Improved Wavelet Based Thresholding for Contrast Enhancement of Digital Mammograms

Data sets collected by image sensors are generally contaminated by noise. This assures the need for imaging enhancement to aid interpretation. This paper introduces an efficient enhancement algorithm of digital mammograms based on wavelet analysis and mathematical morphology. In this proposed method, we adopt mathematical morphology and wavelet-based-level dependent thresholding algorithm to increase the contrast in mammograms to ease extraction of suspicious regions known as regions of interest (ROIs). Experimental results show that the proposed algorithm yields significantly superior image quality and better Contrast Improvement Index (CII). Here, to prove the efficiency of this method, we have compared this with various well-known algorithms like VisuShrink and BayesShrink.