3-D MRI Brain Scan Feature Classification Using an Oct-Tree Representation

  • Akadej Udomchaiporn
  • Frans Coenen
  • Marta García-Fiñana
  • Vanessa Sluming
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8346)


This paper presents a procedure for the classification of specific 3-D features in Magnetic Resonance Imaging (MRI) brain scan volumes. The main contributions of the paper are: (i) a proposed Bounding Box segmentation technique to extract the 3-D features of interest from MRI volumes, (ii) an oct-tree technique to represent the extracted sub-volumes and (iii) a frequent sub-graph mining based feature space mechanism to support classification. The proposed process was evaluated using 210 3-D MRI brain scans of which 105 were from “healthy” people and 105 from epilepsy patients. The features of interest were the left and right ventricles. Both the process and the evaluation are fully described. The results indicate that the proposed process can be effectively used to classify 3-D MRI brain scan features.


Image mining 3-D Magnetic Resonance Imaging (MRI) Image segmentation Oct-tree representation Image classification 


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  1. 1.
    Bramer, M.: Principles of Data Mining. Springer (2007)Google Scholar
  2. 2.
    Burger, W., Burge, M.J.: Digital Image Processing: An algorithmic Introduction Using Java. Springer (2008)Google Scholar
  3. 3.
    Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models-their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar
  4. 4.
    Cortes, C., Vapnik, V.: Support-vector Networks. Machine Learning 20(3), 273–297 (1995)zbMATHGoogle Scholar
  5. 5.
    Da, L., Costa, F., Cesar Jr., R.M.: Shape Analysis and Classification: Theory and Practice. CRC Press (2001)Google Scholar
  6. 6.
    Elsayed, A., Coenen, F., Jiang, C., García-Fiñana, M., Sluming, V.: Corpus Callosum MR Image Classification. In: Proceedings AI 2009, pp. 333–348. Springer (2009)Google Scholar
  7. 7.
    Elsayed, A., Coenen, F., Jiang, C., García-Fiñana, M., Sluming, V.: Corpus Callosum MR Image Classification. Knowledge-Based Systems 23(4), 330–336 (2010)CrossRefGoogle Scholar
  8. 8.
    Hall, M., Frank, E., Holmes, G.: The WEKA Data Mining Software: An Update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)CrossRefGoogle Scholar
  9. 9.
    Ahmad Hijazi, M.H., Jiang, C., Coenen, F., Zheng, Y.: Image Classification for Age-related Macular Degeneration Screening Using Hierarchical Image Decompositions and Graph Mining. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part II. LNCS, vol. 6912, pp. 65–80. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Huan, J., Wang, W., Prins, J.: Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism. In: Proceedings of The Third IEEE International Conference on Data Mining, pp. 549–552. IEEE Comput. Soc. (2003)Google Scholar
  11. 11.
    Inokuchi, A., Washio, T., Motoda, H.: An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. 12.
    Jackins, C.L., Tanimoto, S.L.: Oct-trees and Their Use in Representing Three-dimensional Objects. Computer Graphics and Image Processing 14(3), 249–270 (1980)CrossRefGoogle Scholar
  13. 13.
    Jiang, C., Coenen, F.: Graph-based Image Classification by Weighting Scheme. In: Applications and Innovations in Intelligent System XVI, pp. 63–76 (2009)Google Scholar
  14. 14.
    Long, S., Holder, L.B.: Graph-based Shape Shape Analysis for MRI Classification. International Journal of Knowledge Discovery in Bioinformatics 2(2), 19–33 (2011)CrossRefGoogle Scholar
  15. 15.
    Osher, S., Sethian, J.A.: Fronts Propagating with Curvature-Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations. Journal of Computational Physics 79(1), 12–49 (1988)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers (1993)Google Scholar
  17. 17.
    Rousson, M., Paragios, N., Deriche, R.: Implicit Active Shape Models for 3D Segmentation in MR Imaging. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 209–216. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  18. 18.
    Yan, X.: gSpan: Graph-based Substructure Pattern Mining. In: Proceeding of The IEEE International Conference on Data Mining, pp. 721–724. IEEE Comput. Soc. (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Akadej Udomchaiporn
    • 1
  • Frans Coenen
    • 1
  • Marta García-Fiñana
    • 2
  • Vanessa Sluming
    • 3
  1. 1.Department of Computer ScienceUniversity of LiverpoolLiverpoolUK
  2. 2.Department of BiostatisticsUniversity of LiverpoolLiverpoolUK
  3. 3.School of Health ScienceUniversity of LiverpoolLiverpoolUK

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