This paper presents (1) an improved hierarchical method for segmenting the
component tissue regions in fast spin echo T2 and PD images of the brain of
Multiple Sclerosis (MS) patients, and (2) a methodology to characterize the
disease utilizing the distributions of standardized T2 and PD intensities in
the segmented tissue regions.
First, the background intensity inhomogeneities are corrected and the intensity scales are standardized for all acquired images.
The segmentation method imposes a feedback-like procedure on our previously
developed hierarchical brain tissue segmentation method. With gradually
simplified patterns in images and stronger evidences, pathological objects
are recognized and segmented in an interplay fashion. After the brain
parenchymal (BP) mask is generated, an under-estimated gray matter mask (uGM)
and an over-estimated white matter mask (oWM) are created. Pure WM (PWM) and
lesion (LS) masks are extracted from the all-inclusive oWM mask. By feedback,
accurate GM and WM masks are subsequently formed. Finally, partial volume
regions of GM and WM as well as Dirty WM (DWM) masks are generated.
Intensity histograms and their parameters (peak height, peak location, and
25th, 50th and 75th percentile values) are computed for both T2 and PD
images within each tissue region. Tissue volumes are also estimated.
Spearman correlation coefficient rank test is then utilized to assess if there
exists a trend between clinical states and the image-based
This image analysis method has been applied to a data set consisting
of 60 patients with MS and 20 normal controls. LS related parameters and clinical Extended Disability Status Scale (EDSS)
scores demonstrate modest correlations. Almost every intensity-based parameter
shows statistical difference between normal control and patient groups with a
level better than 5%. These results can be utilized to monitor
disease progression in MS.