A Novel Segmentation Method for Left Ventricular from Cardiac MR Images Based on Improved Markov Random Field Model

Abstract

In this paper, we propose a improved Markov Random Field (MRF) segmentation model, which integrates region, priori knowledge and boundary information of the image, for segmenting left ventricle (LV) boundary from cardiac MR image. The proposed model incorporates geometry shape boundary information, and improves the objective function of traditional MRF model. Furthermore, Chaotic Simulated Annealing (CSA) algorithm is introduced to solve the MRF model for the first time. Since CSA algorithm introduces chaos ergodicity mechanism, it can take advantage of Chaos Algorithm (COA) and Simulated Annealing (SA) algorithm in the search process. CSA algorithm can not only avoid the limitations of mathematical optimization methods, but also greatly enhance the speed of global optimization. Experiments on clinical cardiac MR images show that the improved MRF model has high performance on segmenting LV boundary. The evaluation results illustrate that this model is robust, accurate and efficient, especially for the weak boundary and concave region .

Endocardial Border Detection in Cardiac Magnetic Resonance Images Using Level Set Method

“…The probabilistic cardiac atlas has been constructed from the manual segmentations of 3D cardiac image sequences of 14 subjects. In [9], the authors proposed an improved Markov random field segmentation model, which integrates region prior knowledge and boundary information of the image and this for segmenting LV boundary from cardiac MR image.…”