Computer-Aided Diagnostic System for Digital Mammography

Abstract

In this work, Computer-Aided Detection (CADe) and Computer-Aided Diagnosis (CADx) systems are developed and tested using the public and freely available mammographic databases named MIAS and DDSM databases, respectively. CADe system is used to differentiate between normal and abnormal tissues, and it assists radiologists to avoid missing a breast abnormality. At the same time, CADx is developed to distinguish between normal, benign and malignant breast tissues, and it helps radiologists to decide whether a biopsy is needed when reading a diagnostic mammogram or not. Any CAD system is constituted of typical stages including preprocessing and segmentation of mammogram images, extraction of regions of interest (ROI), features removal, features selection and classification. In both proposed CAD systems, ROIs are selected using a window size of 32×32 pixels, then a total of 543 features from four different feature categories are extracted from each ROI and then normalized. After that, the selection of the most relevant features is performed using four different selection methods from MATLAB Pattern Recognition Toolbox v.5 (PRtool5) named Sequential Backward Selection (SBS), Sequential Forward Selection (SFS), Sequential Floating Forward Selection (SFFS) and Branch and Bound Selection (BBS) methods. We also utilized Principal Component Analysis (PCA) as the fifth method to reduce the dimensions of the features set. After that, we used different classifiers such as Support Vector Machines (SVM), K-voting Nearest Neighbor (K-NN), Quadratic Discriminant Analysis (QDA) and Artificial Neural Networks (ANN) for the classification. Both CAD systems have the same implementation stages but different output. CADe systems are designed to detect breast abnormalities while CADx system indicates the likelihood of malignancy of lesions. Finally, we independently compared the performance of all classifiers with each selection method in both modes. The evaluation of the proposed CAD systems is done using performance indices such as sensitivity, specificity, the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) curves, the overall accuracy and Cohen-k factor. Both CAD systems provided encouraging results. These results were different corresponding to the selection method and classifier.