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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 194614, 14 pages
Research Article

Segmentation of Intensity Inhomogeneous Brain MR Images Using Active Contours

1Department of Computer Engineering and Mathematics, Rovira i Virgili University, 43007 Tarragona, Spain
2Korea Institute of Science & Technology Information, Daejeon 305-806, Republic of Korea
3Department of Computer Science & Engineering, Chung-Ang University, Seoul 156-756, Republic of Korea

Received 17 March 2014; Revised 23 June 2014; Accepted 25 June 2014; Published 16 July 2014

Academic Editor: Xiaobo Qu

Copyright © 2014 Farhan Akram et al. 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.


Segmentation of intensity inhomogeneous regions is a well-known problem in image analysis applications. This paper presents a region-based active contour method for image segmentation, which properly works in the context of intensity inhomogeneity problem. The proposed region-based active contour method embeds both region and gradient information unlike traditional methods. It contains mainly two terms, area and length, in which the area term practices a new region-based signed pressure force (SPF) function, which utilizes mean values from a certain neighborhood using the local binary fitted (LBF) energy model. In turn, the length term uses gradient information. The novelty of our method is to locally compute new SPF function, which uses local mean values and is able to detect boundaries of the homogenous regions. Finally, a truncated Gaussian kernel is used to regularize the level set function, which not only regularizes it but also removes the need of computationally expensive reinitialization. The proposed method targets the segmentation problem of intensity inhomogeneous images and reduces the time complexity among locally computed active contour methods. The experimental results show that the proposed method yields better segmentation result as well as less time complexity compared with the state-of-the-art active contour methods.