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007			cr nn 008mamaa
008			230422s2013 sz | s |||| 0|eng d
020			_a9783031016561
024	7		_a10.1007/978-3-031-01656-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRG493.5.R33 _b2013 EB
100	1		_aBanik, Shantanu _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686693
245	1	0	_aComputer-Aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer _cby Shantanu Banik, Rangaraj Rangayyan, J.E. Leo Desautels
250			_a1st edition 2013
264		1	_aCham _bSpringer International Publishing _c2013
300			_a1 recurso en línea (XX, 176 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Biomedical Engineering _x1930-0336
505	0		_aIntroduction -- 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.
520			_aArchitectural 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.
988			_aSynthesis Collection of Technology_2013
650		7	_2embne _9182522 _aMamas _xCáncer
650		7	_2embne _9147244 _aMamografía
700	1		_aRangayyan, Rangaraj M. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686111
700	1		_aDesautels, J. E. Leo _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688213
776	0	8	_iPrinted edition: _z9783031005282
776	0	8	_iPrinted edition: _z9783031027840
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01656-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _eIG _zSI