Computerized Analysis Of Mammographic Images For Detection And Characterization Of Breast Cancer
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Computerized Analysis Of Mammographic Images For Detection And Characterization Of Breast Cancer
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Author : Paola Casti
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2017-07-06
Computerized Analysis Of Mammographic Images For Detection And Characterization Of Breast Cancer written by Paola Casti and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-06 with Technology & Engineering categories.
The identification and interpretation of the signs of breast cancer in mammographic images from screening programs can be very difficult due to the subtle and diversified appearance of breast disease. This book presents new image processing and pattern recognition techniques for computer-aided detection and diagnosis of breast cancer in its various forms. The main goals are: (1) the identification of bilateral asymmetry as an early sign of breast disease which is not detectable by other existing approaches; and (2) the detection and classification of masses and regions of architectural distortion, as benign lesions or malignant tumors, in a unified framework that does not require accurate extraction of the contours of the lesions. The innovative aspects of the work include the design and validation of landmarking algorithms, automatic Tabár masking procedures, and various feature descriptors for quantification of similarity and for contour independent classification of mammographic lesions. Characterization of breast tissue patterns is achieved by means of multidirectional Gabor filters. For the classification tasks, pattern recognition strategies, including Fisher linear discriminant analysis, Bayesian classifiers, support vector machines, and neural networks are applied using automatic selection of features and cross-validation techniques. Computer-aided detection of bilateral asymmetry resulted in accuracy up to 0.94, with sensitivity and specificity of 1 and 0.88, respectively. Computer-aided diagnosis of automatically detected lesions provided sensitivity of detection of malignant tumors in the range of [0.70, 0.81] at a range of falsely detected tumors of [0.82, 3.47] per image. The techniques presented in this work are effective in detecting and characterizing various mammographic signs of breast disease.
Computer Aided Detection Of Architectural Distortion In Prior Mammograms Of Interval Cancer
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Author : Shantanu Banik
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2013-01-01
Computer Aided Detection Of Architectural Distortion In Prior Mammograms Of Interval Cancer written by Shantanu Banik and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-01-01 with Technology & Engineering categories.
Architectural distortion is an important and early sign of breast cancer, but because of its subtlety, it is a common cause of false-negative findings on screening mammograms. Screening mammograms obtained prior to the detection of cancer could contain subtle signs of early stages of breast cancer, in particular, architectural distortion. This book presents image processing and pattern recognition techniques to detect architectural distortion in prior mammograms of interval-cancer cases. The methods are based upon Gabor filters, phase portrait analysis, procedures for the analysis of the angular spread of power, fractal analysis, Laws' texture energy measures derived from geometrically transformed regions of interest (ROIs), and Haralick's texture features. With Gabor filters and phase-portrait analysis, 4,224 ROIs were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 true-positive ROIs related to architectural distortion, and from 52 mammograms of 13 normal cases. For each ROI, the fractal dimension, the entropy of the angular spread of power, 10 Laws' texture energy measures, and Haralick's 14 texture features were computed. The areas under the receiver operating characteristic (ROC) curves obtained using the features selected by stepwise logistic regression and the leave-one-image-out method are 0.77 with the Bayesian classifier, 0.76 with Fisher linear discriminant analysis, and 0.79 with a neural network classifier. Free-response ROC analysis indicated sensitivities of 0.80 and 0.90 at 5.7 and 8.8 false positives (FPs) per image, respectively, with the Bayesian classifier and the leave-one-image-out method. The present study has demonstrated the ability to detect early signs of breast cancer 15 months ahead of the time of clinical diagnosis, on the average, for interval-cancer cases, with a sensitivity of 0.8 at 5.7 FP/image. The presented computer-aided detection techniques, dedicated to accurate detection and localization of architectural distortion, could lead to efficient detection of early and subtle signs of breast cancer at pre-mass-formation stages. Table of Contents: Introduction / Detection of Early Signs of Breast Cancer / Detection and Analysis of Oriented Patterns / Detection of Potential Sites of Architectural Distortion / Experimental Set Up and Datasets / Feature Selection and Pattern Classification / Analysis of Oriented Patterns Related to Architectural Distortion / Detection of Architectural Distortion in Prior Mammograms / Concluding Remarks
Computerized Analysis Of Mammographic Images For Detection And Characterization Of Breast Cancer
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Author : Arianna Mencattini
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Computerized Analysis Of Mammographic Images For Detection And Characterization Of Breast Cancer written by Arianna Mencattini and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.
