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

Madiraju, Naveen and Singh, Amarjot and Omkar, S N (2017) Level Set Segmentation of Brain Matter Using a Trans-Roto-Scale Invariant High Dimensional Feature. In: 13th Asian Conference on Computer Vision, ACCV 2016, November 20-24, 2016, Taipei, Taiwan, pp. 595-609.

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Official URL: https://doi.org/10.1007/978-3-319-54427-4_43


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.

Item Type: Conference Paper
Publisher: Springer Verlag
Additional Information: The Copyright of the article belongs to the Springer
Keywords: Magnetic resonance imaging; Medical imaging; High dimensional feature; Intensity inhomogeneity; Irregular boundary; Level set segmentation; Medical professionals; Scale invariant features; State-of-the-art methods; Variational framework; Computer vision
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 29 May 2022 06:31
Last Modified: 29 May 2022 06:31
URI: https://eprints.iisc.ac.in/id/eprint/72591

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