A novel region growing segmentation algorithm for the detection of breast cancer

As medical images are mostly fuzzy in nature, segmenting regions based intensity is the most challenging task. Segmentation of medical images using seeded region growing technique is increasingly becoming a popular method because of its ability to involve high-level knowledge of anatomical structures in seed selection process. In this paper, we have made improvements in region growing image segmentation for mammogram images to detect the breast cancer. Selective median filter is used for preprocessing, CLAHE (Contrast Limited Adaptive Histogram Equalization) method is used for the enhancement, Harris corner detect theory is used to auto find growing seeds and the seeded region growing rule for the development of regions. This work also includes a new uncertainty theory-Cloud Model to realize automatic and adaptive segmentation threshold selecting, which considers the uncertainty of image and extracts concepts from characteristics of the region to be segmented like human being. We found this method works reliable on homogeneity and region characteristics. Furthermore, the method has been tested for over 40 sample images and the results found were good.