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Ventricle shape analysis using modified WKS for atrophy detection.

Brain ventricle is one of the biomarkers for detecting neurological disorders. Studying the shape of the ventricles will aid in the diagnosis process of atrophy and other CSF-related neurological disorders, as ventricles are filled with CSF. This paper introduces a spectral analysis algorithm based...

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Published in:Medical & Biological Engineering & Computing Vol. 59; no. 7/8; pp. 1485 - 1494
Main Authors: Thirumagal, Jayaraman, Mahadevappa, Manjunatha, Sadhu, Anup, Dutta, Pranab Kumar
Format: Journal Article
Published: Springer Nature Aug2021
Online Access:View this record in EBSCOhost
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      dt: Aug2021
      vid: 59
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      pub: Springer Nature
      place: New York, New York
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      tig:
        atl: Ventricle shape analysis using modified WKS for atrophy detection.
      aug:
        au:
          Thirumagal, Jayaraman
          Mahadevappa, Manjunatha
          Sadhu, Anup
          Dutta, Pranab Kumar
        affil: School of Medical Science and Technology, IIT Kharagpur, Kharagpur, India
      sug:
        subj:
          Magnetic Resonance Imaging
          Algorithms
          Atrophy
          Brain
      ab: Brain ventricle is one of the biomarkers for detecting neurological disorders. Studying the shape of the ventricles will aid in the diagnosis process of atrophy and other CSF-related neurological disorders, as ventricles are filled with CSF. This paper introduces a spectral analysis algorithm based on wave kernel signature. This shape signature was used for studying the shape of segmented ventricles from the brain images. Based on the shape signature, the study groups were classified as normal subjects and atrophy subjects. The proposed algorithm is simple, effective, automated, and less time consuming. The proposed method performed better than the other methods heat kernel signature, scale invariant heat kernel signature, wave kernel signature, and spectral graph wavelet signature, which were used for validation purpose, by producing 94-95% classification accuracy by classifying normal and atrophy subjects correctly for CT, MR, and OASIS datasets.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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