Connected component based ROI selection to improve identification of microcalcification from mammogram images

In this paper, we propose automated image processing method for mammogram image analysis to enhance the classification of malignant and benign clustered micro calcifications. Mini-Mias, one of the most renowned mammogram dataset, is used for our experiment. Region of interest (ROI) is selected using connected component labeling method. Shape and textural features are computed and used in five different classifiers to validate the correctness of labeling method usage for this specific research. Experimental results show that Random Forest and Bagging classifier can produce best classification accuracy among them. We used Receiver operating characteristic (ROC) analysis to asses and differentiate the classification performance from different methods. Random Forest and Bagging, both classifiers provide more than 99% accuracy.