Breast Cancer Diagnosis System Based on Wavelet Analysis and Neural Networks

Breast Cancer Diagnosis System Based on Wavelet Analysis and Neural Networks

K. Taifi (Information Processing and Telecommunication Teams, Faculty of Science and Technology, Sultan Moulay Slimane University, Beni-Mellal, Morocco), S. Safi (Information Processing And Telecommunication Teams, Polydisciplinary Faculty of Science and Technology, Sultan Moulay Slimane University, Beni-Mellal, Morocco), M. Fakir (Information Processing and Telecommunication Teams, Faculty of Science and Technology, Sultan Moulay Slimane University, Beni-Mellal, Morocco) and A. Elbalaoui (Information Processing and Telecommunication Teams, Faculty Of Science and Technology, Sultan Moulay Slimane University, Beni-Mellal, Morocco)
Copyright: © 2014 |Pages: 16
DOI: 10.4018/ijcvip.2014010101
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Abstract

The high incidence of breast cancer has increased significantly in the recent years. The most familiar breast tumors types are mass and microcalcifications (Mcs). Mammogram is considered the most reliable method in early detection of breast cancer. Computer-aided diagnosis system can be very helpful for radiologist in detection and diagnosing abnormalities earlier and faster than traditional screening programs. Several techniques can be used to accomplish this task. In this work, the authors present a preprocessing method, based on homomorphic filtering and wavelet, to extract the abnormal Mcs in mammographic images. The authors use four different methods of feature extraction for classification of normal and abnormal patterns in mammogram. Four different feature extraction methods are used here are Wavelet, Gist, Gabor and Tamura. A classification system based on neural network and nearest neighbor classification is used.
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Approach Description

  • 1.

    Problematic: Mammographic images show a contrast between the two main constituents of the breast fatty tissue and connective-fibrous matrix. In general, it is extremely difficult to define normality of mammographic images: Indeed, the appearance of the mammary gland is extremely variable depending on the patient’s age and the period during which the mammogram is done.

  • 2.

    Algorithm: The mammography images segmentation systems illustrated in Figure1.

Figure 1.

Segmentation system of mammographic images based on wavelet transforms

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