Title: Automated segmentation of multiple sclerosis lesions by model outlier detection
Authors: Van Leemput, Koen ×
Maes, Frederik
Vandermeulen, Dirk
Colchester, Alan
Suetens, Paul #
Issue Date: Aug-2001
Publisher: Institute of Electrical and Electronics Engineers
Series Title: IEEE transactions on medical imaging vol:20 issue:8 pages:677-688
Abstract: 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 expert segmentations, and between expert and automatic measurements.
Description: Van Leemput K., Maes F., Vandermeulen D., Colchester A., Suetens P., ''Automated segmentation of multiple sclerosis lesions by model outlier detection'', IEEE transactions on medical imaging, vol. 20, no. 8, pp. 677-688, August 2001.
ISSN: 0278-0062
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - PSI, Processing Speech and Images
× corresponding author
# (joint) last author

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