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3D heart registration has become an important issue in cardio-vascular diagnosis and treatment. This is mainly due to more accessible medical imaging technologies that can nowadays provide high precision imaging data at relatively lower cost. 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 indicate risk level of various cardiovascular diseases. As such, 2D/3D registration of epicardial fat, through automatic or semi-automatic detection/segmentation, is considered as valuable task for medical doctors (MDs) to include as additional feature within the already existing software for medical imaging and visualization. Although MDs can visually detect regions of epicardial fat from the image slices manually, i.e., subjectively, it is usually time consuming and error prone task. Moreover, due to considerable amount of parameters used for image pre-processing, which can strongly influence visibility of certain features in the image by MD, it often happens that some important features are missed. Consequently, the most preferable solution is the one that combines objective and subjective (by MD) description of particular image feature (in this example epicardial fat) and then subsequently employs semi-automatic segmentation approach, where in execution stage MD would only roughly indicate particular region of interest (ROI), based on which designed algorithm would process the whole heart volume and compute the 3D volume of the heart and epicardial fat. In this paper, we aim at optimizing and enhancing previously developed algorithm for 2D fat segmentation based on (i) pre-knowledge about epicardial structure (provided by the MDs) and (ii) subjective and objective metric correspondence. Based on the 2D segmentation method we compute the 3D volume in order to perform 3D registration. This new optimized approach is shown to considerably improve the accuracy of the epicardial fat registration using CT images. © 2015 IEEE.