I. Introduction
The automatic identification of hypertrophic cardiomyopathy (HCM) using cardiac medical images is an emerging and challenging field. The development of medical imaging technologies provides the capability of early diagnosis and detection of the disease. Computed tomography (CT) among other imaging techniques is preferred for the visualization of the heart left ventricle (LV) and the evaluation of cardiomyopathies [1]. Segmentation and delineation of the left ventricle is a crucial step for the quantification of the morphological and pathological changes, providing important clinical variables, such as ejection fraction, end systolic and diastolic volume, wall thickness, etc. However, for most of the imaging modalities used, the manual segmentation of the heart is labor-intensive and time-consuming for a single subject [2]. Thus, automating the segmentation is highly desirable as it can provide significant contribution both in the clinical and the bioengineering domain.