Comput Math Methods Med - Multimodal brain-tumor segmentation based on Dirichlet process mixture model with anisotropic diffusion and Markov random field prior.

Tópicos

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Resumo

Brain-tumor segmentation is an important clinical requirement for brain-tumor diagnosis and radiotherapy planning. 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. Because the classical MDP segmentation cannot be applied for real-time diagnosis, 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 using 32 multimodal MR glioma image sequences, and the segmentation results are compared with other approaches. The accuracy and computation time of our algorithm demonstrates very impressive performance and has a great potential for practical real-time clinical use.

Resumo Limpo

braintumor segment import clinic requir braintumor diagnosi radiotherapi plan wellknown number cluster one import paramet automat segment howev difficult defin owe high divers appear tumor tissu among differ patient ambigu boundari lesion studi nonparametr mixtur dirichlet process mdp model appli segment tumor imag mdp segment can perform without initi number cluster classic mdp segment appli realtim diagnosi new nonparametr segment algorithm combin anisotrop diffus markov random field mrf smooth constraint propos studi besid segment singl modal braintumor imag develop algorithm segment multimod braintumor imag magnet reson mr multimod featur obtain activ tumor edema time propos algorithm evalu use multimod mr glioma imag sequenc segment result compar approach accuraci comput time algorithm demonstr impress perform great potenti practic realtim clinic use

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