Heat Diffusion Based Dissimilarity Analysis for Schizophrenia Classification

  • Aydın Ulaş
  • Umberto Castellani
  • Vittorio Murino
  • Marcella Bellani
  • Michele Tansella
  • Paolo Brambilla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7036)


We apply shape analysis by means of heat diffusion and we show that dissimilarity space constructed using the features extracted from heat diffusion present a promising way of discriminating between schizophrenic patients and healthy controls. We use 30 patients and 30 healthy subjects and we show the effect of several dissimilarity measures on the classification accuracy of schizophrenia using features extracted by heat diffusion. As a novel approach, we propose an adaptation of random subspace method to select random subsets of bins from the original histograms; and by combining the dissimilarity matrices computed by this operation, we enrich the dissimilarity space and show that we can achieve higher accuracies.


heat diffusion schizophrenia dissimilarity space support vector machines random subspace 


  1. 1.
    Agarwal, N., Port, J.D., Bazzocchi, M., Renshaw, P.F.: Update on the use of MR for assessment and diagnosis of psychiatric diseases. Radiology 255(1), 23–41 (2010)CrossRefGoogle Scholar
  2. 2.
    American Psychiatric Association: Diagnostic and statistical manual of mental disorders. DSM-IV. Washington DC, 4th edn. (1994)Google Scholar
  3. 3.
    Baiano, M., Perlini, C., Rambaldelli, G., Cerini, R., Dusi, N., Bellani, M., Spezzapria, G., Versace, A., Balestrieri, M., Mucelli, R.P., Tansella, M., Brambilla, P.: Decreased entorhinal cortex volumes in schizophrenia. Schizophrenia Research 102(1-3), 171–180 (2008)CrossRefGoogle Scholar
  4. 4.
    Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)zbMATHGoogle Scholar
  5. 5.
    Bronstein, A.M., Bronstein, M.M., Ovsjanikov, M., Guibas, L.J.: Shape recognition with spectral distances. IEEE Trans. Pattern Analysis and Machine Intelligence 33(5), 1065–1071 (2011)CrossRefGoogle Scholar
  6. 6.
    Carli, A., Castellani, U., Bicego, M., Murino, V.: Dissimilarity-based representation for local parts. In: 2010 2nd International Workshop on Cognitive Information Processing (CIP), pp. 299–303 (June 2010)Google Scholar
  7. 7.
    Castellani, U., Mirtuono, P., Murino, V., Bellani, M., Rambaldelli, G., Tansella, M., Brambilla, P.: A new shape diffusion descriptor for brain classification. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 426–433. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Cha, S.-H., Srihari, S.N.: On measuring the distance between histograms. Pattern Recognition 35(6), 1355–1370 (2002)CrossRefzbMATHGoogle Scholar
  9. 9.
    Corradi-DellAcqua, C., Tomelleri, L., Bellani, M., Rambaldelli, G., Cerini, R., Pozzi-Mucelli, R., Balestrieri, M., Tansella, M., Brambilla, P.: Thalamic-insular dysconnectivity in schizophrenia: Evidence from structural equation modeling. Human Brain Mapping (in press, 2011)Google Scholar
  10. 10.
    Gerig, G., Styner, M.A., Shenton, M.E., Lieberman, J.A.: Shape Versus Size: Improved Understanding of the Morphology of Brain Structures. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 24–32. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    Giuliani, N.R., Calhouna, V.D., Pearlson, G.D., Francis, A., Buchanan, R.W.: Voxel-based morphometry versus region of interest: a comparison of two methods for analyzing gray matter differences in schizophrenia. Schizophrenia Research 74(2-3), 135–147 (2005)CrossRefGoogle Scholar
  12. 12.
    Ho, T.K.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)CrossRefGoogle Scholar
  13. 13.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)CrossRefGoogle Scholar
  14. 14.
    Kuncheva, L.I.: Combining pattern classifiers: methods and algorithms. Wiley Interscience (2004)Google Scholar
  15. 15.
