Table of Contents Author Guidelines Submit a Manuscript
International Journal of Biomedical Imaging
Volume 2006, Article ID 83847, 27 pages
http://dx.doi.org/10.1155/IJBI/2006/83847

Evolution-Operator-Based Single-Step Method for Image Processing

1Department of Mathematics, College of Natural Science, Michigan State University, MI 48824, USA
2Department of Electrical and Computer Engineering, College of Engineering, Michigan State University, MI 48824-1226, USA
3Department of Radiology and Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA

Received 28 July 2005; Revised 27 October 2005; Accepted 7 November 2005

Academic Editor: Sun Yoo

Copyright © 2006 Yuhui Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. A. O'Sullivan, M. Jiang, X.-M. Ma, and G. Wang, “Axiomatic quantification of multidimensional image resolution,” IEEE Signal Processing Letters, vol. 9, no. 4, pp. 120–122, 2002. View at Google Scholar
  2. A. P. Witkin, “Scale-space filtering,” in Proceedings of 8th International Joint Conference on Artificial Intelligence (IJCAI~'83), pp. 1019–1022, Karlsruhe, West Germany, August 1983.
  3. L. Alvarez, F. Guichard, P.-L. Lions, and J.-M. Morel, “Axioms and fundamental equations of image processing,” Archive for Rational Mechanics and Analysis, vol. 123, no. 3, pp. 199–257, 1993. View at Publisher · View at Google Scholar
  4. M. Nielsen, P. Johansen, O. F. Olsen, and J. Weickert, Eds., Scale-Space Theories in Computer Vision, vol. 1682 of Lecture Notes in Computer Science, Springer, Berlin, Germany, 1999.
  5. R. T. Whitaker and S. M. Pizer, “A multi-scale approach to nonuniform diffusion,” CVGIP: Image Understanding, vol. 57, no. 1, pp. 99–110, 1993. View at Publisher · View at Google Scholar
  6. G. W. Wei and S. Zhao, “Synchronization and information processing by an on-off coupling,” Physical Review E, vol. 65, no. 5, pp. 056210/1–056210/8, 2002. View at Publisher · View at Google Scholar
  7. C. Goodall, “A survey of smoothing techniques,” in Modern Methods of Data Analysis, J. Fox and J. S. Long, Eds., pp. 126–176, Sage, Newbury Park, Calif, USA, 1990. View at Google Scholar
  8. S. Zhao and G. W. Wei, “Jump process for the trend estimation of time series,” Computational Statistics & Data Analysis, vol. 42, no. 1-2, pp. 219–241, 2003. View at Google Scholar
  9. P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629–639, 1990. View at Publisher · View at Google Scholar
  10. M. J. Black, G. Sapiro, D. H. Marimont, and D. Heeger, “Robust anisotropic diffusion,” IEEE Transactions on Image Processing, vol. 7, no. 3, pp. 421–432, 1998. View at Publisher · View at Google Scholar
  11. F. Catté, P.-L. Lions, J.-M. Morel, and T. Coll, “Image selective smoothing and edge detection by nonlinear diffusion,” SIAM Journal on Numerical Analysis, vol. 29, no. 1, pp. 182–193, 1992. View at Google Scholar
  12. S. Kichenassamy, “The Perona-Malik paradox,” SIAM Journal on Applied Mathematics, vol. 57, no. 5, pp. 1328–1342, 1997. View at Publisher · View at Google Scholar
  13. M. Nitzberg and T. Shiota, “Nonlinear image filtering with edge and corner enhancement,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 8, pp. 826–833, 1992. View at Publisher · View at Google Scholar
  14. L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D: Nonlinear Phenomena, vol. 60, no. 1–4, pp. 259–268, 1992. View at Publisher · View at Google Scholar
  15. J. Shah, “A common framework for curve evolution, segmentation and anisotropic diffusion,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '96), pp. 136–142, San Francisco, Calif, USA, June 1996.
  16. S. Teboul, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Variational approach for edge-preserving regularization using coupled PDEs,” IEEE Transactions on Image Processing, vol. 7, no. 3, pp. 387–397, 1998. View at Publisher · View at Google Scholar
