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- W2208766130 abstract "It is well-known that the number of clusters is one of the most important parameters for automatic segmentation. However, it is difficult to define owing to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this study, a nonparametric mixture of Dirichlet process (MDP) model is applied to segment the tumor images, and the MDP segmentation can be performed without the initialization of the number of clusters. A new nonparametric segmentation algorithm combined with anisotropic diffusion and a Markov random field (MRF) smooth constraint is proposed in this study. Besides the segmentation of single modal brain tumor images, we developed the algorithm to segment multimodal brain tumor images by the magnetic resonance (MR) multimodal features and obtain the active tumor and edema in the same time. The proposed algorithm is evaluated and compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance." @default.
- W2208766130 created "2016-06-24" @default.
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- W2208766130 date "2015-12-14" @default.
- W2208766130 modified "2023-09-23" @default.
- W2208766130 title "Research of the multimodal brain-tumor segmentation algorithm" @default.
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- W2208766130 doi "https://doi.org/10.1117/12.2205629" @default.
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