A new method for quantitative analysis of multiple scelerosis using MR images

D. Chen, W. Huang, C. Christodoulou, L. Li, H. Qian, L. Krupp, Z. Liang

Research output: Contribution to journalConference article

3 Scopus citations

Abstract

A method for quantitative analysis of multiple sclerosis (MS) was presented. An automatic self-adaptive image segmentation algorithm was first employed to classify voxels in multi-spectral magnetic resonance (MR) images. The segmentation results from multi-spectral MR images were then combined to obtain reliable results. The volumes of brain tissues and cerebral spinal fluid (CSF) were finally extracted. Since it is fully automated, the results of the segmentation algorithm are completely reproducible. The repeatability of the presented method was evaluated on volunteer data sets. The variation is less than 0.2% for the intra-cranial volume, the whole brain volume, the central CSF, the white matter (WM) and the gray matter (GM). The variation of 3% for the entire CSF is mainly due to the peripheral CSF part, which has more partial volume effect and is less important than the central one. Methods for minimizing this variation are under investigation. These measurements demonstrate the potential for study on whole brain atrophy and cerebral atrophy. Feasibility studies on 14 MS patients were performed. The results are promising.

Original languageEnglish (US)
Pages (from-to)375-380
Number of pages6
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4321
DOIs
StatePublished - Jan 1 2001
EventMedical Imaging 2001: Physiology and Function from Multidimensional Images - Sandiego, CA, United States
Duration: Feb 18 2001Feb 20 2001

Keywords

  • Adaptive segmentation
  • Brain atrophy
  • Multi-spectral MRI
  • Multipl sclerosis
  • Quantative analysis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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