A methodology to study Multiple Sclerosis (MS) based on distributions of standardized intensities in segmented tissue regions

This paper presents a hierarchical methodology (1) for segmenting the component tissue regions in fast spin echo T2 and PD images of the brain of Multiple Sclerosis (MS) patients, and (2) for characterizing 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 a 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 rank test is then utilized to assess if there exists a trend between clinical states and the image-based parameters. 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. These results may have implications in monitoring disease progression and treatment effects in MS.