Epicardial fat registration by local adaptive morphology-thresholding based 2D segmentation

Abstract

3D heart registration has become an important issue in cardio vascular diagnosis and treatment. This is mainly due to advanced medical imaging technologies that provide significant amount of data with high precision. One of the important features of the heart that has recently drawn attention is epicardial fat (surrounds the heart), which according to some preliminary studies can be correlated well with risk prediction of various cardiovascular diseases. Consequently, automatic detection and registration of epicardial fat is considered as important task for medical doctors to include as additional feature within the already existing software for medical imaging and visualization. In this paper, we analyze heart images obtained by 4D CT technology and propose a segmentation scheme that automatically extracts epcardial fat in each 2D slice in order to perform 3D epicardial fat registration and visualization. The segmentation algorithm first enhances input image after which it performs patch based labeling and clustering of the selected features. The experimental results indicate good epicardial fat registration performance in comparison to manual segmentation obtained by the medical doctors.