Left ventricle segmentation from MSCT data based on random walks approach

Cardiovascular diseases (CVDs) continue to be the number one cause of death globally. Functions evaluation of left ventricle (LV), especially ejection fraction (EF) and mass, is the significant predictor of CVDs. Taking the place of extremely time-consuming manual segmentation, accurate extraction of the cavity and myocardium of LV is the key step for analyzing heart functions quantitatively. In this paper, an improved robust semi-automated approach is presented for segmentation of cavity and myocardium from 3D cardiac multi-slice CT (MSCT) dataset. Based on random walks, a novel seeds selection method composed of region growing technique and morphological operation is introduced to locate and identify the cavity and myocardium of LV. 6-connected lattice topology and Conjugate Gradient method have been applied in the random walker algorithm to promote the segmentation performance of 3D dataset. The consecutive result of 3D reconstruction shows the efficacy and advantage of our method for the segmentation of LV in MSCT images.