Advertisement

Adaptive Contextual Energy Parameterization for Automated Image Segmentation

  • Josna Rao
  • Ghassan Hamarneh
  • Rafeef Abugharbieh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5875)

Abstract

Image segmentation techniques are predominately based on parameter-laden optimization processes. The segmentation objective function traditionally involves parameters (i.e. weights) that need to be tuned in order to balance the underlying competing cost terms of image data fidelity and contour regularization. In this paper, we propose a novel approach for automatic adaptive energy parameterization. In particular, our contributions are three-fold; 1) We spatially adapt fidelity and regularization weights to local image content in an autonomous manner. 2) We modulate the weight using a novel contextual measure of image quality based on the concept of spectral flatness. 3) We incorporate our proposed parameterization into a general segmentation framework and demonstrate its superiority to two alternative approaches: the best possible spatially-fixed parameterization and the globally optimal spatially-varying, but non- contextual, parameters. Our segmentation results are evaluated on real and synthetic data and produce a reduction in mean segmentation error when compared to alternative approaches.

Keywords

Adaptive regularization contextual weights image segmentation energy minimization adapting energy functional spectral flatness noise estimation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Samsonov, A.A., Johnson, C.R.: Noise-adaptive nonlinear diffusion filtering of MR images with spatially varying noise levels. Magnetic Resonance in Medicine 52(4), 798–806 (2004)CrossRefGoogle Scholar
  2. 2.
    Burnham, K.P., Anderson, D.R.: Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods & Research 33(2), 291–304 (2004)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Akselrod-Ballin, A., Galun, M., Gomori, M.J., Brandt, A., Basri, R.: Prior knowledge driven multiscale segmentation of brain MRI. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 118–126. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Zhao, P., Yu, B.: Stagewise lasso. Journal of Machine Learning Research 8, 2701–2726 (2007)MathSciNetGoogle Scholar
  5. 5.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1988)CrossRefGoogle Scholar
  6. 6.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. International Journal of Computer Vision 22(1), 61–79 (1997)zbMATHCrossRefGoogle Scholar
  7. 7.
    Osher, S.J., Paragios, N.: Geometric Level Set Methods in Imaging, Vision, and Graphics. Springer, Heidelberg (2003)zbMATHGoogle Scholar
  8. 8.
    Pluempitiwiriyawej, C., Moura, J.M.F., Wu, Y.J.L., Ho, C.: STACS: New active contour scheme for cardiac MR image segmentation. IEEE Transactions on Medical Imaging 24(5), 593–603 (2005)CrossRefGoogle Scholar
  9. 9.
    Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vision 70(2), 109–131 (2006)CrossRefGoogle Scholar
  10. 10.
    Barrett, W.A., Mortensen, E.N.: Interactive live-wire boundary extraction. Medical Image Analysis 1, 331–341 (1997)CrossRefGoogle Scholar
  11. 11.
    McIntosh, C., Hamarneh, G.: Is a single energy functional sufficient? Adaptive energy functionals and automatic initialization. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 503–510. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Udupa, J.K., Grevera, G.J.: Go digital, go fuzzy. Pattern Recognition Letters 23(6), 743–754 (2002)zbMATHCrossRefGoogle Scholar
  13. 13.
    Dong, B., Chien, A., Mao, Y., Ye, J., Osher, S.: Level set based surface capturing in 3D medical images. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 162–169. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Kokkinos, I., Evangelopoulos, G., Maragos, P.: Texture analysis and segmentation using modulation features, generative models, and weighted curve evolution. IEEE Trans. Pattern Analysis and Machine Intelligence 31(1), 142–157 (2009)CrossRefGoogle Scholar
  15. 15.
    Malik, J., Belongie, S., Leung, T.K., Shi, J.: Contour and texture analysis for image segmentation. International Journal of Computer Vision 43(1), 7–27 (2001)zbMATHCrossRefGoogle Scholar
  16. 16.
    Erdem, E., Tari, S.: Mumford-Shah regularizer with contextual feedback. Journal of Mathematical Imaging and Vision 33(1), 67–84 (2009)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Jayant, N.S., Noll, P.: Digital Coding of Waveforms. Prentice-Hall, Englewood Cliffs (1984)Google Scholar
  18. 18.
    Taubman, D.S., Marcellin, M.W.: JPEG 2000: Image Compression Fundamentals, Standards and Practice. Kluwer Academic Publishers, Norwell (2001)Google Scholar
  19. 19.
    Poon, K., Hamarneh, G., Abugharbieh, R.: Live-vessel: Extending livewire for simultaneous extraction of optimal medial and boundary paths in vascular images. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 444–451. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    Li, H., Yezzi, A.: Vessels as 4-D curves: Global minimal 4-D paths to extract 3-D tubular surfaces and centerlines. IEEE Transactions on Medical Imaging 26(9), 1213–1223 (2007)CrossRefGoogle Scholar
  21. 21.
    Cohen, L.D., Kimmel, R.: Global minimum for active contour models: A minimal path approach. International Journal of Computer Vision 24(1), 57–78 (1997)CrossRefGoogle Scholar
  22. 22.
    Bagon, S.: Matlab wrapper for graph cut (December 2006)Google Scholar
  23. 23.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE transactions on Pattern Analysis and Machine Intelligence 20(12), 1222–1239 (2001)CrossRefGoogle Scholar
  24. 24.
    Cocosco, C.A., Kollokian, V., Kwan, R.K.S., Evans, A.C.: BrainWeb: Online interface to a 3D MRI simulated brain database. In: Friberg, L., Gjedde, A., Holm, S., Lassen, N.A., Nowak, M. (eds.) Third International Conference on Functional Mapping of the Human Brain, NeuroImage, vol. 5. Academic Press, London (1997)Google Scholar
  25. 25.
    Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Transactions on Image Processing 10(2), 266–277 (2001)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Josna Rao
    • 1
  • Ghassan Hamarneh
    • 2
  • Rafeef Abugharbieh
    • 1
  1. 1.Biomedical Signal and Image Computing LabUniversity of British ColumbiaCanada
  2. 2.Medical Image Analysis LabSimon Fraser UniversityCanada

Personalised recommendations