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This paper proposes a novel segmentation method based on improved Markov Random Field(MRF) model, which integrates priori and boundary information of the image. First, proposes a novel prior energy function which uses pixel intensity and boundary information of the image simultaneously for the first time, it can give higher segmentation accuracy while maintaining a good boundary. Then, introduces...
Medical imaging applications produce large sets of similar images. The huge amount of data makes the manual analysis and interpretation a fastidious task. Medical image segmentation is thus an important process in image processing used to partition the images into different regions (e.g. gray matter, white matter and cerebrospinal fluid). Hidden Markov Random Field (HMRF) Model and Gibbs distributions...
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...
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