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Proceedings Paper

A methodology to study multiple sclerosis (MS) based on distributions of standardized intensities in segmented tissue regions
Author(s): T. Lei; J. K. Udupa; D. Odhner; S. Mishra; G. Wu; E. Schwartz; G.-S. Ying; T. Iwanaga; L. Desiderio; L. Balcer
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Paper Abstract

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 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 with a level better than 5%. These results can be utilized to monitor disease progression in MS.

Paper Details

Date Published: 13 March 2006
PDF: 12 pages
Proc. SPIE 6143, Medical Imaging 2006: Physiology, Function, and Structure from Medical Images, 61430V (13 March 2006); doi: 10.1117/12.654562
Show Author Affiliations
T. Lei, Univ. of Pennsylvania (United States)
J. K. Udupa, Univ. of Pennsylvania (United States)
D. Odhner, Univ. of Pennsylvania (United States)
S. Mishra, Univ. of Pennsylvania (United States)
G. Wu, Univ. of Pennsylvania (United States)
E. Schwartz, Univ. of Pennsylvania (United States)
G.-S. Ying, Univ. of Pennsylvania (United States)
T. Iwanaga, Univ. of Pennsylvania (United States)
L. Desiderio, Univ. of Pennsylvania (United States)
L. Balcer, Univ. of Pennsylvania (United States)

Published in SPIE Proceedings Vol. 6143:
Medical Imaging 2006: Physiology, Function, and Structure from Medical Images
Armando Manduca; Amir A. Amini, Editor(s)

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