Computerized classification of microcalcifications on mammograms using fuzzy logic and genetic algorithm

The purpose of this study is to develop a computerized scheme for the discrimination between benign and malignant clustered microcalcifications that would aid radiologists in interpreting mammograms. In our scheme, microcalcifications in regions of interest (ROIs) are detected by using morphological filter. Then, four feature values including the total number, mean area, mean circularity and mean minimum distance of microcalcifications are calculated for classification. Gaussian-distributed membership functions used for fuzzy logic are determined from means and standard deviations of these feature values. Finally, fuzzy logic using the genetic-algorithm for optimization of membership functions is employed to classify clustered microcalcifications in unknown ROI. Our scheme was applied to twenty mammographic images with microcalcifications in the Mammographic Image Analysis Society database, containing thirteen benign and twelve malignant ROIs. Of the images ten each benign and malignant ROIs were used for training in fuzzy logic. The remaining five images were classified as benign or malignant cases by fuzzy logic. All sets of their combinations were employed to obtain the result. As the results, the average accuracy was approximately 88% (sensitivity: 100%, specificity: 77%), and Az value of ROC curve was 0.95.