The auxiliary diagnosis based on three-dimensional (3D) modeling techniques can improve the diagnosis efficiency of heart disease. In this paper, we propose a new method for the 3D modeling of left ventricle. The novel approach incorporatestwo key points: one is LV endocardium segmentation via the simplified pulse-coupled neural network (SPCNN), and the other is interpolation between slices. To ensure the accuracy of modeling, we firstly use the cubic spline interpolation to increase the resolution between slices. Then, the endocardial contours of all slices are automatically obtained based on SPCNN. Finally, Marching Cubes (MC) algorithm is implemented to reconstruct the LV endocardial surface. We test the new method on four setsof 3D data and obtain effective results. The APD and ODM are respectively 1.7950mm and 91.34% for the endocardium segmentation, which makes the surface reconstruction of LV endocardium accurate andsmooth.
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.
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