期刊
MAGNETIC RESONANCE IMAGING
卷 30, 期 2, 页码 222-229出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.mri.2011.09.016
关键词
Brain; Normal aging; White matter; MRI; Image processing; Segmentation
资金
- Help the Aged
- UK Medical Research Council
- Row Fogo Trust
- Carnegie Trust for the Universities of Scotland
- Scottish Funding Council (SFC)
- Centre for Cognitive Ageing and Cognitive Epidemiology [G0700704/84698]
- Economic and Social Research Council
- Engineering and Physical Sciences Research Council
- Medical Research Council
- Biotechnology and Biological Sciences Research Council
- Medical Research Council [G0701120, G1001245, G0700704] Funding Source: researchfish
- MRC [G1001245, G0700704, G0701120] Funding Source: UKRI
White matter loss, ventricular enlargement and white matter lesions are common findings on brain scans of older subjects. Accurate assessment of these different features is therefore essential for normal aging research. Recently, we developed a novel unsupervised classification method, named 'Multispectral Coloring Modulation and Variance Identification' (MCMxxxVI), that fuses two different structural magnetic resonance imaging (MM) sequences in red/green color space and uses Minimum Variance Quantization (MVQ) as the clustering technique to segment different tissue types. Here we investigate how this method performs compared with several commonly used supervised image classifiers in segmenting normal-appearing white matter, white matter lesions and cerebrospinal fluid in the brains of 20 older subjects with a wide range of white matter lesion load and brain atrophy. The three tissue classes were segmented from T-1-, T-2-, T-2*- and fluid attenuation inversion recovery (FLAIR)-weighted structural MM data using MCMxxxVI and the four supervised multispectral classifiers available in the Analyze package, namely, Back-Propagated Neural Networks, Gaussian classifier, Nearest Neighbor and Parzen Windows. Bland-Altman analysis and Jaccard index values indicated that,. in general, MCMxxxVI performed better than the supervised multispectral classifiers in identifying the three tissue classes, although final manual editing was still required to deliver radiologically acceptable results. These analyses show that MVQ, as implemented in MCMxxxVI, has the potential to provide quick and accurate white matter segmentations in the aging brain, although further methodological developments are still required to automate fully this technique. (C) 2012 Elsevier Inc. All rights reserved.
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