Research of the multimodal brain-tumor segmentation algorithm

Yisu Lu; Wufan Chen

doi:10.1117/12.2205629

14 December 2015 Research of the multimodal brain-tumor segmentation algorithm

Yisu Lu, Wufan Chen

Proceedings Volume 9814, MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing; 98140I (2015) https://doi.org/10.1117/12.2205629
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

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.

Citation Download Citation

Yisu Lu and Wufan Chen "Research of the multimodal brain-tumor segmentation algorithm", Proc. SPIE 9814, MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing, 98140I (14 December 2015); https://doi.org/10.1117/12.2205629

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KEYWORDS

Image segmentation

Tumors

Magnetic resonance imaging

Image processing algorithms and systems

Magnetorheological finishing

Brain

Image processing

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