RESEARCH ARTICLE
Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs
Bassem A Abdullah*, Akmal A Younis, Nigel M John
Article Information
Identifiers and Pagination:
Year: 2012Volume: 6
First Page: 56
Last Page: 72
Publisher ID: TOBEJ-6-56
DOI: 10.2174/1874120701206010056
Article History:
Received Date: 4/11/2011Revision Received Date: 11/11/2011
Acceptance Date: 23/12/2011
Electronic publication date: 9/5/2012
Collection year: 2012
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
Abstract
In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The classification is done on each of the axial, sagittal and coronal sectional brain view independently and the resultant segmentations are aggregated to provide more accurate output segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional view segmentation to produce verified segmentation. The proposed textural-based SVM technique was evaluated using three simulated datasets and more than fifty real MRI datasets. The results were compared with state of the art methods. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI.
