Abstract

Myocardial Ischemia segmentation is a challenging task for basic and translational research on cardiovascular, as it provides ultimately “realistic” in heart muscle model. The main objective of the research work is to find an efficient segmentation technique for the myocardial ischemia based on the myocardial infarcted MRI data set for the accurate classification of scar volume. The paper will give an insight about the segmentation technique based on myocardial ischemia and discusses essential cellular components. The paper provides an integrated approach which comprises of fuzzy c-means and morphological operations along with median filtering enhancement technique help in detecting the myocardial ischemia. The developed model is tested with 2D and 3D enhanced myocardial ischemia MRI and also with normal heart. The purpose of segmentation in myocardial ischemia is to identify the scar region in the heart. The integrated model is evaluated based on statistical measures and validated based on manual segmentation done by clinical expert. The scar classification is done based on the myocardial ischemia segmentation which leads to better prediction of arrhythmia in heart patient. The integrated model is considered as one of the best model for segmenting myocardial ischemia.

中文翻译：

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心肌缺血的综合分割技术

摘要

心肌缺血分割是心血管基础研究和转化研究的一项艰巨任务，因为它最终在心肌模型中提供了“现实”。研究工作的主要目的是基于心肌梗死MRI数据集找到一种有效的心肌缺血分割技术，以对疤痕量进行准确分类。本文将提供有关基于心肌缺血的分割技术的见解，并讨论必需的细胞成分。本文提供了一种综合的方法，该方法包括模糊c均值和形态学运算以及中值滤波增强技术，有助于检测心肌缺血。使用2D和3D增强型心肌缺血MRI以及正常心脏对开发的模型进行测试。心肌缺血中分割的目的是识别心脏的疤痕区域。基于统计量评估集成模型，并基于临床专家进行的手动分割进行验证。疤痕分类是基于心肌缺血分割进行的，可以更好地预测心脏病患者的心律不齐。集成模型被认为是分割心肌缺血的最佳模型之一。