Medical Image Retrieval for Alzheimer’s Disease Using Structural MRI Measures

  • Katarina TrojacanecEmail author
  • Ivan Kitanovski
  • Ivica Dimitrovski
  • Suzana Loshkovska
  • for the Alzheimer’s Disease Neuroimaging Initiative*
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 574)


The aim of the paper is to study medical image retrieval for Alzheimer’s Disease (AD) on the bases of structural MRI measures. The main goal of the strategy used in this paper is to improve the retrieval performance while using smaller number of features. The feature vector consists of the measurements of cortical and subcortical brain structures, including volumes of the brain structures and cortical thickness. The feature subset selection is additionally applied using the Correlation-based Feature Selection method to exclude irrelevant, redundant or possibly noisy data and to consider the most relevant and discriminative features. Six different scenarios for the image representation are studied: volumetric features, cortical thickness features, all imaging features, selected volumetric features, selected cortical thickness feature and selected imaging features. Euclidean distance is used as a similarity measurement. The dataset used for evaluation of the retrieval performance is provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Experimental results show that the strategy used in this research outperforms the traditional one despite its simplicity and small number of features used for representation. Additionally, the performed analysis demonstrated that the selected features are highly stable through the leave-one-out strategy. Moreover, they are stressed in the literature as significant biomarkers for Alzheimer’s Disease, which makes the strategy used in this research even more reasonable.


CBIR Alzheimer’s Disease VOI Segmentation Feature extraction Feature selection MRI ADNI 



Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). The National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; 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.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company are all funders of ADNI. ADNI clinical sites in Canada are supported and funded by the Canadian Institutes of Health Research. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( The study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego, whereas the grantee organization for it, is the Northern California Institute for Research and Education. The Laboratory for Nero Imaging at the University of Southern California is dissemination the ADNI data.

Authors also acknowledge the support of the European Commission through the project MAESTRA - Learning from Massive, Incompletely annotated, and Structured Data (Grant number ICT-2013-612944).


