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New Representation of Information Extracted from MRI Volumes Applied to Alzheimer’s Disease

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ICT Innovations 2014 (ICT Innovations 2014)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 311))

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Abstract

The aim of the paper is to propose a new representation of the information extracted from the MRI volumes in the context of Alzheimer’s Disease (AD). Two main stages are required: segmentation and estimation of the quantitative measurements. The representation is comprised of the quantitative measurements highlighted in the literature as valuable markers for distinguishing AD from healthy controls: cortical thickness of the separate parts of the brain cortex, the volume of the ventricular structures: left and right lateral ventricle, third and fourth ventricle, and the volume of the left and right hippocampus, left and right amygdala. Moreover, a representation that addresses the change of the patient’s condition over the time is also proposed. An illustration and discussion of the proposed method is given by using the MRIs provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI).

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References

  1. Mazziotta, J.C., Toga, A.W., Frackowiak, R.S. (eds.): Brain Mapping: The Disorders. Academic Press (2000)

    Google Scholar 

  2. Akgül, C.B., Rubin, D.L., Napel, S., Beaulieu, C.F., Greenspan, H., Acar, B.: Content-Based Image Retrieval in Radiology: Current Status and Future Directions. J. Digit. Imag. 24(2), 208–222 (2011)

    Article  Google Scholar 

  3. Chincarini, A., Bosco, P., Calvini, P., Gemme, G., Esposito, M., Olivieri, C., Rei, L., et al.: Local MRI Analysis Approach in the Diagnosis of Early and Prodromal Alzheimer’s Disease. Neuroimage 58(2), 469–480 (2011)

    Article  Google Scholar 

  4. Agarwal, M., Mostafa, J.: Content-Based Image Retrieval for Alzheimer’s Disease Detection. In: 9th International Workshop on Content-Based Multimedia Indexing (CBMI), Madrid, Spain, pp. 13–18 (2011)

    Google Scholar 

  5. Agarwal, M., Mostafa, J.: Image Retrieval for Alzheimer’s Disease Detection. In: Caputo, B., Müller, H., Syeda-Mahmood, T., Duncan, J.S., Wang, F., Kalpathy-Cramer, J. (eds.) MCBR-CDS 2009. LNCS, vol. 5853, pp. 49–60. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  6. Yang, S.T., Lee, J.D., Huang, C.H., Wang, J.J., Hsu, W.C., Wai, Y.Y.: Computer-Aided Diagnosis of Alzheimer’s Disease Using Multiple Features with Artificial Neural Network. In: Zhang, B.-T., Orgun, M.A. (eds.) PRICAI 2010. LNCS (LNAI), vol. 6230, pp. 699–705. Springer, Heidelberg (2010)

    Google Scholar 

  7. Moore, D.W., Kovanlikaya, I., Heier, L.A., Raj, A., Huang, C., Chu, K.W., Relkin, N.R.: A Pilot Study of Quantitative MRI Measurements of Ventricular Volume and Cortical Atrophy for the Differential Diagnosis of Normal Pressure Hydrocephalus. Neurology Research International 2012 (2011)

    Google Scholar 

  8. Gerardin, E., Chételat, G., Chupin, M., Cuingnet, R., Desgranges, B., Kim, H.S., Niethammer, M., et al.: Multidimensional Classification of Hippocampal Shape Features Discriminates Alzheimer’s Disease and Mild Cognitive Impairment from Normal Aging. Neuroimage 47(4), 1476–1486 (2009)

    Article  Google Scholar 

  9. Lötjönen, J., Wolz, R., Koikkalainen, J., Julkunen, V., Thurfjell, L., Lundqvist, R., Waldemar, G., Soininen, H., Rueckert, D.: Fast and Robust Extraction of Hippocampus from MR Images for Diagnostics of Alzheimer’s Disease. Neuroimage 56(1), 185–196 (2011)

    Article  Google Scholar 

  10. Leonardo, I.: Atrophy Measurement Biomarkers using Structural MRI for Alzheimer’s Disease. In: The 15th Int. Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) (2012)

    Google Scholar 

  11. Nestor, S.M., Rupsingh, R., Borrie, M., Smith, M., Accomazzi, V., Wells, J.L., Fogarty, J., Bartha, R.: Ventricular Enlargement as a Possible Measure of Alzheimer’s Disease Progression Validated using the Alzheimer’s Disease Neuroimaging Initiative Database. Brain 131(9), 2443–2454 (2008)

    Article  Google Scholar 

  12. Yuan, L., Wang, Y., Thompson, P.M., Narayan, V.A., Ye, J.: Multi-Source Feature Learning for Joint Analysis of Incomplete Multiple Heterogeneous Neuroimaging Data. NeuroImage 61(3), 622–632 (2012)

    Article  Google Scholar 

  13. Sabuncu, M.R., Desikan, R.S., Sepulcre, J., Yeo, B.T.T., Liu, H., Schmansky, N.J., Reuter, M., et al.: The Dynamics of Cortical and Hippocampal Atrophy in Alzheimer Disease. Archives of Neurology 68(8), 1040–1048 (2011)

