Hippocampus Localization Guided by Coherent Point Drift Registration Using Assembled Point Set

  • Anusha Achuthan
  • Mandava Rajeswari
  • Win Mar @ Salmah Jalaluddin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8073)


This paper presents a new approach for hippocampus localization using pairwise non-rigid Coherent Point Drift registration method. The concept of assembled point set is introduced, which is a combination of the available training point sets into a single data space that represents its distribution. Non-rigid Coherent Point Drift is then adapted to register the assembled point set with a randomly chosen base model for hippocampus localization. The primary focus of this work is on the computational intensiveness of the localization approach, in which the proposed localization approach using assembled point set is compared with an existing groupwise non-rigid Coherent Point Drift (GCPD) approach. The computation intensiveness of the proposed approach grows at a quadratic rate as compared with GCPD that grows at a cubic rate. The proposed approach is validated with hippocampus localization task using 40-datasets. The Root Mean Square (RMS) distance between the approximated hippocampus locations and the ground truth is within an acceptable average of 0.6957-mm.


point set registration localization hippocampus 


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  1. 1.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood estimation from incomplete data via the em algorithm. Journal of the Royal Statistical Society 39, 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Myronenko, A., Song, X.: Point set registration: Coherent point drift. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(12), 2262–2275 (2010)CrossRefGoogle Scholar
  3. 3.
    Rasoulian, A., Rohling, R., Abolmaesumi, P.: Group-wise registration of point sets for statistical shape models. IEEE Transactions on Medical Imaging 31(11), 2025–2034 (2012)CrossRefGoogle Scholar
  4. 4.
    Shattuck, D.W., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K.L., Poldrack, R.A., Bilder, R.M., Toga, A.W.: Construction of a 3d probabilistic atlas of human cortical structures. NeuroImage 39(3), 1064–1080 (2008)CrossRefGoogle Scholar
  5. 5.
    Bailleul, J., Ruan, S., Constans, J.M.: Statistical shape model-based segmentation of brain mri images. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2007, pp. 5255–5258 (August 2007)Google Scholar
  6. 6.
    Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-8(6), 679–698 (1986)CrossRefGoogle Scholar
  7. 7.
    Joseph, J., Warton, C., Jacobson, S.W., Jacobson, J.L., Molteno, C.D., Eicher, A., Marais, P., Phillips, O.R., Narr, K.L., Meintjes, E.M.: Three-dimensional surface deformation-based shape analysis of hippocampus and caudate nucleus in children with fetal alcohol spectrum disorders. Human Brain Mapping (2012)Google Scholar
  8. 8.
    Lu, X., Luo, S.: The application of watersnakes algorithm in segmentation of the hippocampus from brain MR image. In: Gao, X., Müller, H., Loomes, M.J., Comley, R., Luo, S. (eds.) MIMI 2007. LNCS, vol. 4987, pp. 277–286. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Cabezas, M., Oliver, A., Lladó, X., Freixenet, J., Cuadra, M.B.: A review of atlas-based segmentation for magnetic resonance brain images. Computer Methods and Programs in Biomedicine 104(3), 158–177 (2011)CrossRefGoogle Scholar
  10. 10.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. PWS Publishing (1999)Google Scholar
  11. 11.
    Balafar, M.A., Ramli, A.R., Saripan, M.I., Mashohor, S.: Review of brain mri image segmentation methods. Artificial Intelligence Review 33, 261–274 (2010)CrossRefGoogle Scholar
  12. 12.
    Amenta, N., Bern, M., Kamvysselis, M.: A new voronoi-based surface reconstruction algorithm. In: Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1998, pp. 415–421. ACM, New York (1998)CrossRefGoogle Scholar
  13. 13.
    Duta, N., Sonka, M.: Segmentation and interpretation of mr brain images using an improved knowledge-based active shape model. In: Duncan, J.S., Gindi, G. (eds.) IPMI 1997. LNCS, vol. 1230, pp. 375–380. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  14. 14.
    Duta, N., Sonka, M.: Segmentation and interpretation of mr brain images: An improved active shape model. IEEE Transactions on Medical Imaging 17(6), 1049–1062 (1998)CrossRefGoogle Scholar
  15. 15.
    Colliot, O., Camara, O., Bloch, I.: Integration of fuzzy spatial relations in deformable models: Application to brain mri segmentation. Pattern Recognition 39(8), 1401–1414 (2006)CrossRefGoogle Scholar
  16. 16.
    Nempont, O., Atif, J., Angelini, E.D., Bloch, I.: Combining radiometric and spatial structural information in a new metric for minimal surface segmentation. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 283–295. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Coupe, P., Manjon, J.V., Fonov, V., Pruessner, J., Robles, M., Collins, D.L.: Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940–954 (2011)CrossRefGoogle Scholar
  18. 18.
    Yushkevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G.: User-guided 3d active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. NeuroImage 31(3), 1116–1128 (2006)CrossRefGoogle Scholar
  19. 19.
    Besl, P.J., McKay, H.D.: A method for registration of 3d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)CrossRefGoogle Scholar
  20. 20.
    Khallaghi, S., et al.: Registration of a statistical shape model of the lumbar spine to 3D ultrasound images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 68–75. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  21. 21.
    Heimann, T., Meinzer, H.: Statistical shape models for 3d medical image segmentation: A review. Medical Image Analysis 13(4), 543–563 (2009)CrossRefGoogle Scholar
  22. 22.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Training models of shape from sets of examples. In: Proc. British Machine Vision Conference 1992. Springer (1992)Google Scholar
  23. 23.
    Crum, W.R., Hartkens, T., Hill, D.L.G.: Non-rigid image registration: theory and practice. The British Journal of Radiology 77, 140–153 (2004)CrossRefGoogle Scholar
  24. 24.
    Tae, W.S., Kim, S.S., Lee, K.U., Nam, E.C., Choi, J.W., Park, J.I.: Hippocampal shape deformation in female patients with unremitting major depressive disorder. American Journal of Neuroradiology 32(4), 671–676 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Anusha Achuthan
    • 1
  • Mandava Rajeswari
    • 1
  • Win Mar @ Salmah Jalaluddin
    • 1
  1. 1.Computer Vision Research Lab, School of Computer SciencesUniversiti Sains MalaysiaPenangMalaysia

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