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.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Van Leemput K,Maes F,Vandermeulen D,Colchester A,Suetens Pdoi
10.1109/42.938237subject
Has Abstractpub_date
2001-08-01 00:00:00pages
677-88issue
8eissn
0278-0062issn
1558-254Xjournal_volume
20pub_type
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