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Proceedings Paper

Detection of multiple sclerosis lesions in MRIs with an SVM classifier
Author(s): Annarita D'Addabbo; Nicola Ancona; Palma N. Blonda; Roberto Alberto De Blasi
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Paper Abstract

The purpose of this paper is to test the effectiveness of a Support Vector Machine (SVM) classifier, with gaussian kernel function, in the automatic detection of small lesions from Magnetic Resonance Images (MRIs) of a patientt affected by multiple sclerosis. The data set consists of Proton Density, T2 (the spin-spin relaxation time) Spin-Echo images and a three-dimensional T1-weighted gradient echo sequence, called Magnetization-Prepared RApid Gradient Echo, that can be generated from contiguous and very thin sections, allowing detection of small lesions typically affected by partial volume effects and intersection gaps in T1 weighted Spin-Echol sequences. In this context of classification, SVM with Gaussian kernel function exhibited a good classification accuracy, higher than accuracies obtained, on the same data set, with a traditional RBF, confirming its high generalization capability and its effectiveness when applied to low-dimensional multi-spectral images.

Paper Details

Date Published: 15 May 2003
PDF: 8 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.480407
Show Author Affiliations
Annarita D'Addabbo, Institute of Intelligent Systems for Automation-CNR (Italy)
Univ. of Bari (Italy)
Nicola Ancona, Institute of Intelligent Systems for Automation-CNR (Italy)
Univ. of Bari (Italy)
Palma N. Blonda, Institute of Intelligent Systems for Automation-CNR (Italy)
Univ. of Bari (Italy)
Roberto Alberto De Blasi, Institute of Intelligent Systems for Automation-CNR (Italy)
Univ. of Bari (Italy)


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

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