    Article  Google Scholar 

  14. Gray, K.R., Aljabar, P., Heckemann, R.A., Hammers, A., Rueckert, D.: Random Forest-Based Similarity Measures for Multi-Modal Classification of Alzheimer’s Disease. NeuroImage 65, 167–175 (2013)

    Article  Google Scholar 

  15. Gray, K.R., Wolz, R., Heckemann, R.A., Aljabar, P., Hammers, A., Rueckert, D.: Multi-Region Analysis of Longitudinal FDG-PET for the Classification of Alzheimer’s Disease. NeuroImage 60(1), 221–229 (2012)

    Article  Google Scholar 

  16. Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M.O., Chupin, M., Benali, H., Colliot, O.: Automatic Classification of Patients with Alzheimer’s Disease from Structural MRI: A Comparison of Ten Methods using the ADNI Database. Neuroimage 56(2), 766–781 (2011)

    Article  Google Scholar 

  17. Müller, H., Greenspan, H.: Overview of the Third Workshop on Medical Content–Based Retrieval for Clinical Decision Support (MCBR–CDS 2012). In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 1–9. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  18. Chupin, M., Gérardin, E., Cuingnet, R., Boutet, C., Lemieux, L., Lehéricy, S., Benali, H., Garnero, L., Colliot, O.: Fully Automatic Hippocampus Segmentation and Classification in Alzheimer’s Disease and Mild Cognitive Impairment Applied on Data from ADNI. Hippocampus 19(6), 579–587 (2009)

    Article  Google Scholar 

  19. Heckemann, R.A., Keihaninejad, S., Aljabar, P., Gray, K.R., Nielsen, C., Rueckert, D., Hajnal, J.V., Hammers, A.: Automatic Morphometry in Alzheimer’s Disease and Mild Cognitive Impairment. Neuroimage 56(4), 2024–2037 (2011)

    Article  Google Scholar 

  20. FreeSurfer, https://surfer.nmr.mgh.harvard.edu

  21. Accomazzi, V., Lazarowich, R., Barlow, C.J., Davey, B.: U.S. Patent No. 7,596,267. U.S. Patent and Trademark Office, Washington, DC(2009)

    Google Scholar 

  22. Cataldo, R., Agrusti, A., De Nunzio, G., Carlà, A., De Mitri, I., Favetta, M., Quarta, M., Monno, L., Rei, L., Fiorina, E.: Generating a Minimal Set of Templates for the Hippocampal Region in MR Neuroimages. J. Neuroim. 23(3), 473–483 (2013)

    Google Scholar 

  23. Casanova, R., Hsu, F.C., Espeland, M.A.: Alzheimer’s Disease Neuroimaging Initiative: Classification of Structural MRI Images in Alzheimer’s Disease from the Perspective of Ill-Posed Problems. PLoS One 7(10), e44877 (2012)

    Google Scholar 

  24. Qian, Y., Gao, X., Loomes, M., Comley, R., Barn, B., Hui, R., Tian, Z.: Content-Based Retrieval of 3D Medical Images. In: eTELEMED 2011, The Third International Conference on eHealth, Telemedicine, and Social Medicine, pp. 7–12 (2011)

    Google Scholar 

  25. Reuter, M., Schmansky, N.J., Rosas, H.D., Fischl, B.: Within-Subject Template Estimation for Unbiased Longitudinal Image Analysis. Neuroimage 61(4), 1402–1418 (2012)

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, The Alzheimer’s Disease Neuroimaging Initiative, Ss. Cyril and Methodius University, Rugjer Boshkovik 16, P.O. Box 393, Skopje, Macedonia

    Katarina Trojacanec, Ivan Kitanovski, Ivica Dimitrovski & Suzana Loshkovska

Authors
  1. Katarina Trojacanec
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  2. Ivan Kitanovski
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  3. Ivica Dimitrovski
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  4. Suzana Loshkovska
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Correspondence to Katarina Trojacanec .

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Editors and Affiliations

  1. Faculty of Computer Science and Engineering, Ss Cyril and Methodius University, Skopje, Macedonia

    Ana Madevska Bogdanova

  2. Faculty of Computer Science and Engineering, Ss Cyril and Methodius University, Skopje, Macedonia

    Dejan Gjorgjevikj

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Trojacanec, K., Kitanovski, I., Dimitrovski, I., Loshkovska, S. (2015). New Representation of Information Extracted from MRI Volumes Applied to Alzheimer’s Disease. In: Bogdanova, A., Gjorgjevikj, D. (eds) ICT Innovations 2014. ICT Innovations 2014. Advances in Intelligent Systems and Computing, vol 311. Springer, Cham. https://doi.org/10.1007/978-3-319-09879-1_25

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  • DOI: https://doi.org/10.1007/978-3-319-09879-1_25

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09878-4

  • Online ISBN: 978-3-319-09879-1

  • eBook Packages: EngineeringEngineering (R0)

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