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Applied Computational Intelligence and Soft Computing
Volume 2016, Article ID 8508329, 14 pages
http://dx.doi.org/10.1155/2016/8508329
Research Article

Semisupervised Soft Mumford-Shah Model for MRI Brain Image Segmentation

1School of Information Science & Engineering, Changzhou University, Changzhou 213164, China
2Department of Natural Science & Mathematics, West Liberty University, West Liberty, WV 26074, USA

Received 27 March 2016; Accepted 29 May 2016

Academic Editor: Serafín Moral

Copyright © 2016 Hong-Yuan Wang and Fuhua Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.