title: Cardiac image segmentation from cine cardiac MRI using graph cuts and shape priors.
abstract: In this paper, we propose a novel method for segmentation of the left ventricle, right ventricle, and myocardium from cine cardiac magnetic resonance images of the STACOM database. Our method incorporates prior shape information in a graph cut framework to achieve segmentation. Poor edge information and large within-patient shape variation of the different parts necessitates the inclusion of prior shape information. But large interpatient shape variability makes it difficult to have a generalized shape model. Therefore, for every dataset the shape prior is chosen as a single image clearly showing the different parts. Prior shape information is obtained from a combination of distance functions and orientation angle histograms of each pixel relative to the prior shape. To account for shape changes, pixels near the boundary are allowed to change their labels by appropriate formulation of the penalty and smoothness costs. Our method consists of two stages. In the first stage, segmentation is performed using only intensity information which is the starting point for the second stage combining intensity and shape information to get the final segmentation. Experimental results on different subsets of 30 real patient datasets show higher segmentation accuracy in using shape information and our method's superior performance over other competing methods.
authors: Department of Computer Science, Swiss Federal Institute of Technology (ETH), CAB F 61.1, Universitätstrasse 6, 8092 Zurich, Switzerland. dmahapatra@gmail.com
authors: Dwarikanath
authors: D
authors: Mahapatra
mesh_terms: D006352:Heart Ventricles / Q000473:pathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D015233:Models, Statistical; D009206:Myocardium / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results