  17. F. Torkamani-Azar and K. E. Tait, “Image recovery using the anisotropic diffusion equation,” IEEE Transactions on Image Processing, vol. 5, no. 11, pp. 1573–1578, 1996. View at Publisher · View at Google Scholar
  18. J. Weickert, B. M. T. H. Romeny, and M. A. Viergever, “Efficient and reliable schemes for nonlinear diffusion filtering,” IEEE Transactions on Image Processing, vol. 7, no. 3, pp. 398–410, 1998. View at Publisher · View at Google Scholar
  19. Y.-L. You, W. Xu, A. Tannenbaum, and M. Kaveh, “Behavioral analysis of anisotropic diffusion in image processing,” IEEE Transactions on Image Processing, vol. 5, no. 11, pp. 1539–1553, 1996. View at Publisher · View at Google Scholar
  20. G. W. Wei, “Generalized Perona-Malik equation for image restoration,” IEEE Signal Processing Letters, vol. 6, no. 7, pp. 165–167, 1999. View at Publisher · View at Google Scholar
  21. P. V. Blomgren and T. F. Chan, “Color TV: total variation methods for restoration of vector-valued images,” IEEE Transactions on Image Processing, vol. 7, no. 3, pp. 304–309, 1998. View at Publisher · View at Google Scholar
  22. V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” International Journal of Computer Vision, vol. 22, no. 1, pp. 61–79, 1997. View at Publisher · View at Google Scholar
  23. S. Osher and L. I. Rudin, “Feature-oriented image enhancement using shock filters,” SIAM Journal on Numerical Analysis, vol. 27, no. 4, pp. 919–940, 1990. View at Publisher · View at Google Scholar
  24. S. Osher and L. I. Rudin, “Shocks and other nonlinear filtering applied to image processing,” in Applications of Digital Image Processing XIV, vol. 1567 of Proceedings of SPIE, pp. 414–431, San Diego, Calif, USA, July 1991.
  25. G. Sapiro and D. L. Ringach, “Anisotropic diffusion of multivalued images with applications to color filtering,” IEEE Transactions on Image Processing, vol. 5, no. 11, pp. 1582–1586, 1996. View at Publisher · View at Google Scholar
  26. G. Sapiro, “From active contours to anisotropic diffusion: connections between basic PDE's in image processing,” in Proceedings of IEEE International Conference on Image Processing (ICIP '96), vol. 1, pp. 477–480, Lausanne, Switzerland, September 1996.
  27. T. F. Chan, A. Marquina, and P. Mulet, “High-order total variation-based image restoration,” SIAM Journal on Scientific Computing, vol. 22, no. 2, pp. 503–516, 2000. View at Publisher · View at Google Scholar
  28. Y.-L. You and M. Kaveh, “Fourth-order partial differential equations for noise removal,” IEEE Transactions on Image Processing, vol. 9, no. 10, pp. 1723–1730, 2000. View at Publisher · View at Google Scholar
  29. A. L. Bertozzi and J. B. Greer, “Low-curvature image simplifiers: global regularity of smooth solutions and Laplacian limiting schemes,” Communications on Pure and Applied Mathematics, vol. 57, no. 6, pp. 764–790, 2004. View at Publisher · View at Google Scholar
  30. J. B. Greer and A. L. Bertozzi, “Traveling wave solutions of fourth order PDEs for image processing,” SIAM Journal on Mathematical Analysis, vol. 36, no. 1, pp. 38–68, 2004. View at Publisher · View at Google Scholar
  31. J. B. Greer and A. L. Bertozzi, “H-1 solutions of a class of fourth order nonlinear equations for image processing,” Discrete and Continuous Dynamical Systems, vol. 10, no. 1-2, pp. 349–366, 2004. View at Google Scholar
  32. M. Lysaker, A. Lundervold, and X.-C. Tai, “Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time,” IEEE Transactions on Image Processing, vol. 12, no. 12, pp. 1579–1590, 2003. View at Publisher · View at Google Scholar
