Abstract
White matter changes (WMC) are the focus of intensive research and have been linked
to cognitive impairment and depression in the elderly. Cumbersome manual outlining
procedures make research on WMC labor intensive and prone to subjective bias. We present
a fast, fully automated method for WMC segmentation using a cascade of reduced support
vector machines (SVMs) with active learning. Data of 102 subjects was used in this
study. Two MRI sequences (T1-weighted and FLAIR) and masks of manually outlined WMC
from each subject were used for the image analysis. The segmentation framework comprises
pre-processing, classification (training and core segmentation) and post-processing.
After pre-processing, the model was trained on two subjects and tested on the remaining
100 subjects. The effectiveness and robustness of the classification was assessed
using the receiver operating curve technique. The cascade of SVMs segmentation framework
outputted accurate results with high sensitivity (90%) and specificity (99.5%) values,
with the manually outlined WMC as reference. An algorithm for the segmentation of
WMC is proposed. This is a completely competitive and fast automatic segmentation
framework, capable of using different input sequences, without changes or restrictions
of the image analysis algorithm.
Keywords
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Article info
Publication history
Published online: August 27, 2012
Accepted:
July 26,
2012
Received in revised form:
July 23,
2012
Received:
February 8,
2012
Identification
Copyright
© 2012 Elsevier B.V. Published by Elsevier Inc. All rights reserved.
