Detection of Cancerous Masses in Mammograms by Template Matching: Optimization of Template Brightness Distribution by Means of Evolutionary Algorithm

Abstract

Optimization of brightness distribution in the template used for detection of cancerous masses in mammograms by means of correlation coefficient is presented. This optimization is performed by the evolutionary algorithm using an auxiliary mass classifier. Brightness along the radius of the circularly symmetric template is coded indirectly by its second derivative. The fitness function is defined as the area under curve (AUC) of the receiver operating characteristic (ROC) for the mass classifier. The ROC and AUC are obtained for a teaching set of regions of interest (ROIs), for which it is known whether a ROI is true-positive (TP) or false-positive (F). The teaching set is obtained by running the mass detector using a template with a predetermined brightness. Subsequently, the evolutionary algorithm optimizes the template by classifying masses in the teaching set. The optimal template (OT) can be used for detection of masses in mammograms with unknown ROIs. The approach was tested on the training and testing sets of the Digital Database for Screening Mammography (DDSM). The free-response receiver operating characteristic (FROC) obtained with the new mass detector seems superior to the FROC for the hemispherical template (HT). Exemplary results are the following: in the case of the training set in the DDSM, the true-positive fraction (TPF) = 0.82 for the OT and 0.79 for the HT; in the case of the testing set, TPF = 0.79 for the OT and 0.72 for the HT. These values were obtained for disease cases, and the false-positive per image (FPI) = 2.