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Automatic segmentation of brain white matter and white matter lesions in normal aging: comparison of five multispectral techniques.

Abstract：

: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 (MRI) 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 MRI 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.

journal_name

Magn Reson Imaging

journal_title

Magnetic resonance imaging

authors

Valdés Hernández Mdel C,Gallacher PJ,Bastin ME,Royle NA,Maniega SM,Deary IJ,Wardlaw JM

doi

10.1016/j.mri.2011.09.016

subject

Has Abstract

pub_date

2012-02-01 00:00:00

pages

222-9

issue

eissn

0730-725X

issn

1873-5894

pii

S0730-725X(11)00348-1

journal_volume

pub_type

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Automatic segmentation of brain white matter and white matter lesions in normal aging: comparison of five multispectral techniques.