Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern

This paper introduces a computer-aided classification (CAC) system for the severity analysis of mitral regurgitation (MR) utilizing multiresolution local binary pattern variants texture features. Initially, the Gaussian pyramid has been used as a multiresolution technique. Subsequently, seven variants of the local binary pattern (LBP) have been employed to extract the features. At last, support vector machine and random forest classifiers are used for classification. The performances of conventional LBP variants and proposed features have been evaluated on MR image database in three classes, i.e., mild, moderate, and severe, in three different views. The Gaussian pyramid-based center-symmetric local binary pattern performed well in all three views. The achieved classification accuracies are 95.66 ± 0.98% in the apical 2 chamber, 94.47 ± 1.91% in the apical 4 chamber and 94.21 ± 1.31% in parasternal long axis views using SVM classifier with the tenfold cross-validation. The outcomes of paper confirm that the performance of the conventional LBP features is enhanced significantly and the proposed CAC system is useful in assisting cardiologists in the severity analysis of MR.