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- Title
Multiple Sclerosis Lesion Detection Using Constrained GMM and Curve Evolution.
- Authors
Freifeld, Oren; Greenspan, Hayit; Goldberger, Jacob
- Abstract
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 probabilisticmodel termed Constrained GaussianMixtureModel (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.
- Subjects
MULTIPLE sclerosis; MAGNETIC resonance imaging; BRAIN imaging; GAUSSIAN processes; TISSUE analysis; ALGORITHMS
- Publication
International Journal of Biomedical Imaging, 2009, p1
- ISSN
1687-4188
- Publication type
Article
- DOI
10.1155/2009/715124