    Lee, W.-J., Duin, R.P.W., Loog, M., Ibba, A.: An experimental study on combining euclidean distances. In: 2010 2nd International Workshop on Cognitive Information Processing (CIP), pp. 304–309 (June 2010)Google Scholar
  16. 16.
    Liu, Y., Teverovskiy, L., Carmichael, O., Kikinis, R., Shenton, M.E., Carter, C.S., Stenger, V.A., Davis, S., Aizenstein, H.J., Becker, J.T., Lopez, O.L., Meltzer, C.C.: Discriminative MR Image Feature Analysis for Automatic Schizophrenia and Alzheimer’s Disease Classification. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 393–401. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Pekalska, E., Duin, R.P.W.: The Dissimilarity Representation for Pattern Recognition. Foundations and Applications. World Scientific, Singapore (2005)CrossRefzbMATHGoogle Scholar
  18. 18.
    Pekalska, E., Duin, R.P.W., Paclík, P.: Prototype selection for dissimilarity-based classifiers. Pattern Recognition 39(2), 189–208 (2006)CrossRefzbMATHGoogle Scholar
  19. 19.
    Pękalska, E.z., Skurichina, M., Duin, R.P.W.: Combining fisher linear discriminants for dissimilarity representations. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 117–126. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  20. 20.
    Raviv, D., Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Volumetric heat kernel signatures. In: Workshop on 3D Object Retrieval (2010)Google Scholar
  21. 21.
    Reuter, M., Wolter, F.E., Shenton, M., Niethammer, M.: Laplace-Beltrami eigenvalues and topological features on eigenfuntions for statistical shape analysis. Computed-Aided Design 41(10), 739–755 (2009)CrossRefGoogle Scholar
  22. 22.
    Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. International Journal of Computer Vision 40(2), 99–121 (2000)CrossRefzbMATHGoogle Scholar
  23. 23.
    Rujescu, D., Collier, D.A.: Dissecting the many genetic faces of schizophrenia. Epidemiologia e Psichiatria Sociale 18(2), 91–95 (2009)Google Scholar
  24. 24.
    Serratosa, F., Sanfeliu, A.: Signatures versus histograms: Definitions, distances and algorithms. Pattern Recognition 39(5), 921–934 (2006)CrossRefzbMATHGoogle Scholar
  25. 25.
    Shenton, M.E., Dickey, C.C., Frumin, M., McCarley, R.W.: A review of mri findings in schizophrenia. Schizophrenia Research 49(1-2), 1–52 (2001)CrossRefGoogle Scholar
  26. 26.
    Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi-scale signature based on heat diffusion. In: Proceedings of the Symposium on Geometry Processing, pp. 1383–1392 (2009)Google Scholar
  27. 27.
    Timoner, S.J., Golland, P., Kikinis, R., Shenton, M.E., Grimson, W.E.L., Wells III, W.M.: Performance issues in shape classification. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, pp. 355–362. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  28. 28.
    Ulaş, A., Duin, R.P.W., Castellani, U., Loog, M., Mirtuono, P., Bicego, M., Murino, V., Bellani, M., Cerruti, S., Tansella, M., Brambilla, P.: Dissimilarity-based detection of schizophrenia. International Journal of Imaging Systems and Technology 21(2), 179–192 (2011)CrossRefGoogle Scholar
  29. 29.
    Vapnik, V.N.: Statistical learning theory. John Wiley and Sons (1998)Google Scholar
  30. 30.
    Yushkevich, P., Dubb, A., Xie, Z., Gur, R., Gur, R., Gee, J.: Regional structural characterization of the brain of schizophrenia patients. Academic Radiology 12(10), 1250–1261 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Aydın Ulaş
    • 1
  • Umberto Castellani
    • 1
  • Vittorio Murino
    • 1
    • 2
  • Marcella Bellani
    • 3
  • Michele Tansella
    • 3
  • Paolo Brambilla
    • 4
  1. 1.Department of Computer ScienceUniversity of VeronaVeronaItaly
  2. 2.Istituto Italiano di Tecnologia (IIT)GenovaItaly
  3. 3.Department of Public Health and Community MedicineVeronaItaly
  4. 4.IRCCS “E. Medea” Scientific InstituteUdineItaly

Personalised recommendations