Multiple Sclerosis Lesion Detection Using Constrained GMM and Curve Evolution
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...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2009-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2009/715124 |
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| Summary: | 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. |
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| ISSN: | 1687-4188 1687-4196 |