Druck auf Anfrage Neuware - Printed after ordering - 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.

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T�tulo: Computer-Aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer
Editor: Springer International Publishing
Fecha de publicaci�n: 2013
Idioma: Ingl�s
ISBN 10: 3031005287
ISBN 13: 9783031005282
Encuadernaci�n: Taschenbuch
Estado: Neu
Dimensiones: 235�mil�metros de ancho por 191 de alto por 11 de largo
Peso: 377 gramos

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Sinopsis

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

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Shantanu Banik received his Ph.D. in 2011 and M.Sc. in 2008 from the Department of Elec trical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada, and his B.Sc. in 2005 in Electrical and Electronic Engineering from the Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh. His Ph.D. thesis was on the problem of detection of architectural distortion in prior mammograms to aid the process of early detection of breast cancer. His research interests include medical signal and image processing and analysis, development of computer-aided diagnosis (CAD) techniques for the detection of cancer, landmarking and segmen tation of medical images, pattern recognition and classification, medical imaging, and automatic segmentation and analysis of tumors. He has coauthored several journal papers, a number of con ference papers, three book chapters, and a book titled Landmarking and Segmentation of 3D CT Images (Morgan & Claypool, 2009). He is currently writing two more books on image processing and biomedical applications. He received many awards and scholarships as a graduate student at the University of Calgary, including the Institute of Cancer Research (ICR), Canada Publication Prize for significant contribution on cancer research; Natural Sciences and Engineering Research Council (NSERC) of Canada and Collaborative Research and Training Experience (CREATE) postdoctoral fellowship; J. B. Hyne Research Innovation Award for outstanding research activity at the University of Calgary; Robert B. Paugh Memorial Award; Graduate Student Productivity Award; the Queen Elizabeth II Graduate (Doctoral) Scholarship; the Graduate Faculty Council Scholarship (Doc toral); the University Technologies International Inc. (UTI) Fellowship; the University of Calgary Alumni Association Graduate Scholarship; and the Schulich School of Engineering Teaching As sistant Excellence Award. He is currently working as a Research and Developement Engineer at theCircle Cardiovascular Imaging, Calgary, Alberta, Canada Rangaraj Mandayam Rangayyan is a Professor with the Department of Electrical and Computer Engineering, and an Adjunct Professor of Surgery and Radiology, at the University of Calgary, Calgary, Alberta, Canada. He received a Bachelor of Engineering degree in Electronics and Com munication in 1976 from the University of Mysore at the People’s Education Society College of Engineering, Mandya, Karnataka, India, and a Ph.D. in Electrical Engineering from the Indian Institute of Science, Bangalore, Karnataka, India, in 1980. His research interests are in the areas of digital signal and image processing, biomedical signal analysis, biomedical image analysis, and computer-aided diagnosis. He has published more than 150 papers in journals and 250 papers in proceedings of conferences. His research productivity was recognized with the 1997 and 2001 Re search Excellence Awards of the Department of Electrical and Computer Engineering, the 1997 Research Award of the Faculty of Engineering, and by appointment as a “University Professor” in 2003, at the University of Calgary. He is the author of two textbooks: Biomedical Signal Analysis (IEEE/ Wiley, 2002) and Biomedical Image Analysis (CRC, 2005). He has coauthored and coedited several other books, including Color Image Processing with Biomedical Applications (SPIE, 2011). He was recognized by the IEEE with the award of the Third Millennium Medal in 2000, and was elected as a Fellow of the IEEE in 2001, Fellow of the Engineering Institute of Canada in 2002, Fellow of the American Institute for Medical and Biological Engineering in 2003, Fellow of SPIE: the International Society for Optical Engineering in 2003, Fellow of the Society for Imaging Infor matics in Medicine in 2007, Fellow of the Canadian Medical and Biological Engineering Society in 2007, and Fellow of the Canadian Academy of Engineering in 2009. He has been awarded the Killam Resident Fellowship thrice (1998, 2002, and 2007) in support of his book-writing projects.

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