Breast Image Segmentation for evaluation of Cancer Disease
Thanh-Tam Nguyen, Thanh-Hai Nguyen, Ba-Viet Ngo, Duc-Dung Vo · 2020
Breast cancer is one of dangerous diseases and difficult to cure. It is observed that early detection of malignancy can help in the diagnosis of the disease and patient can be saved. For the detection of breast cancer, breast images will be enhanced using a Fuzzy logic and possibility distribution algorithm and then segmented to produce images with region of interest, in which just cancer shape appears in the image for detecting and estimating disease status. This paper proposes a statistic method based on the gray level of pixels in the image through histograms of two breast image sets to classify two cases of cancer and normal one. Simulation results on breast image sets will show that the proposed method is effective and it can be developed for detection of benign and malignant tumors in artificial intelligent systems.