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Semisupervised Soft Mumford-Shah Model for MRI Brain Image Segmentation
Joint Authors
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-06-28
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Information Technology and Computer Science
Abstract EN
One challenge of unsupervised MRI brain image segmentation is the central gray matter due to the faint contrast with respect to the surrounding white matter.
In this paper, the necessity of supervised image segmentation is addressed, and a soft Mumford-Shah model is introduced.
Then, a framework of semisupervised image segmentation based on soft Mumford-Shah model is developed.
The main contribution of this paper lies in the development a framework of a semisupervised soft image segmentation using both Bayesian principle and the principle of soft image segmentation.
The developed framework classifies pixels using a semisupervised and interactive way, where the class of a pixel is not only determined by its features but also determined by its distance from those known regions.
The developed semisupervised soft segmentation model turns out to be an extension of the unsupervised soft Mumford-Shah model.
The framework is then applied to MRI brain image segmentation.
Experimental results demonstrate that the developed framework outperforms the state-of-the-art methods of unsupervised segmentation.
The new method can produce segmentation as precise as required.
American Psychological Association (APA)
Wang, Hong-Yuan& Chen, Fuhua. 2016. Semisupervised Soft Mumford-Shah Model for MRI Brain Image Segmentation. Applied Computational Intelligence and Soft Computing،Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1094918
Modern Language Association (MLA)
Wang, Hong-Yuan& Chen, Fuhua. Semisupervised Soft Mumford-Shah Model for MRI Brain Image Segmentation. Applied Computational Intelligence and Soft Computing No. 2016 (2016), pp.1-14.
https://search.emarefa.net/detail/BIM-1094918
American Medical Association (AMA)
Wang, Hong-Yuan& Chen, Fuhua. Semisupervised Soft Mumford-Shah Model for MRI Brain Image Segmentation. Applied Computational Intelligence and Soft Computing. 2016. Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1094918
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1094918