Bag of Features for Automatic Classification of Alzheimer’s Disease in Magnetic Resonance Images

  • Andrea Rueda
  • John Arevalo
  • Angel Cruz
  • Eduardo Romero
  • Fabio A. González
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)


The goal of this paper is to evaluate the suitability of a bag-of-feature representation for automatic classification of Alzheimer’s disease brain magnetic resonance (MR) images. The evaluated method uses a bag-of-features (BOF) to represent the MR images, which are then fed to a support vector machine, which has been trained to distinguish between normal control and Alzheimer’s disease. The method was applied to a set of images from the OASIS data set. An exhaustive exploration of different BOF parameters was performed, i.e. feature extraction, dictionary construction and classification model. The experimental results show that the evaluated method reaches competitive performance in terms of accuracy, sensibility and specificity. In particular, the method based on a BOF representation outperforms the best published result in this data set improving the equal error classification rate in about 10% (0.80 to 0.95 for Group 1 and 0.71 to 0.81 for Group 2).


Support Vector Machine Patch Size Visual Word Dictionary Size Visual Dictionary 
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.


  1. 1.
    Ashburner, J., Friston, K.J.: Voxel-based morphometry–the methods. Neuroimage 11(6), 805–821 (2000)CrossRefGoogle Scholar
  2. 2.
    Ashburner, J., Hutton, C., Frackowiak, R., Johnsrude, I., Price, C., Friston, K.: et al. Identifying global anatomical differences: deformation-based morphometry. Human Brain Mapping 6(5-6), 348–357 (1998)CrossRefGoogle Scholar
  3. 3.
    Avni, U., Greenspan, H., Sharon, M., Konen, E., Goldberger, J.: X-ray image categorization and retrieval using patch-based visualwords representation. In: IEEE International Symposium on Biomedical Imaging, ISBI 2009, pp. 350–353. IEEE (2009)Google Scholar
  4. 4.
    Buckner, R.L., Head, D., Parker, J., Fotenos, A.F., Marcus, D., Morris, J.C., Snyder, A.Z.: A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. Neuroimage 23(2), 724–738 (2004)CrossRefGoogle Scholar
  5. 5.
    Caicedo, J.C., Cruz, A., Gonzalez, F.A.: Histopathology Image Classification Using Bag of Features and Kernel Functions. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds.) AIME 2009. LNCS, vol. 5651, pp. 126–135. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Chung, M.K., Worsley, K.J., Paus, T., Cherif, C., Collins, D.L., Giedd, J.N., Rapoport, J.L., Evans, A.C.: A unified statistical approach to deformation-based morphometry. NeuroImage 14(3), 595–606 (2001)CrossRefGoogle Scholar
  7. 7.
    Cruz-Roa, A., Caicedo, J.C., González, F.A.: Visual pattern mining in histology image collections using bag of features. In: Artificial Intelligence in Medicine (2011)Google Scholar
  8. 8.
    Cruz-Roa, A., Díaz, G., Romero, E., González, F.A.: Automatic Annotation of Histopathological Images Using a Latent Topic Model Based On Non-negative Matrix Factorization. J. Path Inform. 2(1), 4 (2011)CrossRefGoogle Scholar
  9. 9.
    Diaz, G., Romero, E.: Micro-structural tissue analysis for automatic histopathological image annotation. Microscopy Research and Technique, pp. 343–358 (2011)Google Scholar
  10. 10.
    Lao, Z., Shen, D., Xue, Z., Karacali, B., Resnick, S.M., Davatzikos, C.: Morphological classification of brains via high-dimensional shape transformations and machine learning methods. NeuroImage 21(1), 46–57 (2004)CrossRefGoogle Scholar
  11. 11.
    Mangin, J.F., Riviere, D., Cachia, A., Duchesnay, E., Cointepas, Y., Papadopoulos-Orfanos, D., Collins, D.L., Evans, A.C., Régis, J.: Object-based morphometry of the cerebral cortex. IEEE Transactions on Medical Imaging 23(8), 968–982 (2004)CrossRefGoogle Scholar
  12. 12.
    Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies (oasis): cross-sectional mri data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience 19(9), 1498–1507 (2007)CrossRefGoogle Scholar
  13. 13.
    Nestor, P.J., Scheltens, P., Hodges, J.R.: Advances in the early detection of alzheimer’s disease. Nature Reviews Neuroscience (2004)Google Scholar
  14. 14.
    Pohl, K.M., Sabuncu, M.R.: A Unified Framework for MR Based Disease Classification. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds.) IPMI 2009. LNCS, vol. 5636, pp. 300–313. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Studholme, C., Drapaca, C., Iordanova, B., Cardenas, V.: Deformation-based mapping of volume change from serial brain mri in the presence of local tissue contrast change. IEEE Transactions on Medical Imaging 25(5), 626–639 (2006)CrossRefGoogle Scholar
  16. 16.
    Thompson, P.M., Giedd, J.N., Woods, R.P., MacDonald, D., Evans, A.C., Toga, A.W., et al.: Growth patterns in the developing brain detected by using continuum mechanical tensor maps. Nature 404(6774), 190–192 (2000)CrossRefGoogle Scholar
  17. 17.
    Toews, M., Wells III, W., Collins, D.L., Arbel, T.: Feature-based morphometry: Discovering group-related anatomical patterns. NeuroImage 49(3), 2318–2327 (2010)CrossRefGoogle Scholar
  18. 18.
    Toga, A.W., Thompson, P.M., Mega, M.S., Narr, K.L., Blanton, R.E.: Probabilistic approaches for atlasing normal and disease-specific brain variability. Anatomy and Embryology 204(4), 267–282 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andrea Rueda
    • 1
  • John Arevalo
    • 1
  • Angel Cruz
    • 1
  • Eduardo Romero
    • 1
  • Fabio A. González
    • 1
  1. 1.BioIngenium Research GroupUniversidad Nacional de ColombiaBogotáColombia

Personalised recommendations