The identification and interpretation of the signs of breast cancer in mammographic images from screening programs can be very difficult due to the subtle and diversified appearance of breast disease. This book presents new image processing and pattern recognition techniques for computer-aided detection and diagnosis of breast cancer in its various forms. The main goals are: (1) the identification of bilateral asymmetry as an early sign of breast disease which is not detectable by other existing approaches; and (2) the detection and classification of masses and regions of architectural distortion, as benign lesions or malignant tumors, in a unified framework that does not require accurate extraction of the contours of the lesions. The innovative aspects of the work include the design and validation of landmarking algorithms, automatic Tabár masking procedures, and various feature descriptors for quantification of similarity and for contour independent classification of mammographic lesions. Characterization of breast tissue patterns is achieved by means of multidirectional Gabor filters. For the classification tasks, pattern recognition strategies, including Fisher linear discriminant analysis, Bayesian classifiers, support vector machines, and neural networks are applied using automatic selection of features and cross-validation techniques. Computer-aided detection of bilateral asymmetry resulted in accuracy up to 0.94, with sensitivity and specificity of 1 and 0.88, respectively. Computer-aided diagnosis of automatically detected lesions provided sensitivity of detection of malignant tumors in the range of [0.70, 0.81] at a range of falsely detected tumors of [0.82, 3.47] per image. The techniques presented in this work are effective in detecting and characterizing various mammographic signs of breast disease.
Fractal Analysis Of Breast Masses In Mammograms
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Author : Thanh M. Cabral
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2012-10-01
Fractal Analysis Of Breast Masses In Mammograms written by Thanh M. Cabral and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-01 with Technology & Engineering categories.
Fractal analysis is useful in digital image processing for the characterization of shape roughness and gray-scale texture or complexity. Breast masses present shape and gray-scale characteristics in mammograms that vary between benign masses and malignant tumors. This book demonstrates the use of fractal analysis to classify breast masses as benign masses or malignant tumors based on the irregularity exhibited in their contours and the gray-scale variability exhibited in their mammographic images. A few different approaches are described to estimate the fractal dimension (FD) of the contour of a mass, including the ruler method, box-counting method, and the power spectral analysis (PSA) method. Procedures are also described for the estimation of the FD of the gray-scale image of a mass using the blanket method and the PSA method. To facilitate comparative analysis of FD as a feature for pattern classification of breast masses, several other shape features and texture measures are described in the book. The shape features described include compactness, spiculation index, fractional concavity, and Fourier factor. The texture measures described are statistical measures derived from the gray-level cooccurrence matrix of the given image. Texture measures reveal properties about the spatial distribution of the gray levels in the given image; therefore, the performance of texture measures may be dependent on the resolution of the image. For this reason, an analysis of the effect of spatial resolution or pixel size on texture measures in the classification of breast masses is presented in the book. The results demonstrated in the book indicate that fractal analysis is more suitable for characterization of the shape than the gray-level variations of breast masses, with area under the receiver operating characteristics of up to 0.93 with a dataset of 111 mammographic images of masses. The methods and results presented in the book are useful for computer-aided diagnosis of breast cancer. Table of Contents: Computer-Aided Diagnosis of Breast Cancer / Detection and Analysis of\newline Breast Masses / Datasets of Images of Breast Masses / Methods for Fractal Analysis / Pattern Classification / Results of Classification of Breast Masses / Concluding Remarks
State Of The Art In Digital Mammographic Image Analysis
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Author : K. W. Bowyer
language : en
Publisher: World Scientific
Release Date : 1994
State Of The Art In Digital Mammographic Image Analysis written by K. W. Bowyer and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Medical categories.
This book provides a detailed assessment of the state of the art in automated techniques for the analysis of digital mammogram images. Topics covered include a variety of approaches for image processing and pattern recognition aimed at assisting the physician in the task of detecting tumors from evidence in mammogram images. The chapters are written by recognized experts in the field and are revised versions of papers selected from those presented at the “First International Workshop on Mammogram Image Analysis” held in San Jose as part of the 1993 Biomedical Image Processing conference.
Digital Mammography Development Of An Advanced Computer Aided System For Breast Cancer Detection
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Author :
language : en
Publisher:
Release Date : 2004
Digital Mammography Development Of An Advanced Computer Aided System For Breast Cancer Detection written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
The goal of the project is to develop computer-aided diagnosis (CAD) methods and systems for mammography using advanced computer vision techniques and image information fusion from multiple mammograms to improve lesion detection and characterization. When fully developed, the CAD system can assist radiologists in mammographic interpretation. During this project year, we have performed the following tasks: (1) collected databases of digital mammograms (DMs) and digitized film mammograms (DFMs) for development of the CAD systems, (2) conducted a study to compare the percent dense area manually segmented by experienced radiologists on DMs and DFMs, (3) developed new image enhancement techniques and new false-positive reduction methods for mass detection, and conducted studies to compare the accuracy of mass detection by the CAD systems for DMs and DFMs using FROC analysis, (4) developed automated method for nipple detection on mammograms as a basis of multiple image fusion analysis for CAD systems, and (5) compared the accuracy for classification of malignant and benign breast masses using single-view and fused two-view information on mammograms by computer, and evaluated the effects of CAD on experienced radiologists' characterization of malignant and benign breast masses in two-view temporal pairs of mammograms. In summary, we have investigated a number of areas in CAD of mammographic lesions and evaluated the new techniques for both DMs and DFMs. We have made progress in the six tasks proposed in the project. We have found that our new computer-vision techniques and two-view information fusion approach can improve the performance of the CAD systems. We will continue the development of the CAD systems for%Ms and DFMs in the coming years.
Digital Mammography Development Of An Advanced Computer Aided Diagnosis System For Breast Cancer Detection
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Author :
language : en
Publisher:
Release Date : 2005
Digital Mammography Development Of An Advanced Computer Aided Diagnosis System For Breast Cancer Detection written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
The goal of the project is to develop computer-aided diagnosis (CAD) methods and systems for mammography using advanced computer vision techniques and image information fusion from multiple mammograms to improve lesion detection and characterization. When tally developed, the CAD system can assist radiologists in mammographic interpretation. During this project year, we have performed the following tasks: (1) collected databases of digital mammograms (DMs) and digitized film mammograms (DFMs) for development of the CAD systems, (2) developed computer vision techniques and a prototype CAD system for detection of microcalcifications on DMs, (3) developed computer vision techniques and a prototype CAD system for detection of masses on DFMs and DMs, (4) explored the feasibility of improving mass detection by CAD on digital breast tomosynthesis mammograms, (5) developed automated pectoral muscle detection method for preprocessing of MLO view mammograms for multiple image analysis, (6) developed a prototype CAD method for classification of malignant and benign masses by fusion of information from mammograms and ultrasound images and investigated the effects of the multi-modality CAD system on radiologists performance. In summary, we have investigated a number of areas in CAD of mammographic lesions and evaluated the new techniques for both DMs and DFMs. We have made progress in the six tasks proposed in the project. We have found that our new computer-vision techniques and two-view information fusion approach can improve the performance of the CAD systems. We will continue the development of the CAD systems for DMs and DFMs in the coming years.
Fractal Analysis Of Breast Masses In Mammograms
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Author : Thanh Cabral
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Fractal Analysis Of Breast Masses In Mammograms written by Thanh Cabral and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Technology & Engineering categories.
