Detection of Microcalcification in Digital Mammograms by Improved-MMGW Segmentation Algorithm

Breast cancer represents the most frequently diagnosed cancer in women. In order to reduce mortality, early detection of breast cancer is important, because diagnosis is more likely to be successful in the early stages of the disease. This paper presents an improved multi-scale morphological gradient watershed segmentation method for automatic detection of clustered microcalcification in digitized mammograms. We use adaptive median filter for preprocessing and incorporated corrections after watershed segmentation by cloned data. This correction has led to better detection and localization of microcalcifications. By comparing our results with original multiscale morphological watershed segmentation method, we proved that the proposed technique is better and performance is improved by approximately 20%. The true positive rate and false positive rate are used to evaluate the performance of the proposed technique. For experimental purpose the dataset from Mammographic Image Analysis Society database and few data collected from local diagnostic center. The result shows achievement of true positive rate of about 95.3% at the rate of 0.14 false positive per image.