  1. 1.
    Nho, K., Risacher, L.S., Crane, P.K., DeCarli, C., Glymour, M.M., Habeck, C., Kim, S., et al.: Voxel and surface-based topography of memory and executive deficits in mild cognitive impairment and Alzheimer’s disease. Brain Imaging Behav. 6(4), 551–567 (2012)CrossRefGoogle Scholar
  2. 2.
    Oliveira, M.C., Cirne, W., de Azevedo Marques, P.M.: Towards applying content-based image retrieval in the clinical routine. Future Gener. Comput. Syst. 23(3), 466–474 (2007)CrossRefGoogle Scholar
  3. 3.
    Rosset, A., Muller, H., Martins, M., Dfouni, N., Vallée, J.-P., Ratib, O.: Casimage project - a digital teaching files authoring environment. J. Thorac. Imaging 19(2), 1–6 (2004)CrossRefGoogle Scholar
  4. 4.
    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. Imaging 24(2), 208–222 (2011)CrossRefGoogle Scholar
  5. 5.
    Depeursinge, A., Zrimec, T., Busayarat, S., Müller, H.: 3D lung image retrieval using localized features. SPIE Medical Imaging, pp. 79632E–79632E. International Society for Optics and Photonics, Bellingham (2011)Google Scholar
  6. 6.
    Akgül, C.B., Ünay, D., Ekin, A.: Automated diagnosis of Alzheimer’s disease using image similarity and user feedback. In: Proceedings of the ACM International Conference on Image and Video Retrieval, p. 34 (2009)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Agarwal, M., Mostafa, J.: Content-based image retrieval for Alzheimer’s disease detection. In: 9th International Workshop on Content-based Multimedia Indexing (CBMI), pp. 13–18 (2011)Google Scholar
  9. 9.
    Mizotin, M., Benois-Pineau, J., Allard, M., Catheline, G.: Feature-based brain MRI retrieval for Alzheimer disease diagnosis. In: 19th IEEE International Conference on Image Processing (ICIP), pp. 1241–1244 (2012)Google Scholar
  10. 10.
    Simonyan, K., Modat, M., Ourselin, S., Cash, D., Criminisi, A., Zisserman, A.: Immediate ROI search for 3-D medical images. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 56–67. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Gerardin, E., Gaël, C., Marie, C., Rémi, C., Béatrice, D., Ho-Sung, K., Marc, N., 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)CrossRefGoogle Scholar
  12. 12.
    Lötjönen, J., Robin, W., Juha, K., Valtteri, J., Lennart, T., Roger, L., Gunhild, W., Hilkka, S., Daniel, R.: Fast and robust extraction of hippocampus from MR images for diagnostics of Alzheimer’s disease. Neuroimage 56(1), 185–196 (2011)CrossRefGoogle Scholar
  13. 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. Arch. Neurol. 68(8), 1040–1048 (2011)CrossRefGoogle Scholar
  14. 14.
    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)CrossRefGoogle Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    Liu, S., Cai, W., Song, Y., Pujol, S., Kikinis, R., Feng, D.: A bag of semantic words model for medical content-based retrieval. In: MICCAI Workshop on Medical Content-based Retrieval for Clinical Decision Support (2013)Google Scholar
  17. 17.
    Eskildsen, S.F., Coupé, P., Fonov, V.S., Pruessner, J.C., Collins, D.L., and Alzheimer's Disease Neuroimaging Initiative: Structural imaging biomarkers of Alzheimer's disease: predicting disease progression. Neurobiology of aging 36, S23-S31 (2015)Google Scholar
  18. 18.
    Qian, Y., Gao, X., Loomes, M., Comley, R., Barn, B., Hui, R., Tian, Z.: Content-based re-trieval of 3D medical images. In: eTELEMED 2011, 3rd International Conference on eHealth, Telemedicine, and Social Medicine, pp. 7–12 (2011)Google Scholar
  19. 19.
    Lötjönen, J.M., Wolz, R., Koikkalainen, J.R., Thurfjell, L., Waldemar, G., Soininen, H., Rueckert, D.: Fast and robust multi-atlas segmentation of brain magnetic resonance images. Neuroimage 49(3), 2352–2365 (2010)CrossRefGoogle Scholar
  20. 20.
    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)CrossRefGoogle Scholar
  21. 21.
    Chupin, A., Hammer, A., Liu, R.S., Colliot, O., Burdett, J., Bardinet, E., Duncan, J.S., Garnero, L., Lemieux, L.: Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Neuroimage 46(3), 749–761 (2009)CrossRefGoogle Scholar
  22. 22.
    Velayudhan, L., Proitsi, P., Westman, E., Muehlboeck, J.S., Mecocci, P., Vellas, B., et al.: Entorhinal cortex thickness predicts cognitive decline in Alzheimer’s disease. J. Alzheimers Dis. 33(3), 755–766 (2013)Google Scholar
  23. 23.
    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), 024–2037 (2011)CrossRefGoogle Scholar
  24. 24.
  25. 25.
    Hall, M.A., Holmes, G.: Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans. Knowl. Data Eng. 15(6), 1437–1447 (2003)CrossRefGoogle Scholar
  26. 26.
  27. 27.
    Trojacanec, K., Kitanovski, I., Dimitrovski, I., Loshkovska, S.: Content based retrieval of MRI based on brain structure changes in Alzheimer’s disease. In: Proceedings of the International Conference on Bioimaging, pp. 13–22. doi: 10.5220/0005182200130022 (2015)

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Katarina Trojacanec
    • 1
    Email author
  • Ivan Kitanovski
    • 1
  • Ivica Dimitrovski
    • 1
  • Suzana Loshkovska
    • 1
  • for the Alzheimer’s Disease Neuroimaging Initiative*
  1. 1.Faculty of Computer Science and EngineeringSs. Cyril and Methodius UniversitySkopjeMacedonia

Personalised recommendations