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Proceedings Paper

Segmentation of multiple sclerosis lesions using support vector machines
Author(s): Ricardo José Ferrari; Xingchang Wei; Yunyan Zhang; James N. Scott; J. Ross Mitchell
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Paper Abstract

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 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.

Paper Details

Date Published: 15 May 2003
PDF: 11 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.481377
Show Author Affiliations
Ricardo José Ferrari, Univ. of Calgary (Canada)
Seaman Family MR Research Ctr. (Canada)
Xingchang Wei, Univ. of Calgary (Canada)
Seaman Family MR Research Ctr. (Canada)
Yunyan Zhang, Univ. of Calgary (Canada)
Seaman Family MR Research Ctr. (Canada)
James N. Scott, Univ. of Calgary (Canada)
J. Ross Mitchell, Univ. of Calgary (Canada)
Seaman Family MR Research Ctr. (Canada)


Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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