Classification of Normal, Benign and Malignant Tissues Using Co-occurrence Matrix and Bayesian Neural Network in Mammographic Images
Leonardo O. Martins, Alcione Miranda dos Santos, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva · 2006
This work analyzes the application of the co-occurrence matrix to the characterization of breast tissue as normal, benign or malignant in mammographic images. The method characterization is based on a process that selects, using forward selection technique, from all computed measures which best discriminate among normal, benign and malignant tissues. Then, a Bayesian neural network is used to evaluate the ability of these features to predict the classification for each tissue sample. To verify this application we also describe tests that were carried out using a set of 218 tissues samples, 68 benign and 51 malignant and 99 normals. The result analysis has given an accuracy of 86.84%, which means encouraging results. The preliminary results of this approach are very promising in characterizing breast tissue.