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International Journal of Biomedical Imaging
Volume 2009 (2009), Article ID 715124, 13 pages
Research Article

Multiple Sclerosis Lesion Detection Using Constrained GMM and Curve Evolution

1Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv 69978, Israel
2School of Engineering, Bar-Ilan University, Ramat-Gan 52900, Israel

Received 12 November 2008; Revised 3 June 2009; Accepted 15 July 2009

Academic Editor: Jiang Hsieh

Copyright © 2009 Oren Freifeld 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.


This paper focuses on the detection and segmentation of Multiple Sclerosis (MS) lesions in magnetic resonance (MRI) brain images. To capture the complex tissue spatial layout, a probabilistic model termed Constrained Gaussian Mixture Model (CGMM) is proposed based on a mixture of multiple spatially oriented Gaussians per tissue. The intensity of a tissue is considered a global parameter and is constrained, by a parameter-tying scheme, to be the same value for the entire set of Gaussians that are related to the same tissue. MS lesions are identified as outlier Gaussian components and are grouped to form a new class in addition to the healthy tissue classes. A probability-based curve evolution technique is used to refine the delineation of lesion boundaries. The proposed CGMM-CE algorithm is used to segment 3D MRI brain images with an arbitrary number of channels. The CGMM-CE algorithm is automated and does not require an atlas for initialization or parameter learning. Experimental results on both standard brain MRI simulation data and real data indicate that the proposed method outperforms previously suggested approaches, especially for highly noisy data.