Ms. Dongxia Shi Profile

Dongxia Shi

at China Three Gorges Univ

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Proceedings Article | 14 February 2020 Paper

Medical image segmentation based on tree-structured MRF in wavelet domain

Ping Xia, Dong-xia Shi, Bang-jun Lei, Qiang Ren

Proceedings Volume 11431, 114310D (2020) https://doi.org/10.1117/12.2539125

KEYWORDS: Image segmentation, Wavelets, Medical imaging, Image processing algorithms and systems, Image resolution, Wavelet transforms

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Medical image have the characteristics of the complex overlapping of organ and tissue, and accompanied by noise, local volume effect, artifact. So the traditional segmentation method is not ideal. To solve this problem, a medical image segmentation algorithm based on tree-structured MRF in wavelet domain (WTS-MRF) was proposed. For expressing medical image information. WTS-MRF model defines the same tree structure at every scale of wavelet decomposition. At the same time, wavelet transform has good directional selectivity, non-redundancy and multi-scale characteristics. Multiscale and multi direction expression by wavelet decomposition improved the ability of TS-MRF to describe the non-stationary characteristics of images. Then, it can more accurately describe the statistical characteristics of images, and effectively extract the feature information of medical image. In the WTS-MRF model, there are two structures in the layer TS-MRF structure and the interlayer four fork tree structure of wavelet coefficient. The TS-MRF model is built in the layer, and the node potential function is modeled by Potts model. The Gaussian model is used to build the model for the observed characteristics with the same label. The interlayer wavelet coefficients have the property of first-order Markov. The maximum posterior probability is obtained by recursive operation, and the classification hierarchy tree label is implemented to realize medical image segmentation. the experiment results indicate that the algorithm not only can effectively extract the details but also can relatively completely extract target area of medical image, and has higher segmentation accuracy and robustness．

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