Journal
PATTERN RECOGNITION
Volume 44, Issue 5, Pages 999-1013Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2010.11.017
Keywords
Brain MR image; Image segmentation; FCM; Anisotropic weight; Intensity inhomogeneity; Partial volume
Funding
- National Science Foundation of China
- National Science Foundation of Jiangsu Province
- Ministry of Education of China (RFDP) [60773172, BK2008411, 200802880017]
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Intensity inhomogeneity, noise and partial volume (PV) effect render a challenging task for segmentation of brain magnetic resonance (MR) images. Most of the current MR image segmentation methods focus on only one or two of the effects listed above. In this paper, a framework with modified fast fuzzy c-means for brain MR images segmentation is proposed to take all these effects into account simultaneously and improve the accuracy of image segmentations. Firstly, we propose a new automated method to determine the initial values of the centroids. Secondly, an adaptive method to incorporate the local spatial continuity is proposed to overcome the noise effectively and prevent the edge from blurring. The intensity inhomogeneity is estimated by a linear combination of a set of basis functions. Meanwhile, a regularization term is added to reduce the iteration steps and accelerate the algorithm. The weights of the regularization terms are all automatically computed to avoid the manually tuned parameter. Synthetic and real MR images are used to test the proposed framework. Improved performance of the proposed algorithm is observed where the intensity inhomogeneity, noise and PV effect are commonly encountered. The experimental results show that the proposed method has stronger anti-noise property and higher segmentation precision than other reported FCM-based techniques. (C) 2010 Elsevier Ltd. All rights reserved.
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