@inproceedings{00e485588a6949a7b62d5459354599ca,
title = "Quantitative analysis of multiple sclerosis: A feasibility study",
abstract = "Multiple Sclerosis (MS) is an inflammatory and demyelinating disorder of the central nervous system with a presumed immune-mediated etiology. For treatment of MS, the measurements of white matter (WM), gray matter (GM), and cerebral spinal fluid (CSF) are often used in conjunction with clinical evaluation to provide a more objective measure of MS burden. In this paper, we apply a new unifying automatic mixture-based algorithm for segmentation of brain tissues to quantitatively analyze MS. The method takes into account the following effects that commonly appear in MR imaging: 1) The MR data is modeled as a stochastic process with an inherent inhomogeneity effect of smoothly varying intensity; 2) A new partial volume (PV) model is built in establishing the maximum a posterior (MAP) segmentation scheme; 3) Noise artifacts are minimized by a priori Markov random field (MRF) penalty indicating neighborhood correlation from tissue mixture. The volumes of brain tissues (WM, GM) and CSF are extracted from the mixture-based segmentation. Experimental results of feasibility studies on quantitative analysis of MS are presented.",
keywords = "Inhomogeneity, Magnetic Resonance Image, Markov random field, Maximum A Posterior, Multiple Sclerosis, Partial Volume Segmentation",
author = "Lihong Li and Xiang Li and Xinzhou Wei and Deborah Sturm and Hongbing Lu and Zhengrong Liang",
year = "2006",
doi = "10.1117/12.654181",
language = "English",
isbn = "0819461865",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
booktitle = "Medical Imaging 2006",
note = "Medical Imaging 2006: Physiology, Function, and Structure from Medical Images ; Conference date: 12-02-2006 Through 14-02-2006",
}