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Spectral Shape Analysis of the Hippocampal Structure for Alzheimer’s Disease Diagnosis

  • G. Maicas
  • A. I. Muñoz
  • G. Galiano
  • A. Ben Hamza
  • E. Schiavi
  • for the Alzheimer’s Disease Neuroimaging Initiative
Chapter
Part of the SEMA SIMAI Springer Series book series (SEMA SIMAI, volume 8)

Abstract

We present an automatic pipeline for spectral shape analysis of brain subcortical hippocampal structures with the aim to improve the Alzheimer’s Disease (AD) detection rate for early diagnosis. The hippocampus is previously segmented from volumetric T1-weighted Magnetic Resonance Images (MRI) and then it is modelled as a triangle mesh (Fang and Boas, Proceedings of IEEE international symposium on biomedical imaging, pp 1142–1145, 2009) on which the spectrum of the Laplace-Beltrami (LB) operator is computed via a finite element method (Lai, Computational differential geometry and intrinsic surface processing. Doctoral dissertation. University of California, 2010). A fixed number of eigenpairs is used to compute, following (Li and Ben Hamza, Multimed Syst 20(3):253–281, 2014), three different shape descriptors at each vertex of the mesh, which are the heat kernel signature (HKS), the scale-invariant heat kernel signature (SIHKS) and the wave kernel signature (WKS). Each of these descriptors is used separately in a Bag-of-Features (BoF) framework. In this preliminary study we report on the implementation of the proposed descriptors using ADNI (adni.loni.usc.edu), and DEMCAM (T1-weighted MR images acquired on a GE Healthcare Signa HDX 3T scanner) datasets. We show that the best quality of the DEMCAM dataset images have a great impact on the AD rate of detection which can reach up to 95 %. For further development of the modelling approach, local deformation analysis is also considered through a spectral segmentation of the hippocampal structure.

Keywords

Shape Retrieval Hippocampal Structure Heat Kernel Signature Neighborhood Filter Wave Kernel Signature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The second and last two authors would like to thank Ministerio de Economía y Competitividad de España for supporting Project TEC2012-39095-C03-02. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and from the Hospital Fundación Reina Sofía, Madrid, Spain (DEMCAM dataset).

ADNI data: This project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI; NIH Grant U01 AG024904; Principal Investigator: Michael Weiner). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Industry contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • G. Maicas
    • 1
  • A. I. Muñoz
    • 1
  • G. Galiano
    • 2
  • A. Ben Hamza
    • 3
  • E. Schiavi
    • 1
  • for the Alzheimer’s Disease Neuroimaging Initiative
  1. 1.Departamento de Matemática Aplicada, Ciencia e Ingeniería de Materiales y Tecnología ElectrónicaUniversidad Rey Juan Carlos, ESCETMóstolesSpain
  2. 2.Departamento de MatematicasUniversidad de OviedoOviedoSpain
  3. 3.Concordia Institute for Information Systems EngineeringConcordia UniversityMontrealCanada

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