We present two semi-automatic solution methods for the three dimensional (3D) segmentation of cavity and myocardium from a 3D cardiac multislice CT (MSCT) data. The main framework of the segmentation algorithms is based on random walks, in which the novelty lies in a seeds-selection method composed of region growing technique and morphological operation to locate and identify the cavity and myocardium of the left ventricle (LV). In the first solution, a semi-automatic segmentation approach (Method_1) is suggested to extract the epi-cardium and endo-cardium boundaries of LV of the heart. This proposed solution depends on the use of the normal random walker algorithm. In the second solution, a semi-automatic segmentation approach (Method_2) based on improved random walks is proposed. Segmentation is done within the framework of toboggan algorithm in combination with a random walk based technique. The two proposed semi-automatic segmentation methods either based on the normal random walker or the improved random walker algorithms utilized six-connected lattice topology and a conjugate gradient method to promote the segmentation performance of the 3D data. The two semi-automatic solution methods were evaluated using 20 cardiac MSCT datasets. Semi-automatic generated contours were compared to expert contours. For Method_1, 83.4% of epi-cardial contours and 74.7% of endo-cardial contours had a maximum error of 5 mm along 95% of the contour arc length. For Method_2, those numbers were 94.3% (epi-cardium) and 92.3% (endo-cardium), respectively. Volume regression analysis revealed good linear correlations between manual and semiautomatic volumes, r ≥ 0.99.