Heart mediastinal and epicardial fat tissues are related to several adverse metabolic effects and cardiovascular risk factors, especially coronary artery disease (CAD). The manual segmentation of those fats is that the high dependence on user intervention and time-consuming analyzes. As a result, the automated measurement of cardiac fats could be considered as one of the most important biomarkers for cardiovascular risks in imaging and medical visualization by physicians. In this paper, we validate an automatic approach for the cardiac fat segmentation in non-contrast CT images then investigate the correlation between cardiac fat volume and CAD using the association rule mining algorithm. The pre-processing step includes threshold and contrast enhancement, the feature extraction step includes Gabor filter bank based on GLCM, the cardiac fat segmentation step is predicated on pattern recognition classification algorithms, and eventually, the step of investigating the relationship between cardiac fat volume and CAD is using FP-Growth algorithm. Experimental validation using CT images of two databases points to a good performance in cardiac fat segmentation. Experiments showed that the accuracy of the designed algorithm using the ensemble classifier with the best performance over other classifiers for the cardiac fat segmentation was 99.2%, with a sensitivity of 96.3% and a specificity of 99.8%. The results of using the FP-Growth algorithm showed that the low volume of epicardial (Confidence = 0.6818, Lift = 1.0626) and mediastinal (Confidence = 0.6696, Lift = 1.0436) fat are associated with healthy individuals and the high volume of epicardial (Confidence = 0.8, Lift = 2.2326) and mediastinal (Confidence = 0.75, Lift = 2.093) fat are related to individuals of CAD. As a result, cardiac fats can be used as a reliable biomarker tool in predicting the extent of CAD stenosis.

中文翻译：

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基于 Gabor 滤波器的心脏脂肪分割以及在 CT 扫描中使用 FP-Growth 算法的脂肪体积与冠状动脉疾病的关系

心脏纵隔和心外膜脂肪组织与多种不良代谢作用和心血管危险因素有关，尤其是冠状动脉疾病 (CAD)。这些脂肪的手动分割是对用户干预和耗时分析的高度依赖。因此，心脏脂肪的自动测量可以被认为是医师成像和医学可视化中心血管风险最重要的生物标志物之一。在本文中，我们验证了一种用于非对比 CT 图像中心脏脂肪分割的自动方法，然后使用关联规则挖掘算法研究心脏脂肪体积与 CAD 之间的相关性。预处理步骤包括阈值和对比度增强，特征提取步骤包括基于 GLCM 的 Gabor 滤波器组，心脏脂肪分割步骤基于模式识别分类算法，最终研究心脏脂肪体积与CAD之间关系的步骤是使用FP-Growth算法。使用两个数据库的 CT 图像进行的实验验证表明在心脏脂肪分割方面具有良好的性能。实验表明，所设计的算法使用性能优于其他分类器的集成分类器对心脏脂肪分割的准确率为99.2%，灵敏度为96.3%，特异性为99.8%。使用 FP-Growth 算法的结果表明，低体积的心外膜 (Confidence = 0.6818, Lift = 1.0626) 和纵隔 (Confidence = 0.6696, Lift = 1.0436) 脂肪与健康个体和大量的心外膜 (Confidence) 相关= 0.8, Lift = 2.2326) 和纵隔 (Confidence = 0.75, Lift = 2.093) 脂肪与 CAD 个体相关。因此，心脏脂肪可用作预测 CAD 狭窄程度的可靠生物标志物工具。