An Improved Adaptive Smoothing Method

  • Xin Hu
  • Hui Peng
  • Joseph Kesker
  • Xiang Cai
  • William G. Wee
  • Jing-Huei Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)

Abstract

An improvement of the Chen’s method has been provided through the calculation of a more accurate H map. The H map is the pixel’s contextual inhomogeneity value reflecting its proximity position with respect to an edge feature, and a more accurate H value leads to the more accurate smoothing speed for the pixel. While experiments on 5 real images show slight improvements in SNRs of our method over that of the Chen method, edge features preserving capability has been enhanced with low FARs (false alarm rates) for edge feature extracted from applying the Sobel filter to the image. Furthermore, parameter values have been determined through an exhaustive searching process resulting in the suggestions of h=0.4 and T=4 for practical applications where the original noise free image is not available and/or no viewer to visually make a selection of the final smoothed image as the output.

Keywords

Edge Pixel Edge Feature Smoothing Algorithm Edge Preservation Fuzzy Connectedness 
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.

References

  1. 1.
    Udupa, J.K., Samarasekera, S.: Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation. Graphical Models Image Processing 58(3), 246–261 (1998)CrossRefGoogle Scholar
  2. 2.
    Chen, K.: Adaptive Smoothing via Contextual and Local Discontinuities. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(10), 1552–1567 (2005)CrossRefGoogle Scholar
  3. 3.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Upper Saddle River (2002)Google Scholar
  4. 4.
    Perona, P., Malik, J.: Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Trans. Pattern Analysis and Machine Intelligence 12, 629–639 (1990)CrossRefGoogle Scholar
  5. 5.
    Black, M.J.: Robust Anisotropic Diffusion. IEEE Trans. Image Processing 7, 421–432 (1998)CrossRefGoogle Scholar
  6. 6.
    Saha, P.K., Udupa, J.K.: Scale-Based Diffusive Image Filtering Preserving Boundary Sharpness and Fine Structures. IEEE Trans. Medical Imaging 20, 1140–1155 (2001)CrossRefGoogle Scholar
  7. 7.
    Chen, K.: A feature preserving adaptive smoothing method for early vision. Journal of Pattern Recognition Society 13 (2000)Google Scholar
  8. 8.
    Saha, P.K., Udupa, J.K.: Scale-Based Fuzzy Connected Image Segmentation: Theory, Algorithms, and Validation. Computer Vision and Image Understanding 77, 145–174 (2000)CrossRefGoogle Scholar
  9. 9.
    Saha, P.K., Udupa, J.K.: Scale-based filtering of medical images. In: Proc. SPIE: Medical Imaging, vol. 3979, pp. 735–746 (2000)Google Scholar
  10. 10.
    Saha, P.K., Udupa, J.K.: Optimum Image Thresholding via Class Uncertainty and Region Homogeneity. IEEE Trans. Pattern Analysis and Machine Intelligence 23, 689–706 (2001)CrossRefGoogle Scholar
  11. 11.
    Pednekar, A.S., Kakadiaris, I.A.: Image Segmentation Based on Fuzzy Connectedness Using Dynamic Weights. IEEE Transaction on Image Processing 15(6), 1555–1562 (2006)CrossRefGoogle Scholar
  12. 12.
    Vu, R.H.: Fuzzy algorithms: Application to adipose tissue quantification on MR images. Biomedical Signal Processing and Control 2(3), 239–247 (2007)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Peng, H.: Modification and Implementation of a 2D Context-Sensitive Adaptive Smoothing Algorithm and its Extension to 3D, master thesis in University of Cincinnati (2006)Google Scholar
  14. 14.
    Hou, Y.: Application of a 3D Level Set Method in MRI Surface Segmentation, Master thesis in University of Cincinnati (2005)Google Scholar
  15. 15.
    Sethian, J.A.: Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision and Materials Science. Cambridge University Press, Cambridge (1999)MATHGoogle Scholar
  16. 16.
    McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis: a survey. Medical Image Analasis 1, 91–108 (1996)CrossRefGoogle Scholar
  17. 17.
    Pednekar, A.S., Kakadiaris, I.A.: Image Segmentation Based on Fuzzy Connectedness Using Dynamic Weights. IEEE Transaction on Image Processing 15(6), 1555–1562 (2006)CrossRefGoogle Scholar
  18. 18.
    Herman, G.T., Carvalho, B.M.: Multiseeded segmentation using fuzzy connectedness. IEEE Trans. on Pattern Anal. Mach. Intell. 23, 460–474 (2001)CrossRefGoogle Scholar
  19. 19.
    Vu, R.H.: Fuzzy algorithms: Application to adipose tissue quantification on MR images. Biomedical Signal Processing and Control 2(3), 239–247 (2007)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Bloch, I.: Fuzzy spatial relationships for image processing and interpretation: a review. Image and Vision Computing 23(2), 89–110 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Xin Hu
    • 1
  • Hui Peng
    • 2
  • Joseph Kesker
    • 3
  • Xiang Cai
    • 4
  • William G. Wee
    • 4
  • Jing-Huei Lee
    • 5
  1. 1.Microsoft Corporation, RedmondWashingtonUnited States
  2. 2.Advantest America, INCSanta ClaraUnited States
  3. 3.Sheet Dynamic Ltd.CincinnatiUnited States
  4. 4.Department of Electrical and Computer EngineeringUniversity of CincinnatiCincinnatiUnited States
  5. 5.Department of Biomedical EngineeringUniversity of CincinnatiCincinnatiUnited States

Personalised recommendations