Content Based Retrieval of MRI Based on Brain Structure Changes in Alzheimer’s Disease

Katarina Trojacanec, Ivan Kitanovski, Ivica Dimitrovski, Suzana Loshkovska


The aim of the paper is to present Content Based Retrieval of MRI based on the brain structure changes characteristic for Alzheimer’s Disease (AD). The approach used in this paper aims to improve the retrieval performance while using smaller number of features in comparison to the descriptor dimensionality generated by the traditional feature extraction techniques. The feature vector consists of the measurements of cortical and subcortical brain structures, including volumes of the brain structures and cortical thickness. Two main stages are required to obtain these features: segmentation and calculation of the quantitative measurements. The feature subset selection is additionally applied using Correlation-based Feature Selection (CFS) method. Euclidean distance is used as a similarity measurement. The retrieval performance is evaluated using MRIs 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.


  1. Accomazzi V., Lazarowich R., Barlow, C. J., and Davey, B., 2009. U.S. Patent No. 7,596,267. Washington, DC: U.S. Patent and Trademark Office.
  2. Agarwal M., and Mostafa J., 2010 Image Retrieval for Alzheimer's Disease Detection. Medical ContentBased Retrieval for Clinical Decision Support. Springer Berlin Heidelberg. pp. 49-60.
  3. Agarwal, M., and Mostafa, J., 2011 Content-based image retrieval for Alzheimer's disease detection. In ContentBased Multimedia Indexing (CBMI), 2011 9th International Workshop on pp: 13-18.
  4. Akgül, C. B., Ünay, D., and Ekin, A., 2009. Automated diagnosis of Alzheimer's disease using image similarity and user feedback. In Proceedings of the ACM International Conference on Image and Video Retrieval, pp. 34.
  5. Akgül C. B., Rubin, D. L., Napel, S., Beaulieu, C. F., Greenspan, H., Acar, B., 2011. Content-based image retrieval in radiology: current status and future directions. Journal of Digital Imaging, vol. 24 no. 2, pp. 208-222.
  6. Cataldo R, Agrusti A, De Nunzio G, Carlà A, De Mitri I, Favetta M, Quarta M, Monno L, Rei L, Fiorina E; Alzheimer's Disease Neuroimaging Initiative, 2013. Generating a minimal set of templates for the hippocampal region in MR neuroimages. Journal of Neuroimaging 23, no. 3 pp. 473-483.
  7. Chupin M., Gérardin E., Cuingnet R., Boutet C, Lemieux L., Lehéricy S., Benali H., Garnero L., and Colliot O., 2009a. Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI. Hippo-campus 19, no. 6 pp: 579-587.
  8. Chupin A., Hammer A., Liu R.S., Colliot O., Burdett J., Bardinet E., Duncan J.S., Garnero L., Lemieux L., 2009b. Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: method and validation. Neuroimage. vol. 46, no. 3:749-761.
  9. Cuingnet R., Gerardin E., Tessieras J., Auzias G., Lehéricy S., Habert M. O., Chupin M., Benali H., and Colliot O., 2011. Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 56, no. 2 pp. 766-781.
  10. Depeursinge, A., Zrimec, T., Busayarat, S., Müller, H., 2011. 3D lung image retrieval using localized features. In SPIE Medical Imaging, International Society for Optics and Photonics, pp. 79632E-79632E.
  11. FreeSurfer, 2013. Available from: <https://surfer.nmr.>. [25.08.2014]
  12. FreeSurfer methods, 2014. Available from: <http://surfer. on>. [25.08.2014].
  13. Gerardin E, Gaël C., Marie C., Rémi C., Béatrice D., HoSung K., Marc N. et al., 2009. Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging. Neuroimage 47, no. 4, pp. 1476-1486.
  14. Gray, K. R., Aljabar, P., Heckemann, R. A., Hammers, A., and Rueckert, D., 2013. Random forest-based similarity measures for multi-modal classification of Alzheimer's disease. NeuroImage, 65, pp: 167-175.
  15. Hall, M. A., & Holmes, G., 2003. Benchmarking attribute selection techniques for discrete class data mining. Knowledge and Data Engineering, IEEE Transactions on, 15(6), 1437-1447.
  16. Heckemann R. A., Keihaninejad S, Aljabar P., Gray K. R., Nielsen C, Rueckert D., Hajnal J. V., and Hammers A, 2011. Automatic morphometry in Alzheimer's disease and mild cognitive impairment." Neuroimage 56, no. 4 p.: 024-2037.
  17. Leonardo I., 2011. Atrophy Measurement Biomarkers using Structural MRI for Alzheimer's Disease. The 15th Int. Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI).
  18. Liu, S., Cai, W., Song, Y., Pujol, S., Kikinis, R., & Feng, D., 2013. A Bag of Semantic Words Model for Medical Content-based Retrieval. In MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support.
  19. Lötjönen, J. M., Wolz, R., Koikkalainen, J. R., Thurfjell, L., Waldemar, G., Soininen, H., and Rueckert, D., 2010. Fast and robust multi-atlas segmentation of brain magnetic resonance images. Neuroimage, vol. 49, no. 3, 2352-2365.
  20. Lötjönen J., Robin W., Juha K., Valtteri J., Lennart T., Roger L., Gunhild W., Hilkka S., and Daniel R., 2011 Fast and robust extraction of hippocampus from MR images for diagnostics of Alzheimer's disease. Neuroimage 56, no. 1, pp. 185-196.
  21. Mizotin, M., Benois-Pineau, J., Allard, M., and Catheline, G., 2012. Feature-based brain MRI retrieval for Alzheimer disease diagnosis. In Image Processing (ICIP), 19th IEEE International Conference on pp. 1241-1244.
  22. Moore D. W., Kovanlikaya I., Heier L. A., Raj A., Huang C., Chu K. W., and Relkin N. R., 2011 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.
  23. Nestor S. M., Raul R., Michael B., Matthew S., Vittorio A., Jennie L. W., Jennifer F., and Robert B., 2008. Ventricular enlargement as a possible measure of Alzheimer's disease progression validated using the Alzheimer's disease neuroimaging initiative database. Brain 131, no. 9 pp: 2443-2454.
  24. Nho, K., Risacher, L. S., Crane, P. K., DeCarli, C., Glymour, M.M., Habeck, C., Kim, S. et al., 2012. Voxel and surface-based topography of memory and executive deficits in mild cognitive impairment and Alzheimer's disease. Brain imaging and behavior vol. 6, no. 4 pp. 551-567.
  25. Oliveira, M. C., Cirne, W., and de Azevedo Marques, P. M., 2007. Towards applying content-based image retrieval in the clinical routine. Future Generation Computer Systems, vol. 23, no. 3, pp. 466-474.
  26. Qian, Y., Gao, X., Loomes, M., Comley, R., Barn, B., Hui, R., Tian, Z., 2011. Content-based re-trieval of 3D medical images. In eTELEMED 2011, The Third International Conference on eHealth, Telemedicine, and Social Medicine, pp. 7-12.
  27. Rosset A., Muller H., Martins M., Dfouni N., Vallée J.-P., Ratib O., 2004. Casimage project - a digital teaching files authoring environment, Journal of Thoracic Imaging vol. 19 no. 2, 1-6.
  28. Sabuncu, M. R., Desikan R. S., Sepulcre J., Yeo B. T. T, Liu H., Schmansky N. J., Reuter M. et al., 2011. The dynamics of cortical and hippocampal atrophy in Alzheimer disease. Archives of neurology 68, no. 8 pp: 1040-1048.
  29. Simonyan, K., Modat, M., Ourselin, S., Cash, D., Criminisi, A., Zisserman, 2013. A. Immediate ROI search for 3-d medical images. In: Medical ContentBased Retrieval for Clinical Decision Support, pp. 56- 67, Springer Berlin Heidelberg.
  30. Velayudhan, L., Proitsi, P., Westman, E., Muehlboeck, J. S., Mecocci, P., Vellas, B.,et al., 2013. Entorhinal cortex thickness predicts cognitive decline in Alzheimer's disease. Journal of Alzheimer's Disease, vol. 33, no. 3, pp. 755-766.
  31. Yuan, L., Wang, Y., Thompson, P. M., Narayan, V. A., and Ye, J., 2011. Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data. Neu-roImage, 61(3), pp: 622-632.

