Joint histogram modelling for segmentation multiple sclerosis lesions

Authors:
Ziming Zeng;Reyer Zwiggelaar
Affiliations:
Faculty of Information and Control Engineering, Shenyang Jianzhu University, Liaoning, China and Department of Computer Science, Aberystwyth University, UK;Department of Computer Science, Aberystwyth University, UK
Venue:
MIRAGE'11 Proceedings of the 5th international conference on Computer vision/computer graphics collaboration techniques
Year:
2011

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Abstract

This paper presents a novel methodology based on joint histograms, for the automated and unsupervised segmentation of multiple sclerosis (MS) lesion in cranial magnetic resonance (MR) imaging. Our workflow is composed of three steps: locate the MS lesion region in the joint histogram, segment MS lesions, and false positive reduction. The advantage of our approach is that it can segment small lesions, does not require prior skull segmentation, and is robust with regard to noisy and inhomogeneous data. Validation on the BrainWeb simulator and real data demonstrates that our method has an accuracy comparable with other MS lesion segmentation methods.