Cardiac Magnetic Resonance Image (CMRI) is a significant assistant for the cardiovascular disease clinical diagnosis. The segmentation of right ventricle (RV) is essential for cardiac function evaluation, especially for RV function measurement. Automatic RV segmentation is difficult due to the intensity inhomogeneity and the irregular shape. In this paper, we propose an automatic RV segmentation framework. Firstly, we use the anisotropic diffusion to filter the CMRI. And then, the endocardium is extracted by the simplified pulse coupled neural network (SPCNN) segmentation. At last, the morphologic processors are used to obtain the epicardium. The experiment results show that our method obtains a good performance for both the endocardium and the epicardium segmentation.
Automatic segmentation of Left Ventricle (LV) is an essential task in the field of computer-aided analysis of cardiac function. In this paper, a simplified pulse coupled neural network (SPCNN) based approach is proposed to segment LV endocardium automatically. Different from the traditional image-driven methods, the SPCNN based approach is independent of the image gray distribution models, which makes it more stable. Firstly, the temporal and spatial characteristics of the cardiac magnetic resonance image are used to extract a region of interest and to locate LV cavity. Then, SPCNN model is iteratively applied with an increasing parameter to segment an optimal cavity. Finally, the endocardium is delineated via several post-processing operations. Quantitative evaluation is performed on the public database provided by MICCAI 2009. Over all studies, all slices, and two phases (end-diastole and end-systole), the average percentage of good contours is 91.02%, the average perpendicular distance is 2.24 mm and the overlapping dice metric is 0.86.These results indicate that the proposed approach possesses high precision and good competitiveness.