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.

Keywords

Artificial intelligenceSegmentationMarkov random fieldComputer sciencePattern recognition (psychology)OutlierImage segmentationMultispectral imageComputer vision

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Publication Info

Year
2001
Type
article
Volume
20
Issue
8
Pages
677-688
Citations
495
Access
Closed

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Cite This

Koen Van Leemput, Frederik Maes, Dirk Vandermeulen et al. (2001). Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Transactions on Medical Imaging , 20 (8) , 677-688. https://doi.org/10.1109/42.938237

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DOI
10.1109/42.938237