Computerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer / by Paola Casti, Arianna Mencattini, Marcello Salmeri, Rangaraj M. Rangayyan.

Format:

Book

Author/Creator:

Casti, Paola., Author.

Mencattini, Arianna., Author.

Salmeri, Marcello., Author.

Rangayyan, Rangaraj M., Author.

Series:

Synthesis Lectures on Biomedical Engineering, 1930-0336

Language:

English

Subjects (All):

Engineering.

Biophysics.

Biomedical engineering.

Technology and Engineering.

Biomedical Engineering and Bioengineering.

Local Subjects:

Technology and Engineering.

Biophysics.

Biomedical Engineering and Bioengineering.

Physical Description:

1 online resource (XX, 166 p.)

Edition:

1st ed. 2017.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2017.

System Details:

Mode of access: World Wide Web.

Summary:

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.

Contents:

Preface

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.

Notes:

Includes bibliographical references and index.

ISBN:

9783031016646

3031016645