Fractal analysis is useful in digital image processing for the characterization of shape roughness and gray-scale texture or complexity. Breast masses present shape and gray-scale characteristics in mammograms that vary between benign masses and malignant tumors. This book demonstrates the use of fractal analysis to classify breast masses as benign masses or malignant tumors based on the irregularity exhibited in their contours and the gray-scale variability exhibited in their mammographic images. A few different approaches are described to estimate the fractal dimension (FD) of the contour of a mass, including the ruler method, box-counting method, and the power spectral analysis (PSA) method. Procedures are also described for the estimation of the FD of the gray-scale image of a mass using the blanket method and the PSA method. To facilitate comparative analysis of FD as a feature for pattern classification of breast masses, several other shape features and texture measures are described in the book. The shape features described include compactness, spiculation index, fractional concavity, and Fourier factor. The texture measures described are statistical measures derived from the gray-level cooccurrence matrix of the given image. Texture measures reveal properties about the spatial distribution of the gray levels in the given image; therefore, the performance of texture measures may be dependent on the resolution of the image. For this reason, an analysis of the effect of spatial resolution or pixel size on texture measures in the classification of breast masses is presented in the book. The results demonstrated in the book indicate that fractal analysis is more suitable for characterization of the shape than the gray-level variations of breast masses, with area under the receiver operating characteristics of up to 0.93 with a dataset of 111 mammographic images of masses. The methods and results presented in the book are useful for computer-aided diagnosis of breast cancer. Table of Contents: Computer-Aided Diagnosis of Breast Cancer / Detection and Analysis of\newline Breast Masses / Datasets of Images of Breast Masses / Methods for Fractal Analysis / Pattern Classification / Results of Classification of Breast Masses / Concluding Remarks
Demonstration Project On Mammographic Computer Aided Diagnosis For Breast Cancer Detection
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Author :
language : en
Publisher:
Release Date : 1999
Demonstration Project On Mammographic Computer Aided Diagnosis For Breast Cancer Detection written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with categories.
The goal of this project is to demonstrate the clinical usefulness of computer-aided diagnosis (CAD) in mammographic detection of breast cancer. Our plan is to develop advanced CAD schemes for detection and characterization of clustered microcalcifications and masses by incorporating artificial neural networks and various image processing techniques. Clinical mammography workstations for automated detection of suspicious lesions in mammograms will be developed by integration of laser digitizer, high-speed computer and advanced CAD software. The prototype workstations will be used as a "second opinion" in interpreting mammograms by reducing observational errors. The outcomes of radiologists' image readings in the detection of breast cancer will be evaluated by examining radiologists' performance when reading films only and when reading film with the computer results. We believe that the outcomes of this demonstration project will lead to large-scale clinical trials and will result in commercial products for practical routine use in breast imaging.
Digital Mammography
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Author : Nico Karssemeijer
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Digital Mammography written by Nico Karssemeijer and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Medical categories.
In June 1998 the Fourth International Workshop on Digital Mammography was held in Nijmegen, The Netherlands, where it was hosted by the department of Radiology of the University Hospital Nijmegen. This series of meetings was initiated at the 1993 SPIE Biomedical Image Processing Conference in San Jose, USA, where a number of sessions were entirely devoted to mammographic image analysis. At very successful subsequent workshops held in York, UK (1994) and Chicago, USA (1996), the scope of the conference was broadened, establishing a platform for presentation and discussion of new developments in digital mammog raphy. Topics that are addressed at these meetings are computer-aided diagnosis, image processing, detector development, system design, observer performance and clinical evaluation. The goal is to bring researchers from universities, breast cancer experts, and engineers together, to exchange information and present new scientific developments in this rapidly evolving field. This book contains all the scientific papers and posters presented at the work shop in Nijmegen. Contributions came from as many as 20 different countries and 190 participants attended the meeting. At a technical exhibit companies demon strated new products and work in progress. Abstracts of all papers were reviewed by members of the scientific committee. Many of the accepted papers had excellent quality, but due to limited space not all of them could be included as full papers in these proceedings. Papers that were rated high by the reviewers are included as long or short papers, others appear as extended abstracts in the last chapter.