Classification of mammogram masses using selected texture, shape and margin features with multilayer perceptron classifier

Computer-aided detection (CAD) assists radiologists by providing the second opinion in the mammography detection and reduces misdiagnosis. In this work the task of automatically classifying the mass tissue into benign and malign based on the characteristics of mass is investigated. Mass is characterized by its shape, margin, density, size and age of the patient. Geometrical shape, margin and texture features are used in this work to classify the masses. These features are found to be effective in discriminating benign mass from the malign mass. For the purpose of classification, the masses are segmented from the mammogram using gray level thresholding and features are extracted. Then the features are fuzzified using fuzzy membership values. Finally, the classification is performed using different classifiers and their performances are compared. Mammographic Image Analysis Society (MIAS) Database was used for experimental study. The experiments were implemented in MATLAB and WEKA.