Proceedings of the 3rd International Conference on Computer Science and Service System

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/).

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Volume Title
Proceedings of the 3rd International Conference on Computer Science and Service System
Series
Advances in Intelligent Systems Research
Publication Date
June 2014
ISBN
10.2991/csss-14.2014.127
ISSN
1951-6851
DOI
10.2991/csss-14.2014.127How to use a DOI?
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  -