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• W1971924430 abstract "Magnetic resonance imaging (MRI) is frequently used to detect and segment multiple sclerosis lesions due to the detailed and rich information provided. We present a modified expectation-maximisation algorithm to segment brain tissues (white matter, grey matter, and cerebro-spinal fluid) as well as a partial volume class containing fluid and grey matter. This algorithm provides an initial segmentation in which lesions are not separated from tissue, thus a second step is needed to find them. This second step involves the thresholding of the FLAIR image, followed by a regionwise refinement to discard false detections. To evaluate the proposal, we used a database with 45 cases comprising 1.5T imaging data from three different hospitals with different scanner machines and with a variable lesion load per case. The results for our database point out to a higher accuracy when compared to two of the best state-of-the-art approaches." @default.
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• W1971924430 date "2014-07-01" @default.
• W1971924430 modified "2023-10-18" @default.
• W1971924430 title "Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding" @default.
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• W1971924430 doi "https://doi.org/10.1016/j.cmpb.2014.04.006" @default.
• W1971924430 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/24813718" @default.
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