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Image Enhancement for Detection of Diagnostic Signs in Mammograms using Bi-Orthogonal Wavelets

Mammography is the primary method for early detection of abnormalities in the breast. However the diagnostic signs of breast diseases are difficult to detect because of the low-contrast and noisy nature of mammograms. This ensures the need for image enhancement to aid radiologists in interpretation. This paper presents an approach for enhancement of low contrast features in breast mammograms for accurate detection of diagnostic signs like mass and microcalcifications. Contrast of mammograms is enhanced by the use of mathematical morphology. Biorthogonal wavelet thresholding is applied for denoising. Identification of the suspicious region called the Region of interest (ROI) is performed based on homogenous pixel grouping and color quantization .The algorithm has been tested on a large number of mammograms from the database, Mammographic Image Analysis Society (MIAS). The performance of proposed algorithm is compared with other well-known algorithms like Visu shrink [19] and Bayes shrink, based on quantitative metrics and clinical evaluation. Our proposed algorithm seems to meaningfully improve the identification of ROI in the mammograms.