Automated Segmentation of Multiple Sclerosis Lesions by Model Outlier Detection

van Leemput, Koen and Maes, Frederik and Vandermeulen, Dirk and Colchester, Alan C. F. and Suetens, Paul (2001) Automated Segmentation of Multiple Sclerosis Lesions by Model Outlier Detection. IEEE Transactions on Medical Imaging, 20 (8). pp. 677-688. ISSN 0278-0062. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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This paper presents a fully automated algorithm for segmentation of multiple sclerosis (MS) lesions from multispectral magnetic resonance (MR) images. The method performs intensity-based tissue classification using a stochastic model for normal brain images and simultaneously detects MS lesions as outliers that are not well explained by the model. It corrects for MR field inhomogeneities, estimates tissue-specific intensity models from the data itself, and incorporates contextual information in the classification using a Markov random field. The results of the automated method are compared with lesion delineations by human experts, showing a high total lesion load correlation. When the degree of spatial correspondence between segmentations is taken into account, considerable disagreement is found, both between expect segmentations, and between expert and automatic measurements.

Item Type: Article
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image Analysis, Image Processing
Divisions: Faculties > Science Technology and Medical Studies > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Yiqing Liang
Date Deposited: 27 Oct 2008 11:58
Last Modified: 14 Jul 2014 10:31
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