Mammogram Image Segmentation Using Auto Adaptive Fuzzy Index Measure

Breast Cancer involves the uncontrolled growth of abnormal cells that have mutated from normal tissues. A radiologist looks for certain signs and characteristics indicative of cancer when evaluating a mammogram. The main task is to obtain the locations of suspicious regions to assist radiologists in diagnosis. Image segmentation has been approached from a wide variety of perspectives: region-based approach, morphological operation, multi-scale analysis, fuzzy approaches and stochastic approaches have been used for mammogram image segmentation but with some limitations. In spite of the several methods available in the literature, image segmentation still a challenging problem in most of image processing applications. The challenge comes from the fuzziness of image objects and the overlapping of the different regions. In this paper we propose fast auto adaptive image segmentation algorithm for finding the optimal thresholds for segmenting gray scale images. The proposed method is based on fuzzy index which decreases the similarity between pixels increases. The system uses initial estimation of the parameters. The fuzzy subsets derived from the image histogram using weighted fuzzy entropywill shows the similar cost measure as in pixels of the same subset. Experimental results demonstrate the effectiveness of the proposed approach.