Please use this identifier to cite or link to this item:
Title: Improvement of brain segmentation accuracy by optimizing non-uniformity correction using N3
Authors: Zheng, W.
Chee, M.W.L. 
Zagorodnov, V.
Keywords: Cortex thickness estimation
Non-uniformity correction
Issue Date: 15-Oct-2009
Citation: Zheng, 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.
Abstract: Smoothly 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.
Source Title: NeuroImage
ISSN: 10538119
DOI: 10.1016/j.neuroimage.2009.06.039
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Mar 31, 2023


checked on Mar 31, 2023

Page view(s)

checked on Mar 30, 2023

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.