Classification of Breast Masses in Mammograms Using Radial Basis Functions and Simulated Annealing

Abstract

We present pattern classification methods based upon nonlinear and combinational optimization techniques, specifically, radial basis functions (RBF) and simulated annealing (SA), to classify masses in mammograms as malignant or benign. Combinational optimization is used to pre-estimate RBF parameters, namely, the centers and spread matrix. The classifier was trained and tested, using the leave-one-out procedure, with shape, texture, and edge-sharpness measures extracted from 57 regions of interest (20 related to malignant tumors and 37 related to benign masses) manually delineated on mammograms by a radiologist. The classifier's performance, with preestimation of the parameters, was evaluated in terms of the area A z under the receiver operating characteristics curve. Values up to A z = 0.9997 were obtained with RBF-SA with pre-estimation of the centers and spread matrix, which are better than the results obtained with pre-estimation of only the RBF centers, which were up to 0.9470. Overall, the results with the RBF-SA method were better than those provided by standard multilayer perceptron neural networks.

Image Compression Based on Generalized Principal Components Analysis and Simulated Annealing

“…In our simulations, a was evaluated by a e tFactor NR i = − − − (William, Teukolsky, Vetterling, & Falnnerry, 2002), where tFactor arbitrarily assumed values of 1, 0.1, 0.01 (William, Teukolsky, Vetterling, & Falnnerry, 2002;Espírito Santo, Deus Lopes, & Rangayyan 2009). NR and i are respectively the maximum and the actual attempt of finding the optimal solution.…”