Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior
Joint Authors
Yang, Wei
Feng, Qianjin
Lu, Yisu
Jiang, Jun
Chen, Wufan
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-31
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Lu, Yisu& Jiang, Jun& Yang, Wei& Feng, Qianjin& Chen, Wufan. 2014. Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior. Computational and Mathematical Methods in Medicine،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1034668
Modern Language Association (MLA)
Lu, Yisu…[et al.]. Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior. Computational and Mathematical Methods in Medicine No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1034668
American Medical Association (AMA)
Lu, Yisu& Jiang, Jun& Yang, Wei& Feng, Qianjin& Chen, Wufan. Multimodal Brain-Tumor Segmentation Based on Dirichlet Process Mixture Model with Anisotropic Diffusion and Markov Random Field Prior. Computational and Mathematical Methods in Medicine. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1034668
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1034668