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.

收缩性和舒张性心力衰竭 (HF) 受试者通常仅根据临床指标进行分类。这项工作介绍了收缩和舒张心力衰竭不同阶段与左心室 (LV) 心肌组织变异之间的关系。相关熵和优化边缘标准已被纳入水平集 (CEOELS)，用于有效分割心血管磁共振图像中的心肌，以处理噪声、强度不均匀性和轮廓初始化。为了学习分割心肌的形状和局部变化，Krawtchouk 矩特征被计算为十个不同的矩顺序。提取的相关特征通过Harris hawks优化算法得到。优化后的特征被提供给支持向量机 (SVM)，它使用五重交叉验证方法进行分类。实验结果表明，CEOELS 提供了更好的 LV 血腔和心肌分割，相似度分别为 0.93 和 0.92。还观察到大于 30 的单个 Krawtchouk 矩阶数提供了更好的 HF 预测性能。因此，优化的 Krawtchouk 矩特征比单个特征集产生了更高的整体准确度 (80.8%)。在区分高动力患者与正常和收缩功能障碍受试者方面也取得了显着改善，但研究较少。实验结果表明，CEOELS 提供了更好的 LV 血腔和心肌分割，相似度分别为 0.93 和 0.92。还观察到大于 30 的单个 Krawtchouk 矩阶数提供了更好的 HF 预测性能。因此，优化的 Krawtchouk 矩特征比单个特征集产生了更高的整体准确度 (80.8%)。在区分高动力患者与正常和收缩功能障碍受试者方面也取得了显着改善，但研究较少。实验结果表明，CEOELS 提供了更好的 LV 血腔和心肌分割，相似度分别为 0.93 和 0.92。还观察到大于 30 的单个 Krawtchouk 矩阶数提供了更好的 HF 预测性能。因此，优化的 Krawtchouk 矩特征比单个特征集产生了更高的整体准确度 (80.8%)。在区分高动力患者与正常和收缩功能障碍受试者方面也取得了显着改善，但研究较少。