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Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 Workshops pp 595–609Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNIP,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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Author information

Authors and Affiliations

  1. Department of ECE, National Institute of Technology, Warangal, India

    Naveen Madiraju

  2. Faculty of Applied Sciences, Simon Fraser University, Burnaby, Canada

    Amarjot Singh

  3. Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India

    S. N. Omkar

Authors
  1. Naveen Madiraju
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  2. Amarjot Singh
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  3. S. N. Omkar
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Corresponding author

Correspondence to Amarjot Singh .

Editor information

Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University, Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Madiraju, N., Singh, A., Omkar, S.N. (2017). Level Set Segmentation of Brain Matter Using a Trans-Roto-Scale Invariant High Dimensional Feature. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10117. Springer, Cham. https://doi.org/10.1007/978-3-319-54427-4_43

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  • DOI: https://doi.org/10.1007/978-3-319-54427-4_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54426-7

  • Online ISBN: 978-3-319-54427-4

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