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Computerized analysis of mammographic images for detection and characterization of breast cancer /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	618.1907572
書名/作者:	Computerized analysis of mammographic images for detection and characterization of breast cancer // Paola Casti ... [et al.].
其他作者:	Casti, Paola.
出版者:	[San Rafael, Calif.] : : Morgan & Claypool,, c2017.
面頁冊數:	xx, 166 p. : : ill., ports. ;; 24 cm.
標題:	Breast - Radiography.
標題:	Radiography, Medical - Digital techniques.
標題:	Radiography, Medical - Mathematics.
標題:	Image processing - Digital techniques.
標題:	Image processing - Mathematics.
標題:	Pattern recognition systems.
標題:	Breast - Cancer
標題:	Breast Neoplasms - diet therapy.
標題:	Mammography.
標題:	Image Interpretation, Computer-Assisted.
標題:	Diagnosis, Computer-Assisted - methods.
ISBN:	9781681731568 (pbk.) :
書目註:	Includes bibliographical references (p. 147-162).
內容註:	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.
摘要、提要註:	The 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.

Computerized analysis of mammographic images for detection and characterization of breast cancer /
Computerized analysis of mammographic images for detection and characterization of breast cancer /Paola Casti ... [et al.]. - [San Rafael, Calif.] :Morgan & Claypool,c2017. - xx, 166 p. :ill., ports. ;24 cm. - Synthesis lectures on biomedical engineering,#561930-0328 ;.

Includes bibliographical references (p. 147-162).

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.

The 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.

ISBN: 9781681731568 (pbk.) :NTD 1,828

Nat. Bib. Agency Control No.: 101711699DNLMSubjects--Topical Terms:

401799
Breast
--Radiography.

LC Class. No.: RG493.5.R33 / C37 2017

Dewey Class. No.: 618.1907572

Computerized analysis of mammographic images for detection and characterization of breast cancer /
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