Comparison of PCA and ANOVA for Information Selection of CC and MLO Views in Classification of Mammograms

In this paper, we present a method for extraction and attribute selection for textural features classification using the fusion of information from the mediolateral oblique (MLO) view and craniocaudal (CC) views. In the extraction step, wavelet coefficients together with singular value decomposition technique were applied to reduce the number of textural attributes. For the selection stage and reduction of attributes, an evaluation of the Analysis of Variance (ANOVA) technique and Principal Component Analysis (PCA) is performed when used for textural information reduction. In the final step, it was used the Random Forest algorithm for classifying regions of interest (ROIs) of the set of images determined as normal, benign and malignant. The experiments showed that ANOVA reached the higher proportional attributes reduction and featured the best results for information fusion of CC and MLO views. The best classification rates were obtained with ANOVA for normal-benign images (area under the receiver operating characteristic curve - AUC = 0.78) and benign-malignant images (AUC = 0.83) and with the PCA method for normal-malignant images (AUC = 0.85).