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008 220601s2013 sz | s |||| 0|eng d
020 _a9783031016561
_9978-3-031-01656-1
024 7 _a10.1007/978-3-031-01656-1
_2doi
050 4 _aT1-995
072 7 _aTBC
_2bicssc
072 7 _aTEC000000
_2bisacsh
072 7 _aTBC
_2thema
082 0 4 _a620
_223
100 1 _aBanik, Shantanu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984136
245 1 0 _aComputer-Aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer
_h[electronic resource] /
_cby Shantanu Banik, Rangaraj Rangayyan, J.E. Leo Desautels.
250 _a1st ed. 2013.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2013.
300 _aXX, 176 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aEngineering.
_99405
650 0 _aBiophysics.
_94093
650 0 _aBiomedical engineering.
_93292
650 1 4 _aTechnology and Engineering.
_984137
650 2 4 _aBiophysics.
_94093
650 2 4 _aBiomedical Engineering and Bioengineering.
_931842
700 1 _aRangayyan, Rangaraj.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984139
700 1 _aDesautels, J.E. Leo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984140
710 2 _aSpringerLink (Online service)
_984142
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031005282
776 0 8 _iPrinted edition:
_z9783031027840
830 0 _aSynthesis Lectures on Biomedical Engineering,
_x1930-0336
_984143
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01656-1
912 _aZDB-2-SXSC
942 _cEBK
999 _c85622
_d85622