Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.neuroimage.2009.06.039
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dc.titleImprovement of brain segmentation accuracy by optimizing non-uniformity correction using N3
dc.contributor.authorZheng, W.
dc.contributor.authorChee, M.W.L.
dc.contributor.authorZagorodnov, V.
dc.date.accessioned2014-11-26T08:28:52Z
dc.date.available2014-11-26T08:28:52Z
dc.date.issued2009-10-15
dc.identifier.citationZheng, W., Chee, M.W.L., Zagorodnov, V. (2009-10-15). Improvement of brain segmentation accuracy by optimizing non-uniformity correction using N3. NeuroImage 48 (1) : 73-83. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neuroimage.2009.06.039
dc.identifier.issn10538119
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/110127
dc.description.abstractSmoothly varying and multiplicative intensity variations within MR images that are artifactual, can reduce the accuracy of automated brain segmentation. Fortunately, these can be corrected. Among existing correction approaches, the nonparametric non-uniformity intensity normalization method N3 (Sled, J.G., Zijdenbos, A.P., Evans, A.C., 1998. Nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imag. 17, 87-97.) is one of the most frequently used. However, at least one recent study (Boyes, R.G., Gunter, J.L., Frost, C., Janke, A.L., Yeatman, T., Hill, D.L.G., Bernstein, M.A., Thompson, P.M., Weiner, M.W., Schuff, N., Alexander, G.E., Killiany, R.J., DeCarli, C., Jack, C.R., Fox, N.C., 2008. Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils. NeuroImage 39, 1752-1762.) suggests that its performance on 3 T scanners with multichannel phased-array receiver coils can be improved by optimizing a parameter that controls the smoothness of the estimated bias field. The present study not only confirms this finding, but additionally demonstrates the benefit of reducing the relevant parameter values to 30-50 mm (default value is 200 mm), on white matter surface estimation as well as the measurement of cortical and subcortical structures using FreeSurfer (Martinos Imaging Centre, Boston, MA). This finding can help enhance precision in studies where estimation of cerebral cortex thickness is critical for making inferences. © 2009 Elsevier Inc. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.neuroimage.2009.06.039
dc.sourceScopus
dc.subjectCortex thickness estimation
dc.subjectNon-uniformity correction
dc.typeArticle
dc.contributor.departmentDUKE-NUS GRADUATE MEDICAL SCHOOL S'PORE
dc.description.doi10.1016/j.neuroimage.2009.06.039
dc.description.sourcetitleNeuroImage
dc.description.volume48
dc.description.issue1
dc.description.page73-83
dc.description.codenNEIME
dc.identifier.isiut000269321100011
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