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Review of Automatic Segmentation Methods of White Matter Lesions on MRI Data

  • Darya Chyzhyk
  • Manuel GrañaEmail author
  • Gerhard Ritter
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 60)

Abstract

White matter (WM) lesions are a phenomena perceived in magnetic resonance imaging (MRI) which is prevalent in many different brain pathologies, hence the general interest in automated methods for lesion segmentation (LS). We provide a short review of some commonly used state-of-the-art approaches. The article is focused on the machine learning techniques which researches use to construct semi- and fully-automated tools for LS. In addition, we mention the preprocessing steps, features extraction, LS databases and validation techniques.

Keywords

Random Forest Markov Random Field White Matter Hyperintensities Multiple Sclerosis Lesion Tissue Segmentation 
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 International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.GIC Rearch Group of the UPV/EHUDonostia/San SebastiánSpain
  2. 2.CISE DepartmentUniversity of FloridaGainesvilleUSA

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