Paper Citation

in Harvard Style

Trojacanec K., Kitanovski I., Dimitrovski I. and Loshkovska S. (2015). Content Based Retrieval of MRI Based on Brain Structure Changes in Alzheimer’s Disease . In Proceedings of the International Conference on Bioimaging - Volume 1: BIOIMAGING, (BIOSTEC 2015) ISBN 978-989-758-072-7, pages 13-22. DOI: 10.5220/0005182200130022

in Bibtex Style

author={Katarina Trojacanec and Ivan Kitanovski and Ivica Dimitrovski and Suzana Loshkovska},
title={Content Based Retrieval of MRI Based on Brain Structure Changes in Alzheimer’s Disease},
booktitle={Proceedings of the International Conference on Bioimaging - Volume 1: BIOIMAGING, (BIOSTEC 2015)},

in EndNote Style

JO - Proceedings of the International Conference on Bioimaging - Volume 1: BIOIMAGING, (BIOSTEC 2015)
TI - Content Based Retrieval of MRI Based on Brain Structure Changes in Alzheimer’s Disease
SN - 978-989-758-072-7
AU - Trojacanec K.
AU - Kitanovski I.
AU - Dimitrovski I.
AU - Loshkovska S.
PY - 2015
SP - 13
EP - 22
DO - 10.5220/0005182200130022