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008			220601s2017 sz | s |||| 0|eng d
020			_a9783031016646
024	7		_a10.1007/978-3-031-01664-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aRG493.5.R33 _b2017 EB
100	1		_aCasti, Paola _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686773
245	1	0	_aComputerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer _cby Paola Casti, Arianna Mencattini, Marcello Salmeri, Rangaraj M. Rangayyan
250			_a1st edition 2017
264		1	_aCham _bSpringer International Publishing _c2017
300			_a1 recurso en línea (XX, 166 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		_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.
988			_aSynthesis Collection of Technology_2017
650		7	_2embne _9147244 _aMamografía
650		7	_2embne _9423753 _aMamas _xCáncer _xDiagnóstico
650		7	_2embne _9671282 _aRadiografía digital
700	1		_aMencattini, Arianna _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686774
700	1		_aSalmeri, Marcello _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686775
700	1		_aRangayyan, Rangaraj M. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686111
776	0	8	_iPrinted edition: _z9783031005367
776	0	8	_iPrinted edition: _z9783031027925
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01664-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI