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Joint Histogram Modelling for Segmentation Multiple Sclerosis Lesions

  • Ziming Zeng
  • Reyer Zwiggelaar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6930)

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.

Keywords

Grey Matter Segmentation Result Multiple Sclerosis Lesion Grey Matter Region Magnetic Resonance Image Volume 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ziming Zeng
    • 1
    • 2
  • Reyer Zwiggelaar
    • 2
  1. 1.Faculty of Information and Control EngineeringShenyang Jianzhu UniversityLiaoningChina
  2. 2.Department of Computer ScienceAberystwyth UniversityUK

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