matic assessment of mitral regurgitation severity based on extensive textural features on 2D echocardiography videos.

Heart disease is the major cause of death as well as a leading cause of disability in the developed countries. Mitral Regurgitation (MR) is a common heart disease which does not cause symptoms until its end stage. Therefore, early diagnosis of the disease is of crucial importance in the treatment process. Echocardiography is a common method of diagnosis in the severity of MR. Hence, a method which is based on echocardiography videos, image processing techniques and artificial intelligence could be helpful for clinicians, especially in borderline cases. In this paper, we introduce novel features to detect micro-patterns of echocardiography images in order to determine the severity of MR. Extensive Local Binary Pattern (ELBP) and Extensive Volume Local Binary Pattern (EVLBP) are presented as image descriptors which include details from different viewpoints of the heart in feature vectors. Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Template Matching techniques are used as classifiers to determine the severity of MR based on textural descriptors. The SVM classifier with Extensive Uniform Local Binary Pattern (ELBPU) and Extensive Volume Local Binary Pattern (EVLBP) have the best accuracy with 99.52%, 99.38%, 99.31% and 99.59%, respectively, for the detection of Normal, Mild MR, Moderate MR and Severe MR subjects among echocardiography videos. The proposed method achieves 99.38% sensitivity and 99.63% specificity for the detection of the severity of MR and normal subjects.