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A Survey on Texture Analysis of Mammogram for the Detection of Breast Cancer
Abstract
Breast cancer is the leading cause of death of women in United States. Modern mammography is the only technique that has demonstrated the ability to detect breast cancer at an early stage and with high sensitivity and specificity. The search for features in this kind of image is complicated by the higher-frequency textural variations in image intensity. The interpretation of mammograms is a skilled and difficult task. But the high rate of false positives in mammography causes a large number of unnecessary biopsies. A characteristic feature of the mammograms is their textured appearance. With this texture extraction the number of false positives can be reduced. The aim of this paper is to review on existing approaches to the texture extraction in the detection of breast cancer. Existing texture analysis algorithms are carefully studied and classified into three categories: texture analysis in the detection of masses, micro calcification, and also in tissue surrounding the region. Different methods of texture extractions can also be done in each category. The identification of glandular tissues in breast X-rays is another important task in assessing left and right breasts images. The appearance of glandular tissue in mammograms is highly variable, ranging from sparse streaks to dense blobs. Fatty regions are generally smooth and dark. Texture analysis provides a flexible approach to discriminating between glandular and fatty regions. Therefore the importance of texture analysis is presented first in this paper. Each approach is reviewed according to its classification, and its merits and drawbacks are outlined. The reviewed results show that many approaches greatly improve the false positive and false negative reduction rates.
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References
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