Detection of microcalcifications in digital mammograms using combined model-based and statistical textural features

We investigated the performance of clustered microcalcifications (MCs) recognition in digital mammograms by using combined model-based and statistical textural features. Twenty mammograms containing 25 areas of MCs from the MIAS MiniMammogram database were used to test the performance of our method. In the first stage, a wavelet filter and two thresholds were used to detect suspicious MCs from the mammograms. In the second stage, textural features based on Markov random field (MRF) and fractal models together with statistical textural features based on the surrounding region-dependence method (SRDM) were extracted from the neighborhood of the suspicious MCs and were classified by a three-layer BPNN. The free-response operating characteristic (FROC) curve was used to evaluate the performance of classification and compare our results with that presented in the literature from four other studies. The results of the experiments suggest that the combined model-based and statistical textural features are suitable for characterizing microcalcifications and capable of supporting a reliable and effective MCs detection. In particular, a true positive rate of about 94% is achieved at the rate of 1.0 false positive per image, or the false positives per image can be reduced to 0.65 FPs/image at the rate of true positive of about 90%.