Share Email Print
cover

Proceedings Paper

Research of the multimodal brain-tumor segmentation algorithm
Author(s): Yisu Lu; Wufan Chen
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper 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.

Paper Details

Date Published: 14 December 2015
PDF: 8 pages
Proc. SPIE 9814, MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing, 98140I (14 December 2015); doi: 10.1117/12.2205629
Show Author Affiliations
Yisu Lu, South China Institute of Software Engineering (China)
Wufan Chen, Southern Medical Univ. (China)


Published in SPIE Proceedings Vol. 9814:
MIPPR 2015: Parallel Processing of Images and Optimization; and Medical Imaging Processing
Jianguo Liu, Editor(s)

© SPIE. Terms of Use
Back to Top