Abstract
The process of segmentation of the cardiac image aims to limit the inner and outer walls of the heart to segment all or portions of the organ's boundaries. Due to its accurate morphological information, magnetic resonance (MR) images are typically used in cardiac segmentation as they provide the best contrast of soft tissues. The data acquired from the resulting cardiac images simplifies not only the laboratory assessment but also other conventional diagnostic techniques that provide several useful measures to evaluate and diagnose cardiovascular disease (CVD). Therefore, scientists have offered numerous segmentation schemes to remedy these issues for producing more accurate diagnosis. This work conducts a comparative study among several medical image segmentation schemes to find the most accurate segmentation quality based on performance measurements such as Hausdorff distance, peak signal-to-noise ratio (PSNR), and similarity Dice coefficient. This paper utilizes a multi-axis Cardiac Magnetic Resonance Image (CMRI) database in three axes for several case studies which provide the results of various segmentation schemes. Additionally, throughout the experiments, the performance time of every segmentation scheme is estimated and utilized in the comparison process as an additional performance factor.