
to detect cancerous clusters in CT- images. Both algorithms are effectively detect the cancerous regions in
the CT image but with high number of false positive (FP) regions. In this section, we implement a new
approach to reduce number of FP regions using texture features. Having minimum number of FP region will
enhance the performance of our CAD system.
First order texture features base is used in this paper to reduce detected number of FP regions. So,
two datasets are generated to achieve this goal. TP dataset is generated using 223 expertly identified (actual)
TP clusters and the second dataset which is FP dataset used 415 actual FP clusters. Then five texture features
are calculated based on these datasets (TP and FP). These features are summarized in (4)-(8).
𝑃𝑃(𝑖𝑖) = � ℎ(𝑖𝑖)/𝑀𝑀𝑀𝑀
240
𝑖𝑖=40
(3)
Where h(i) is the intensity histogram and M, N are the image region’s height and width respectively.
− The modified mean feature
𝜇𝜇 = � 𝑖𝑖 𝑖𝑖(𝑖𝑖)
240
𝑖𝑖=40
(4)
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− The modified entropy feature
𝐸𝐸 = − � 𝑃𝑃(𝑖𝑖) 𝑙𝑙𝑙𝑙 𝑙𝑙2[ 𝑃𝑃(𝑖𝑖)]
240
𝑖𝑖=40
(5)
− The modified standard deviation feature
𝜎𝜎 = �� (𝑖𝑖 − 𝜇𝜇)2 𝑃𝑃(𝑖𝑖)
240
𝑖𝑖=40
(6)
− The modified third order of moment feature
𝑀𝑀3 = � (𝑖𝑖 − 𝜇𝜇)3
𝑃𝑃(𝑖𝑖)
240
𝑖𝑖=40
(7)
− The modified kurtosis feature
𝐾𝐾 = 𝜎𝜎−4
� (𝑖𝑖 − 𝜇𝜇)4
𝑃𝑃(𝑖𝑖) − 3
240
𝑖𝑖=40
(8)
The values of these features are presented as shown in Figure 5.
Figure 5. The first order feature
5. RESULTS AND ANALYSIS
The cancerous nodule enhancement and detection algorithm is applied on 60 CT-images. Then, five
texture features are generated (Entropy, mean, STD, kurtosis and skewness) which were processed using
statistical analysis to reduce the detected FP regions. The algorithm is subjectively evaluated using three
radiologist, where number of detected FP regions are counted per image. Also TP percentage of each image
is also recoded. Finally, the average of detected FP region and TP percentage is presented in the Table 1 after
6. TELKOMNIKA Telecommun Comput El Control
Cancerous lung nodule detection in computed tomography images (Ayman Abu Baker)
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processing 60 CT-image. From Table 1, it is evident that our algorithm achieves a good performance in
detecting cancerous nodules but still number of FP is slightly high. Figure 6 show the results of CT
processing stages where the image processed using enhancement algorithm, then detection region of interest.
Finally the texture feature analysis is implemented to reduce of the detected FP regions.
(a) (b)
(c) (d)
Figure 6. Accurate detection of cancerous; (a) original image, (b) image enhancement,
(c) image with PLCN, (d) texture features analysis
Table 1. Image quality evaluation
Average of detected FP region (cluster) Average of TP (%)
Radiologist 1 28 97%
Radiologist 2 23 98%
Radiologist 2 24 96%
6. CONCLUSION
This paper presents the ongoing effort in enhancing and detecting the cancerous nodules in the CT
images. This proposes algorithm is divided to three phases. The cancerous nodules are enhanced using
the Laplacian filter. Then, the average filter is modified based on the lower and upper grey levels of
the cancerous nodules in the CT images. This incase, slightly enhances the cancerous nodules in
the mammogram images. In the second phase, the potential cancerous nodules regions are detected using
multi statistical filter which results large number of FP regions. Finally, texture feature analysis is
implemented to reduce the detected FP regions. As a result, the proposed algorithm is subjectively and
objectively tested on 60 CT images and it shows that is algorithm can detect the cancerous nodules with
an average rate 97.6% with FP regions of 25 cluster.
ACKNOWLEDGEMENTS
The author is grateful to the Applied Science Private University, Amman, Jordan, for the full financial.
7. ISSN: 1693-6930
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