Segmentation of cardiac fats based on Gabor filters and relationship of adipose volume with coronary artery disease using FP-Growth algorithm in CT scans

Abstract

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.

Recent Progress in Epicardial and Pericardial Adipose Tissue Segmentation and Quantification Based on Deep Learning: A Systematic Review

“…For EAT segmentation, the authors report a Dice score of 0.979 using the dataset from Rodrigues et al [20]. Similar approaches can be seen in Rodrigues et al [20] and Kazemi et al [46].…”

“…Kazemi et al [46] use contrast limited adaptive histogram equalization in addition to thresholding to preprocess the images. Their features also include a Gabor filter bank, a collection of Gabor filters at different scales and rotations.…”

“…This means that their model was trained and evaluated on different slices of the same patient, allowing potential overfitting of a patient's anatomy. It is not clear if [20,46] use the same validation approach. Ref.…”

Expression Level of IL-17 in Peripheral Blood of Patients with Late Pregnancy and Diagnosis of Maternal-Fetal Tolerance Based on Brain MRI Image Segmentation Algorithm

“…In the whole space-frequency domain, the Gabor filter can extract different features that meet specified conditions in the image by setting different parameter combinations. 5 Twenty-three pregnant and maternal patients with severe preeclampsia and eclampsia admitted to the obstetrics department of our hospital were selected. The diagnosis of hypertension during pregnancy was based on the 7th edition of Obstetrics and Gynecology.…”

“…In the whole space-frequency domain, the Gabor filter can extract different features that meet specified conditions in the image by setting different parameter combinations. 5 …”