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Ventricle shape analysis using modified WKS for atrophy detection

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Abstract

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.

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References

  1. D’Souza-e-Dias-Medora C, Natekar-Prashant E (2007) Morphometric study of the ventricular system of brain by computerised tomography. J Anat Soc India 56(1):19–24

    Google Scholar 

  2. He Q, Duan Y, Yin X, Gu X, Karsch K, Miles J: Shape analysis of corpus callosum in autism subtype using planar conformal mapping. In Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging (Vol. 7262, p. 726217). International Society for Optics and Photonics (2009)

  3. Nitzken MJ, Casanova MF, Gimel’farb G, Inanc T, Zurada JM, El-Baz A (2014) Shape analysis of the human brain: a brief survey. IEEE J Biomed Health Inform 18(4):1337–1354

    Article  Google Scholar 

  4. Narr KL, Thompson PM, Sharma T, Moussai J, Blanton R, Anvar B, Toga AW (2001) Three-dimensional mapping of temporo-limbic regions and the lateral ventricles in schizophrenia: gender effects. Biol Psychiatry 50(2):84–97

    Article  CAS  Google Scholar 

  5. Jatti A,Gangadhar BN (2012) Measuring complexity of lateral ventricle from MR images for schizophrenia. In 2012 IEEE World Congress on Information and Communication Technologies, pp. 315-319

  6. Cates J, Fletcher PT, Styner M, Shenton M, Whitaker R (2007) Shape modeling and analysis with entropy-based particle systems. In Biennial International Conference on Information Processing in Medical Imaging, pp. 333-345

  7. Lombaert H, Grady L, Polimeni JR, Cheriet F (2011) Fast brain matching with spectral correspondence. In Biennial International Conference on Information Processing in Medical Imaging, pp. 660-673

  8. Germanaud D, Lefèvre J, Toro R, Fischer C, Dubois J, Hertz-Pannier L, Mangin JF (2012) Larger is twistier: spectral analysis of gyrification (SPANGY) applied to adult brain size polymorphism. Neuroimage 63(3):1257–1272

    Article  Google Scholar 

  9. Mio W, Bowers JC, Hurdal MK, Liu X (2007) Modeling brain anatomy with 3D arrangements of curves. In 2007 IEEE 11th International Conference on Computer Vision, pp. 1-8

  10. Wang Y, Peterson BS, Staib LH (2003) 3D brain surface matching based on geodesics and local geometry. Comput Vis Image Underst 89(2-3):252–271

    Article  Google Scholar 

  11. Elnakib A, El-Baz A, Casanova MF, Switala AE (2010) Dyslexia diagnostics by centerline-based shape analysis of the corpus callosum. In 2010 IEEE 20th International Conference on Pattern Recognition, pp. 261-264

  12. Reuter M, Wolter FE, Shenton M, Niethammer M (2009) Laplace–Beltrami eigenvalues and topological features of eigenfunctions for statistical shape analysis. Comput Aided Des 41(10):739–755

    Article  Google Scholar 

  13. Reuter M, Wolter FE, Peinecke N (2006) Laplace–Beltrami spectra as ‘Shape-DNA’of surfaces and solids. Comput Aided Des 38(4):342–366

    Article  Google Scholar 

  14. Rustamov RM (2010) Robust volumetric shape descriptor. In Proceedings of the 3rd Eurographics conference on 3D Object Retrieval (pp. 1-5). Eurographics Association

  15. Shishegar R, Soltanian-Zadeh H, Moghadasi SR (2011) Hippocampal shape analysis in epilepsy using Laplace-Beltrami spectrum. In 2011 IEEE 19th Iranian Conference on Electrical Engineering, pp. 1-5

  16. Andreux M, Rodola E, Aubry M, Cremers D (2014) Anisotropic Laplace-Beltrami operators for shape analysis. In European Conference on Computer Vision, pp. 299-312

  17. Wetzler A, Aflalo Y, Dubrovina A, Kimmel R (2013) The Laplace-Beltrami operator: a ubiquitous tool for image and shape processing. In International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing, pp. 302-316

  18. Peinecke N, Wolter FE, Reuter M (2007) Laplace spectra as fingerprints for image recognition. Comput Aided Des 39(6):460–476

    Article  Google Scholar 

  19. Aubry M, Schlickewei U, Cremers D (2011) The wave kernel signature: a quantum mechanical approach to shape analysis. In 2011 IEEE international conference on computer vision workshops (ICCV workshops), pp. 1626-1633

