Segmentation of Cardiac Epicardial and Pericardial Fats by Using Gabor Filter Bank Based GLCM

Epicardial and Pericardial visceral adipose tissues around the cardiac are associated with several cardiovascular risk factors and adverse metabolic effects. As a result, automatic recognition and determination of cardiac fats can be used as an additional feature in medical imaging and visualization by physicians, due to advanced medical imaging technologies that offer a significant amount of information with high accuracy. The determination of cardiac fats in CT images also requires image segmentation methods. In this paper, we propose an automated method for the determination and segmentation of epicardial and pericardial adipose tissues in non-contrast cardiac CT images. The proposed method in this work includes the pre-processing step using thresholding in the fat range and contrast enhancement using histogram analysis, feature extraction step based on texture features extracted using Gabor filter bank based GLCM and pixel information, and the cardiac fats segmentation step are based on pixel labeling and classification algorithms. The experimental results also indicate a good performance of cardiac fat segmentation compared to the manual segmentation obtained by Expert. Experiments showed that the average accuracy obtained for epicardial and pericardial fats was 99% with a mean true positive rate of 98.6%. On average, the Dice Similarity Index was 98.8%.