Shaving diffusion tensor images in discriminant analysis: A study into schizophrenia

被引:44
作者
Caan, M. W. A.
Vermeer, K. A.
van Vliet, L. J.
Majoie, C. B. L. M.
Peters, B. D.
den Heeten, G. J.
Vos, F. M.
机构
[1] Delft Univ Technol, Quantitat Imaging Grp, NL-2628 CJ Delft, Netherlands
[2] Univ Amsterdam, Acad Med Ctr, Dept Radiol, NL-1105 AZ Amsterdam, Netherlands
[3] Rotterdam Eye Hosp, Rotterdam, Netherlands
关键词
shaving; principal component analysis; linear discriminant analysis; diffusion tensor imaging; schizophrenia;
D O I
10.1016/j.media.2006.07.006
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A technique called 'shaving' is introduced to automatically extract the combination of relevant image regions in a comparative study. No hypothesis is needed, as in conventional pre-defined or expert selected region of interest (ROI)-analysis. In contrast to traditional voxel based analysis (VBA), correlations within the data can be modeled using principal component analysis (PCA) and linear discriminant analysis (LDA). A study into schizophrenia using diffusion tensor imaging (DTI) serves as an application. Conventional VBA found a decreased fractional anisotropy (FA) in a part of the genu of the corpus callosum and an increased FA in larger parts of white matter. The proposed method reproduced the decrease in FA in the corpus callosum and found an increase in the posterior limb of the internal capsule and uncinate fasciculus. A correlation between the decrease in the corpus callosum and the increase in the uncinate fasciculus was demonstrated. (C) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码:841 / 849
页数:9
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