van Leemput, K. and Maes, F. and Vandermeulen, D. and Colchester, A.C.F. and Suetens, P. (2001) Automated Segmentation of Multiple Sclerosis Lesions by Model Outlier Detection. IEEE Transactions on Medical Imaging, 20 (8). pp. 677-688. ISSN 0278-0062.
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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.
|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 Jan 2010 14:24|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/6522 (The current URI for this page, for reference purposes)|
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