Computer-aided characterization of malignant and benign microcalcification clusters based on the analysis of temporal change of mammographic features

We have previously demonstrated that interval change analysis can improve differentiation of malignant and benign masses. In this study, a new classification scheme using interval change information was developed to classify mammographic microcalcification clusters as malignant and benign. From each cluster, 20 run length statistic texture features (RLSF) and 21 morphological features were extracted. Twenty difference RLSF were obtained by subtracting a prior RLSF from the corresponding current RLSF. The feature space consisted of the current and the difference RLSF, as well as the current and the difference morphological features. A leave-one-case-out resampling was used to train and test the classifier using 65 temporal image pairs (19 malignant, 46 benign) containing biopsy-proven microcalcification clusters. Stepwise feature selection and a linear discriminant classifier, designed with the training subsets alone, were used to select and merge the most useful features. An average of 12 features were selected from the training subsets, of which 3 difference RLSF and 7 morphological features were consistently selected from most of the training subsets. The classifier achieved an average training Az of 0.98 and a test Az of 0.87. For comparison, a classifier based on the current single image features achieved an average training Az of 0.88 and test Az of 0.81. These results indicate that the use of temporal information improved the accuracy of microcalcification characterization.