Automatic right ventricle segmentation in cardiac MRI via anisotropic diffusion and SPCNN

Kemin Wang; Yurun Ma; Ruoming Lei; Zhen Yang; Yide Ma

doi:10.1117/12.2266118

8 February 2017 Automatic right ventricle segmentation in cardiac MRI via anisotropic diffusion and SPCNN

Kemin Wang, Yurun Ma, Ruoming Lei, Zhen Yang, Yide Ma

Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 1022527 (2017) https://doi.org/10.1117/12.2266118
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan

Abstract

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.

Citation Download Citation

Kemin Wang, Yurun Ma, Ruoming Lei, Zhen Yang, and Yide Ma "Automatic right ventricle segmentation in cardiac MRI via anisotropic diffusion and SPCNN", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 1022527 (8 February 2017); https://doi.org/10.1117/12.2266118

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