Volumetric Analysis of Multiple Sclerosis Using Multispectral MR Images: Method and Validation

Lihong Li, Hongbing Lu, Xiang Li, Wei Huang, Alina Tudorica, Chris Christodoulou, Lauren Krupp, Zhengrong Liang

Research output: Contribution to conferencePaperpeer-review


We present a fully automatic mixture-based algorithm for segmentation of brain tissues (white and gray matters - WM and GM), cerebral spinal fluid (CSF) and brain lesion to quantitatively analyze multiple sclerosis. The method performs intensity-based tissue classification using multispectral magnetic resonance (MR) images based on a stochastic model. With the existence of white Gaussian noise and spatially invariant blurring in acquired MR images, a Karhunen-Loeve (K-L) domain Wiener filter is applied for an accurate noise reduction and resolution restoration on blurred and noisy images to minimize the partial volume effect (PVE), which is a major limiting factor for the quantitative analysis. Following that, we utilize a Markov random field Gibbs model to integrate the local spatial information into the established expectation-maximization model-fitting algorithm. Each voxel is then classified by a mixture-based maximum a posterior (MAP) criterion, indicating its probabilities of belonging to each class, i.e., each voxel is labeled as a mixel with different tissue percentages, leading to further minimization of the PVE. The volumes of WM, GM and CSF are extracted from the mixture-based segmentation and the corresponding brain atrophies are computed. In this study, we have investigated the accuracy and repeatability of the algorithm with inclusion of noise analysis and point spread function for image resolution enhancement. Experimental results on both phantom and healthy volunteer studies are presented.

Original languageEnglish (US)
Number of pages5
StatePublished - 2002
Externally publishedYes
Event2002 IEEE Nuclear Science Symposium Conference Record - Norfolk, VA, United States
Duration: Nov 10 2002Nov 16 2002


Other2002 IEEE Nuclear Science Symposium Conference Record
Country/TerritoryUnited States
CityNorfolk, VA

ASJC Scopus subject areas

  • Radiation
  • Nuclear and High Energy Physics
  • Radiology Nuclear Medicine and imaging


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