  20. Aubry M, Schlickewei U, Cremers D (2011) Pose-consistent 3d shape segmentation based on a quantum mechanical feature descriptor. In Joint Pattern Recognition Symposium, pp. 122-131

  21. Bronstein MM, Kokkinos I (2010) Scale-invariant heat kernel signatures for non-rigid shape recognition. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1704-1711

  22. Bronstein MM, Bronstein AM (2010) Shape recognition with spectral distances. IEEE Trans Pattern Anal Mach Intell 33(5):1065–1071

    Article  Google Scholar 

  23. Hou T, Qin H (2014) Diffusion-driven wavelet design for shape analysis. CRC Press

  24. Hwa Kim W, Chung MK, Singh V (2013) Multi-resolution shape analysis via non-Euclidean wavelets: applications to mesh segmentation and surface alignment problems. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2139-2146

  25. Lu Z, Carneiro G, Bradley AP (2015) An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells. IEEE Trans Image Process 24(4):1261–1272

    Article  Google Scholar 

  26. Li C, Xu C, Gui C, Fox MD (2005) Level set evolution without re-initialization: a new variational formulation. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05) (Vol. 1, pp. 430-436

  27. Meyer M, Desbrun M, Schröder P, Barr AH (2003) Discrete differential-geometry operators for triangulated 2-manifolds. In Visualization and mathematics III, pp. 35-57

  28. Belkin M, Sun J, Wang Y (2008) Discrete Laplace operator on meshed surfaces. In Proceedings of the twenty-fourth annual symposium on Computational geometry, pp. 278-287

  29. Belkin M, Sun J, Wang Y (2009) Constructing Laplace operator from point clouds in ℝ d. In Proceedings of the twentieth annual ACM-SIAM symposium on Discrete algorithms (pp. 1031-1040). Society for Industrial and Applied Mathematics

  30. Sun J, Ovsjanikov M, Guibas LJ (2009) A concise and provably informative multi-scale signature based on heat diffusion. Comput Graph Forum 28(5):1383–1392

    Article  Google Scholar 

  31. Li C, Ben Hamza A (2013) A multiresolution descriptor for deformable 3D shape retrieval. Vis Comput 29(6-8):513–524

    Article  Google Scholar 

  32. Li C, and Hamza AB. “Spatially aggregating spectral descriptors for nonrigid 3D shape retrieval: a comparative survey.” Multimedia Syst 1-29

  33. Keller SS, Roberts N (2009) Measurement of brain volume using MRI: software, techniques, choices and prerequisites. J Anthropol Sci 87:127–151

    PubMed  Google Scholar 

  34. Ertekin T, Acer N, Icer S, Yildirim A (2013) Estimation 0f the total brain volume using semi-automatic segmentation and stereology of the newborns’ brain MRI. NeuroQuantology 11(2):181–188

    Article  Google Scholar 

  35. Kotsiantis SB, Zaharakis I, Pintelas P (2007) Supervised machine learning: a review of classification techniques. Emerg Artif Intell Appl Comp Eng 160:3–24

    Google Scholar 

  36. Shi Y, Lai R, Kern K, Sicotte N, Dinov I, Toga AW (2008) Harmonic surface mapping with Laplace-Beltrami eigenmaps. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 147-154

  37. Reuter M, Niethammer M, Wolter FE, Bouix S, Shenton M (2007) Global medical shape analysis using the volumetric Laplace spectrum. In 2007 IEE International Conference on Cyberworlds (CW’07), pp. 417-426

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Authors and Affiliations

  1. School of Medical Science and Technology, IIT Kharagpur, Kharagpur, India

    Jayaraman Thirumagal & Manjunatha Mahadevappa

  2. EKO CT & MRI Scan Centre, Medical College, Calcutta, India

    Anup Sadhu

  3. Radiologist, EKO-CT & MRI Scan Center, Calcutta Medical College and Hospital, Kolkata, India

    Pranab Kumar Dutta

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  1. Jayaraman Thirumagal
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  2. Manjunatha Mahadevappa
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  3. Anup Sadhu
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  4. Pranab Kumar Dutta
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Correspondence to Manjunatha Mahadevappa.

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Thirumagal, J., Mahadevappa, M., Sadhu, A. et al. Ventricle shape analysis using modified WKS for atrophy detection. Med Biol Eng Comput 59, 1485–1493 (2021). https://doi.org/10.1007/s11517-021-02377-z

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  • DOI: https://doi.org/10.1007/s11517-021-02377-z

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