title: Multimodal brain-tumor segmentation based on Dirichlet process mixture model with anisotropic diffusion and Markov random field prior.
abstract: 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.
authors.affiliation: Electronic Engineering Department, South China Institute of Software Engineering, Guangzhou 510990, China ; Key Lab for Medical Image Processing, Southern Medical University, TongHe, Guangzhou 510515, China.
authors.forename: Yisu
authors.initials: Y
authors.lastname: Lu
authors.affiliation: Key Lab for Medical Image Processing, Southern Medical University, TongHe, Guangzhou 510515, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Jiang
authors.affiliation: Key Lab for Medical Image Processing, Southern Medical University, TongHe, Guangzhou 510515, China.
authors.forename: Wei
authors.identifier: 0000-0002-2161-3231
authors.initials: W
authors.lastname: Yang
authors.affiliation: Key Lab for Medical Image Processing, Southern Medical University, TongHe, Guangzhou 510515, China.
authors.forename: Qianjin
authors.identifier: 0000-0001-8647-0596
authors.initials: Q
authors.lastname: Feng
authors.affiliation: Key Lab for Medical Image Processing, Southern Medical University, TongHe, Guangzhou 510515, China.
authors.forename: Wufan
authors.initials: W
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D016880:Anisotropy; D001921:Brain / Q000473:pathology; D001932:Brain Neoplasms / Q000473:pathology*; D016000:Cluster Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods; D008390:Markov Chains; D064847:Multimodal Imaging / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods