Soft Computing Based Decision Making Approach for Tumor Mass Identification in Mammogram

An intelligent computer aided diagnosis system can be very helpful for radiologist in detecting anddiagnosing breast cancer faster than typical screening program. This study attempted to segment themasses accurately and distinguish malignant from benign masses. The suspicious location of the breastmasses are specified by the radiologists and then masses are accurately segmented using fuzzy c-meansclustering technique. Fourier descriptors are utilized for the extraction of shape features of mammographicmasses. These shape features along with the texture features are fed to the input of the ANFIS classifier fordetermination of the masses as benign, lobular or malignant. The classification system utilizes a simpleEuclidian distance metric to determine the degree of malignancy. The study involves 40 digitizedmammograms from MIAS, BIRADS database and has to be found 87% correct classification rate.