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Multiple Genetic Snakes for Bone Segmentation

  • Lucia Ballerini
  • Leonardo Bocchi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2611)

Abstract

Clinical assessment of skeletal age is a frequent, but yet difficult and time-consuming task. Automatic methods which estimate the skeletal age from a hand radiogram are currently being studied. This work presents a method to segment each bone complex in the radiogram, using a modified active contour approach. Each bone is modelled by an independent contour, while neighbouring contours are coupled by an elastic force. The optimization of the contour is done using a genetic algorithm. Experimental results, carried out on a portion of the whole radiogram, show that coupling of deformable contours with genetic optimization allows to obtain an accurate segmentation.

Keywords

Genetic Algorithm Active Contour Deformable Model Active Contour Model Hand Radiogram 
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.

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References

  1. 1.
    Greulich, W.W., Pyle, S.I.: Radiographic atlas of skeletal development of the hand and wrist. 2nd edn. Stanford University Press, Palo Alto, CA (1959)Google Scholar
  2. 2.
    Tanner, J.M., Whitehouse, R.H., Marshall, W.A., Healy, M.J.R.: Assessment of skeletal maturity and prediction of adult height (TW2 method). 2nd edn. Academic Press, London (1983)Google Scholar
  3. 3.
    Ballerini, L.: Genetic snakes for medical images segmentation. In: Evolutionary Image Analysis, Signal Processing and Telecommunications. Volume 1596 of Lectures Notes in Computer Science., Springer (1999) 59–73Google Scholar
  4. 4.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1 (1988) 321–331CrossRefGoogle Scholar
  5. 5.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA (1989)zbMATHGoogle Scholar
  6. 6.
    McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis: A survey. Medical Image Analysis 1 (1996) 91–108CrossRefGoogle Scholar
  7. 7.
    Jain, A.K., Zhong, Y., Dubuisson-Jolly, M.P.: Deformable template models: A review. Signal Processing 71 (1998) 109–129zbMATHCrossRefGoogle Scholar
  8. 8.
    Xu, C., Pham, D.L., Prince, J.L.: Image segmentation using deformable models. In Sonka, M., Fitzpatrick, J.M., eds.: Handbook of Medical Imaging. Volume 2. SPIE Press (2000) 129–174Google Scholar
  9. 9.
    Cheung, K.W., Yeung, D.Y., Chin, R.T.: On deformable models for visual patter recognition. Patter Recognition 35 (2002) 1507–1526zbMATHCrossRefGoogle Scholar
  10. 10.
    Storvik, G.: A bayesian approach to dynamic contours through stochastic sampling and simulated annealing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16 (1994) 976–986CrossRefGoogle Scholar
  11. 11.
    Grzeszczuk, R.P., Levin, D.N.: Brownian strings: Segmenting images with stochastically deformable contours. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997) 110–1114CrossRefGoogle Scholar
  12. 12.
    Amini, A., Weymouth, T., Jain, R.: Using dynamic programming for solving variational problems in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (1990) 855–867CrossRefGoogle Scholar
  13. 13.
    Geiger, D., Gupta, A., Costa, L., Vlontzos, J.: Dynamic programming for detecting, tracking and matching deformable contours. IEEE Transactions on Pattern Analysis and Machine Intelligence 17 (1995) 294–302CrossRefGoogle Scholar
  14. 14.
    Williams, D.J., Shah, M.: A fast algorithms for active contours and curvature estimation. CVGIP: Image Understanding 55 (1992) 14–26zbMATHCrossRefGoogle Scholar
  15. 15.
    Ji, L., Yan, H.: Attractable snakes based on the greedy algorithm for contour extraction. Pattern Recognition 33 (2002) 791–806CrossRefGoogle Scholar
  16. 16.
    MacEachern, L.A., Manku, T.: Genetic algorithms for active contour optimization. In: Proc. IEEE International Symposium on Circuits and Systems. Volume 4. (1998) 229–232Google Scholar
  17. 17.
    Tanatipanond, T., Covavisaruch, N.: An improvement of multiscale approach to deformable contour for brain MR images by genetic algorithms. In: Proc. IEEE International Symposium on Intelligent Signal Processing and Communication Systems, Phucket, Thailand (1999) 677–680Google Scholar
  18. 18.
    Ooi, C., Liatsis, P.: Co-evolutionary-based active contour models in tracking of moving obstacles. In: Proc. International Conference on Advanced Driver Assistance Systems. (2001) 58–62Google Scholar
  19. 19.
    Toet, A., Hajema, W.P.: Genetic contour matching. Pattern Recognition Letters 16 (1995) 849–856CrossRefGoogle Scholar
  20. 20.
    Cootes, T., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Computer Vision and Image Understanding 61 (1995) 38–59CrossRefGoogle Scholar
  21. 21.
    Ru., C.F., Hughes, S.W., Hawkes, D.J.: Volume estimation from sparse planar images using deformable models. Image and Vision Computing 17 (1999) 559–565CrossRefGoogle Scholar
  22. 22.
    Undrill, P.E., Delibasis, K., Cameron, G.G.: An application of genetic algorithms to geometric model-guided interpretation of brain anatomy. Pattern Recognition 30 (1997) 217–227CrossRefGoogle Scholar
  23. 23.
    Mignotte, M., Collet, C., Pèrez, P., Bouthemy, P.: Hybrid genetic optimization and statistical model-based approach for the classification of shadow shapes in sonar images. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (2000) 129–141CrossRefGoogle Scholar
  24. 24.
    Cagnoni, S., Dobrzeniecki, A.B., Poli, R., Yanch, J.C.: Genetic algorithm-based interactive segmentation of 3D medical images. Image and Vision Computing 17 (1999) 881–895CrossRefGoogle Scholar
  25. 25.
    Schraudolph, N.N., Grefenstette, J.J.: A user’s guide to GAucsd 1.4. Technical Report CS92-249, Computer Science and Engineering Department, University of California, San Diego, La Jolla, CA (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Lucia Ballerini
    • 1
  • Leonardo Bocchi
    • 2
  1. 1.Dept. of Electronics and TelecommunicationsUniversity of FlorenceFirenzeItaly
  2. 2.Dept. of TechnologyÖrebro UniversityÖrebroSweden

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