Automatic MR Brain Tumor Image Segmentation
Authors
Lu Yisu, Chen Wufan
Corresponding Author
Lu Yisu
Available Online June 2014.
- DOI
- 10.2991/csss-14.2014.127How to use a DOI?
- Keywords
- image segmentation; Dirichlet process mixtures; anisotropic diffusion; MRF
- Abstract
Traditional Dirichlet process mixture (MDP) models has the characteristic that the image segmentation can be done without initialization of clustering numbers. For the computing speed of the classical MDP segmentation is jogging, a new kind of nonparametric segmentation (DMMDP algorithm) combined with anisotropic diffusion and Markov Random Fields (MRF) prior was inferred in this paper. The experiment results of menigioma MR images segmentation showed that the properties, such as accuracy and computing speed, of the DMMDP algorithm were significantly greater than the classical MDP model segmentation.
- Copyright
- © 2014, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Lu Yisu AU - Chen Wufan PY - 2014/06 DA - 2014/06 TI - Automatic MR Brain Tumor Image Segmentation BT - Proceedings of the 3rd International Conference on Computer Science and Service System PB - Atlantis Press SP - 541 EP - 544 SN - 1951-6851 UR - https://doi.org/10.2991/csss-14.2014.127 DO - 10.2991/csss-14.2014.127 ID - Yisu2014/06 ER -