SciELO - Scientific Electronic Library Online

vol.19 issue2A Super-Resolution Image Reconstruction using Natural Neighbor InterpolationMorphological Filtering Algorithm for Restoring Images Contaminated by Impulse Noise author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand




Related links

  • Have no similar articlesSimilars in SciELO


Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.19 n.2 México Apr./Jun. 2015 



Hierarchical Contour Shape Analysis


Daniel Valdés-Amaro1 y Abhir Bhalerao2


1 Benemérita Universidad Autónoma de Puebla, Faculty of Computer Science, Puebla, México.

2 University of Warwick, Department of Computer Science, Coventry, UK.

Corresponding author is Daniel Valdés-Amaro.


Article received on 31/01/2014.
Accepted on 17/04/2015.



This paper introduces a novel shape representation which performs shape analysis in a hierarchical fashion using Gaussian and Laplacian pyramids. A background on hierarchical shape analysis is given along with a detailed explanation of the hierarchical method, and results are shown on natural contours. A comparison is performed between the new method and our proposed approach using Point Distribution Models with different shape sets. The paper concludes with a discussion and proposes ideas on how the new approach may be extended.

Keywords: Shape analysis, shape representation, Gaussian pyramids, shape models, brain contours.





D. Valdés-Amaro would like to thank SEP-PROMEP (PROMEP/103.5/12/8136) for the financial support given to this research.



1. Aubert-Broche, B., Griffin, M., Pike, G. B., Evans, A. C., & Collins, D. L. (2006). Twenty new digital brain phantoms for creation of validation image data bases. IEEE Transactions on Medical Imaging, Vol. 25, pp. 1410-14163.         [ Links ]

2. Bhalerao, A. & Wilson, R. (2005). Local Shape Modelling Using Warplets. Kälviäinen, H., Parkkinen, J., & Kaarna, A., editors, Image Analysis, 14th Scandinavian Conference, SCIA 2005, Joensuu, Finland, June 19-22, 2005, Proceedings, volume 3540 of Lecture Notes in Computer Science, Springer, pp. 439-448.         [ Links ]

3. Burt, P. J. & Adelson, E. H. (1983). The laplacian pyramid as a compact image code. IEEE Transactions on Communications, Vol. COM-31, No. 4, pp. 532-540.         [ Links ]

4. Cootes, T. F., Edwards, G., & Taylor, C. (1999). Comparing Active Shape Models with Active Appearance Models. Proceedings of the British Machine Vision Conference, BMVC 1999, University of Nottingham, September 13-16, 1999., BMVA Press, pp. 173-182.         [ Links ]

5. Cootes, T. F., Edwards, G. J., & Taylor, C. J. (1998). Active Appearance Models. 5th European Conference on Computer Vision, volume 1407, Springer, Berlin, pp. 484-498.         [ Links ]

6. Cootes, T. F. & Taylor, C. J. (1992). Active Shape Models: Smart Snakes. British Machine Vision Conference, pp. 267-275.         [ Links ]

7. Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1992). Training models of shape from sets of examples. Proc. British Machine Vision Conference, Springer, Berlin, pp. 266-275.         [ Links ]

8. Davatzikos, C., Tao, X., & Shen, D. (2003). Hierarchical active shape models, using the wavelet transform. IEEE Transactions on Medical Imaging, Vol. 22, No. 3, pp. 414-423.         [ Links ]

9. Dietterich, T. G. (2002). Isolated leaves dataset, oregon state university web resource, url:         [ Links ]

10. Fukunaga, K. & Koontz, W. L. G. (1970). Applications of the Karhunen-Loeve expansion to feature selection and ordering. IEEE Transactions on Computers, Vol. C-19, pp. 311-318.         [ Links ]

11. Gower, J. C. (1975). Generalized Procrustes Analysis. Psychometrika, Vol. 40, pp. 33-51.         [ Links ]

12. Mokhtarian, F., Khalili, N., & Yuen, P. (2002). Estimation of error in curvature computation on multi-scale free-form surfaces. International Journal of Computer Vision, Vol. 48, No. 2, pp. 131-149.         [ Links ]

13. Mokhtarian, F. & Mackworth, A. K. (1995). A theory of multiscale, curvature-based shape representation for planar curves. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 14, No. 8, pp. 789-805.         [ Links ]

14. Rao, A., Aljabar, P., & Rueckert, D. (2008). Hierarchical statistical shape analysis and prediction of sub-cortical brain structures. Medical Image Analysis, Vol. 12, pp. 55-68.         [ Links ]

15. Valdes-Amaro, D. A. & Bhalerao, A. (2008). Local Shape Modelling for Brain Morphometry using Curvature Scale Space. McKenna, S. & Hoey, J., editors, Proceedings of the 12th Annual Conference on Medical Image Understanding and Analysis 2008, British Machine Vision Association, pp. 64-68.         [ Links ]

16. Yu, P., Grant, P. E., Qi, Y., Han, X., Ségonne, F., Pienaar, R., Busa, E., Pacheco, J., Makris, N., Buckner, R. L., Golland, P., & Fischl, B. (2007). Cortical Surface Shape Analysis Based on Spherical Wavelets. IEEE Trans. Medical Imaging, Vol. 26, No. 4, pp. 582-597.         [ Links ]

17. Zhao, Z., Aylward, S. R., & Teoh, E. K. (2005). A novel 3D Partitioned Active Shape Model for Segmentation of Brain MR Images. Duncan, J. S. & Gerig, G., editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI2005, 8th International Conference, Palm Springs, CA, USA, October 26-29, 2005, Proceedings, Part I, volume 3749 of Lecture Notes in Computer Science, Springer, pp. 221-228.         [ Links ]

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License