Segmentation of multiple sclerosis lesions using support vector machines

Ricardo J. Ferrari, Xingchang Wei, Yunyan Zhang, James N. Scott, J. Ross Mitchell

Research output: Contribution to journalConference articlepeer-review

16 Scopus citations


In this paper we present preliminary results to automatically segment multiple sclerosis (MS) lesions in multispectral magnetic resonance datasets using support vector machines (SVM). A total of eighteen studies (each composed of T1-, T2-weighted and FLAIR images) acquired from a 3T GE Signa scanner was analyzed. A neuroradiologist used a computer-assisted technique to identify all MS lesions in each study. These results were used later in the training and testing stages of the SVM classifier. A preprocessing stage including anisotropic diffusion filtering, non-uniformity intensity correction, and intensity tissue normalization was applied to the images. The SVM kernel used in this study was the radial basis function (RBF). The kernel parameter (γ) and the penalty value for the errors (C) were determined by using a very loose stopping criterion for the SVM decomposition. Overall, a 5-fold cross-validation accuracy rate of 80% was achieved in the automatic classification of MS lesion voxels using the proposed SVM-RBF classifier.

Original languageEnglish (US)
Pages (from-to)16-26
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5032 I
StatePublished - 2003
EventMedical Imaging 2003: Image Processing - San Diego, CA, United States
Duration: Feb 17 2003Feb 20 2003


  • MRI
  • Multiple sclerosis
  • Segmentation of multiple sclerosis
  • Support vector machines

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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