Publication

Abstract : The systolic and diastolic heart failure (HF) subjects are typically categorized based on clinical indices only. The relationship between different stages of systolic and diastolic heart failure and left ventricle (LV) myocardial tissue variations is presented in this work. The corr-entropy and optimized edge criterion has been incorporated into the level set (CEOELS) for effective segmentation of myocardium in cardiovascular magnetic resonance images to handle noise, intensity inhomogeneity and contour initialization. In order to learn shape and local variations in segmented myocardium, Krawtchouk moment features are computed for ten different moment orders. The relevant extracted features are obtained through Harris hawks optimization algorithm. The optimized features are fed to support vector machine (SVM) that uses fivefold cross-validation approach for classification. Experimental results show that CEOELS has provided better segmentation of LV blood cavity and myocardium with a similarity measure of 0.93 and 0.92, respectively. It is also observed that individual Krawtchouk moment orders greater than 30 have provided better HF prediction performance. Consequently, optimized Krawtchouk moment features produced an increased overall accuracy (80.8%) than individual feature sets. Significant improvement has also been achieved in distinction of hyperdynamic patients from normal and systolic dysfunction subjects that is less explored.