000 04436nam a22005415i 4500
001 978-3-031-01664-6
003 DE-He213
005 20240730164902.0
007 cr nn 008mamaa
008 220601s2017 sz | s |||| 0|eng d
020 _a9783031016646
_9978-3-031-01664-6
024 7 _a10.1007/978-3-031-01664-6
_2doi
050 4 _aT1-995
072 7 _aTBC
_2bicssc
072 7 _aTEC000000
_2bisacsh
072 7 _aTBC
_2thema
082 0 4 _a620
_223
100 1 _aMencattini, Arianna.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_986523
245 1 0 _aComputerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer
_h[electronic resource] /
_cby Arianna Mencattini, Paola Casti, Marcello Salmeri, Rangaraj M. Rangayyan.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXX, 166 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 _aPreface -- Acknowledgments -- Introduction -- Experimental Setup and Databases of Mammograms -- Multidirectional Gabor Filtering -- Landmarking Algorithms -- Computer-aided Detection of Bilateral Asymmetry -- Design of Contour-independent Features for Classification of Masses -- Integrated CADe/CADx of Mammographic Lesions -- Concluding Remarks -- References -- Authors' Biographies.
520 _aThe 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.
650 0 _aEngineering.
_99405
650 0 _aBiophysics.
_94093
650 0 _aBiomedical engineering.
_93292
650 1 4 _aTechnology and Engineering.
_986526
650 2 4 _aBiophysics.
_94093
650 2 4 _aBiomedical Engineering and Bioengineering.
_931842
700 1 _aCasti, Paola.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_986528
700 1 _aSalmeri, Marcello.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_986530
700 1 _aRangayyan, Rangaraj M.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_986531
710 2 _aSpringerLink (Online service)
_986534
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031005367
776 0 8 _iPrinted edition:
_z9783031027925
830 0 _aSynthesis Lectures on Biomedical Engineering,
_x1930-0336
_986535
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01664-6
912 _aZDB-2-SXSC
942 _cEBK
999 _c85970
_d85970