Level Set Segmentation of Brain Matter Using a Trans-Roto-Scale Invariant High Dimensional Feature

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10117)

Abstract

Brain matter extraction from MR images is an essential, but tedious process performed manually by skillful medical professionals. Automation can be a potential solution to this complicated task. However, it is an ambitious task due to the irregular boundaries between the grey and white matter regions. The intensity inhomogeneity in the MR images further adds to the complexity of the problem. In this paper, we propose a high dimensional translation, rotation, and scale-invariant feature, further used by a variational framework to perform the desired segmentation. The proposed model is able to accurately segment out the brain matter. The above argument is supported by extensive experimentation and comparison with the state-of-the-art methods performed on several MRI scans taken from the McGill Brain Web.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of ECENational Institute of TechnologyWarangalIndia
  2. 2.Faculty of Applied SciencesSimon Fraser UniversityBurnabyCanada
  3. 3.Department of Aerospace EngineeringIndian Institute of ScienceBangaloreIndia

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