Multimodality Breast Imaging: Diagnosis and Treatment

Multimodality Breast Imaging: Diagnosis and Treatment
Author(s):    E. Y. K. Ng; U. Rajendra Acharya; Rangaraj M. Rangayyan; Jasjit S. Suri
Published:   2013
DOI:             10.1117/3.1000499
eISBN: 9780819492951  |  Print ISBN13: 9780819492944
Description:

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.

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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. Hence, the early detection of breast cancer can improve the survival rate and quality of life. Therefore, today, newer modalities are available to more accurately detect breast cancer. Researchers are continuously working to develop novel techniques to detect early stages of breast cancer. This book covers breast cancer detection using different imaging modalities such as mammography, magnetic resonance imaging, computed tomography, positron emission tomography, ultrasonography, infrared imaging, and other modalities.

Architectural distortion is one of the major causes of false-negative findings in the detection of early stages of breast cancer. This book presents methods for computer-aided detection of architectural distortion in mammograms acquired prior to the diagnosis of breast cancer in the interval between scheduled screening sessions. The results are promising and indicate that the proposed methods can detect architectural distortion in prior mammograms taken 15 months (on average) before clinical diagnosis of breast cancer, with a sensitivity of 0.8 at 5.2 false positives per patient.

A computer-aided system for the automated detection of normal, benign, and cancerous breasts using texture features extracted from digitized mammograms and data mining techniques is proposed in Chapter 2. The novelty of this work is to automatically classify the mammogram into normal, benign, and malignant classes using the texture features alone, with an efficiency of 93.3% and sensitivity of 92.3% using a fuzzy classifier.

Breast cancer diagnosis by combination of fuzzy systems and an ant colony optimization algorithm is proposed. Results on the breast cancer diagnosis dataset from the University of California Irvine machine learning repository show that the proposed FUZZY-ACO would be capable of classifying cancer instances with a high accuracy rate and adequate interpretability of extracted rules.

© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)

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