An efficient technique for the extraction of microcalcification's severity features

Abstract

Microcalcifications are very tiny deposits of calcium allocated in the breast tissue. Their gray level is similar to the dense normal breast tissue so its very difficult to differentiate between them. Once detected, its very difficult to between malign end benign microcalcifications. In this paper, we apply a new method to extract features of microcalcifications in order to classify them into malign and benign. This technique, called the Discriminative Completed Local Binary Pattern (DisCLBP), extracts texture characteristics of breast tissue in order to characterize the severity of microcalcifications. Classification of these structures is accomplished through Artificial Neural Network (ANN), which separate them in two groups: malignant and benign microcalcifications. Performance results are given in terms of receiver operating characteristic (ROC). The area under curve (AUC) of the corresponding approach has been found to be 93.45%.