Breast cancer is the most common type of cancer found in women worldwide; approximately 10% of women are confronted with breast cancer in their lives. Breast cancer can be most efficiently treated if detected at an early stage. This book focuses primarily on the application of computer vision for early lesion identification in mammograms and breast-imaging volumes through computer-aided diagnostics (CAD). Color illustrations are included in the text, and an accompanying CD-ROM contains other full-color images. 
The book is divided into four parts. In Part I, the anatomic, histopathologic, and mammographic views of the breast are examined, and the physics for different breast-imaging modalities are presented. Part II presents techniques for lesion and mass detection, and computer-aided diagnosis. Part III discusses the applications of different computer-vision fields in breast-image registration, and performance evaluation for breast CAD techniques is discussed in Part IV.
 Contents 
 - Preface 
 - The Subgross Morphology of Normal and Pathologically Altered Breast Tissue 
 - Fundamentals of Achieving Functional and Anatomical Imaging with Breast MRI: Physical and Clinical Concepts 
 - An Indexed Atlas of Digital Mammograms for Computer-Aided Diagnosis of Breast Cancer 
 - Genetic Algorithms in CAD Mammography 
 - Mammographic Mass Detection by Robust Learning Algorithms 
 - Analysis of Bilateral Asymmetry in Mammograms via Directional Filtering with Gabor Wavelets 
 - Support Vector Machines in CAD Mammography 
 - A Weighted Gaussian Mixture Model with Markov Random Fields and Adaptive Expert Combination Strategy for Segmenting Masses in Mammograms 
 - Detection of Microcalcifications in Mammograms 
 - Using Computational Intelligence for Computer-Aided Diagnosis of Screen Film Mammograms 
 - New Approach for Breast Skin-Line Estimation in Mammograms 
 - Computerized Mass Detection for Digital Breast Tomosynthesis 
 - Breast Imaging Registration: A Review 
 - A Mammographic Registration Framework Based on Anatomical Linear Structures 
 - AMDI - Indexed Atlas of Digital Mammograms that Integrates Case Studies, E-Learning, and Research Systems via the Web 
 - Detection, Characterization, and Visualization of Breast Cancer Using 3D Ultrasound Images 
 - Computer-Aided Diagnosis for 2D/3D Breast Ultrasound 
 - Automatic Pectoral Muscle Segmentation on Mediolateral Oblique View Mammograms 
 - Performance Evaluation of CADe Algorithms in Mammography 
 - A Simple Method for Automatically Locating the Nipple on Mammograms 
 - Algorithms for Sementing Small Low-Contrast Objects in Images 
 - Computer-Aided Diagnosis of Breast Cancer on MR Imaging 
 - Review on Real-Time Magnetic Resonance Gad-Enhanced Breast Lesion Characterization 
 - Breast MRI Computer-Aided Diagnosis Systems 
 - Automatic Assessment of Mammogram Adequacy and Quality 
 - Probability Modeling of CAD Systems for Mammography 
 - Computer-Aided Diagnosis in Breast Imaging: Where Do We Go after Detection? 
 - The Current Status and Likely Future of Breast Imaging CAD 
 - Index

Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer (SPIE Press Monograph Vol. PM155)

Health, Fitness & Dieting

Rangaraj M., Rangayyan

Jasjit S. Suri

Diseases & Physical Ailments

SPIE Publications

This full-color book begins with a detailed study of the nature of color images-including natural, multispectral, and pseudocolor images-and covers acquisition, quality control, and display of color images, as well as issues of noise and artifacts in color images and segmentation for the detection of regions of interest or objects. The book is primarily written with the (post-)graduate student in mind, but practicing engineers, researchers, computer scientists, information technologists, medical physicists, and data-processing specialists will also benefit from its depth of information. Those working in diverse areas such as DIP, computer vision, pattern recognition, telecommunications, seismic and geophysical applications, biomedical applications, hospital information systems, remote sensing, mapping, and geomatics may find this book useful in their quest to learn advanced techniques for the analysis of color or multichannel images.

Color Image Processing with Biomedical Applications

Science & Math

Begoña Acha

Carmen Serrano

Biological Sciences

SPIE--The International Society for Optical Engineering

This book presents the established and currently researched diagnostic and therapeutic imaging techniques used for breast cancer. Section I is a review of the principles, applications, and recent advances of breast imaging modalities. Section II focuses on breast pathologies and presents the use of breast cancer subgross morphology parameters. Section III covers mammography. Section IV focuses on CAD techniques used in breast cancer detection. Section V is dedicated to breast ultrasound, and Section VI discusses the role of magnetic resonance imaging in breast imaging. Thermal imaging in breast cancer is the theme of Section VII, and Section VIII concludes with chapters on breast cancer treatment.

Diagnostic and Therapeutic Applications of Breast Imaging

S. Vinitha Sree

Kwan-Hoong Ng

Physics

Breast cancer is an abnormal growth of cells in the breast, usually in the inner lining of the milk ducts or lobules. It is currently the most common type of cancer in women in developed and developing countries. The number of women affected by breast cancer is gradually increasing and remains as a significant health concern. Researchers are continuously working to develop novel techniques to detect early stages of breast cancer. This book covers breast cancer detection, diagnosis, and treatment using different imaging modalities such as mammography, magnetic resonance imaging, computed tomography, positron emission tomography, ultrasonography, infrared imaging, and other modalities. The information and methodologies presented will be useful to researchers, doctors, teachers, and students in biomedical sciences, medical imaging, and engineering.

Multimodality Breast Imaging: Diagnosis and Treatment

E. Y. K. Ng

U. Rajendra Acharya

Biomedical Signal Analysis

Wiley-IEEE Press

With the development of rapidly increasing medical imaging modalities and their applications, the need for computers and computing in image generation, processing, visualization, archival, transmission, modeling, and analysis has grown substantially. Computers are being integrated into almost every medical imaging system. Medical Image Analysis and Informatics demonstrates how quantitative analysis becomes possible by the application of computational procedures to medical images. Furthermore, it shows how quantitative and objective analysis facilitated by medical image informatics, CBIR, and CAD could lead to improved diagnosis by physicians. Whereas CAD has become a part of the clinical workflow in the detection of breast cancer with mammograms, it is not yet established in other applications. CBIR is an alternative and complementary approach for image retrieval based on measures derived from images, which could also facilitate CAD. This book shows how digital image processing techniques can assist in quantitative analysis of medical images, how pattern recognition and classification techniques can facilitate CAD, and how CAD systems can assist in achieving efficient diagnosis, in designing optimal treatment protocols, in analyzing the effects of or response to treatment, and in clinical management of various conditions. The book affirms that medical imaging, medical image analysis, medical image informatics, CBIR, and CAD are proven as well as essential techniques for health care.

Medical Image Analysis and Informatics: Computer-Aided Diagnosis and Therapy

Paulo Mazzoncini, de Azevedo-Marques

Arianna, Mencattini

Marcello, Salmeri

CRC-Press

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 (Synthesis Lectures on Biomedical Engineering)

Computers & Technology

Computer Science

Morgan & Claypool Publishers

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