  33. G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Image sharpening by flows based on triple well potentials,” Journal of Mathematical Imaging and Vision, vol. 20, no. 1-2, pp. 121–131, 2004. View at Publisher · View at Google Scholar
  34. T. P. Witelski and M. Bowen, “ADI schemes for higher-order nonlinear diffusion equations,” Applied Numerical Mathematics, vol. 45, no. 2-3, pp. 331–351, 2003. View at Publisher · View at Google Scholar
  35. G. W. Wei and Y. Q. Jia, “Synchronization-based image edge detection,” Europhysics Letters, vol. 59, no. 6, pp. 814–819, 2002. View at Publisher · View at Google Scholar
  36. G. Gilboa, N. Sochen, and Y. Y. Zeevi, “Image enhancement segmentation and denoising by time dependent nonlinear diffusion processes,” in Proceedings of IEEE International Conference on Image Processing (ICIP '01), vol. 3, pp. 134–137, Thessaloniki, Greece, October 2001.
  37. R. Deriche, “Fast algorithms for low-level vision,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 1, pp. 78–87, 1990. View at Publisher · View at Google Scholar
  38. T. Lindeberg, “Scale-space for discrete signals,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 3, pp. 234–254, 1990. View at Publisher · View at Google Scholar
  39. M. Nielsen, L. Florack, and R. Deriche, “Regularization, scale-space, and edge detection filters,” Journal of Mathematical Imaging and Vision, vol. 7, no. 4, pp. 291–307, 1997. View at Publisher · View at Google Scholar
  40. D. Zhao and B. Li, “A new implementation of discrete multiscale filtering,” in Proceedings of IEEE International Conference on Image Processing (ICIP '96), vol. 1, pp. 383–385, Lausanne, Switzerland, September 1996.
  41. B. Tremblais and B. Augereau, “A fast multi-scale edge detection algorithm,” Pattern Recognition Letters, vol. 25, no. 6, pp. 603–618, 2004. View at Publisher · View at Google Scholar
  42. E. Bänsch and K. Mikula, “A coarsening finite element strategy in image selective smoothing,” Computing and Visualization in Science, vol. 1, no. 1, pp. 53–61, 1997. View at Google Scholar
  43. J. Kačur and K. Mikula, “Solution of nonlinear diffusion appearing in image smoothing and edge detection,” Applied Numerical Mathematics, vol. 17, no. 1, pp. 47–59, 1995. View at Google Scholar
  44. T. Preußer and M. Rumpf, “An adaptive finite element method for large scale image processing,” in Scale-Space Theories in Computer Vision, M. Nielsen, P. Johansen, O. F. Olsen, and J. Weickert, Eds., vol. 1682 of Lecture Notes in Computer Science, pp. 223–234, Springer, Berlin, Germany, 1999. View at Google Scholar
  45. Z. Krivá and K. Mikula, “An adaptive finite volume scheme for solving nonlinear diffusion equations in image processing,” Journal of Visual Communication and Image Representation, vol. 13, no. 1-2, pp. 22–35, 2002. View at Google Scholar
  46. K. Mikula and N. Ramarosy, “Semi-implicit finite volume scheme for solving nonlinear diffusion equations in image processing,” Numerische Mathematik, vol. 89, no. 3, pp. 561–590, 2001. View at Publisher · View at Google Scholar
  47. J. Fröhlich and J. Weickert, “Image processing using a wavelet algorithm for nonlinear diffusion,” Tech. Rep. 104, Laboratory of Technomathematics, University of Kaiserslautern, Kaiserslautern, Germany, March 1994. View at Google Scholar
  48. F. L. Fontaine and S. Basu, “Wavelet-based solution to anisotropic diffusion equation for edge detection,” International Journal of Imaging Systems and Technology, vol. 9, no. 5, pp. 356–368, 1998. View at Publisher · View at Google Scholar
  49. S. T. Acton, A. C. Bovik, and M. M. Crawford, “Anisotropic diffusion pyramids for image segmentation,” in Proceedings of IEEE International Conference on Image Processing (ICIP~'94), vol. 3, pp. 478–482, Austin, Tex, USA, November 1994.
  50. J. A. Sethian, Level Set Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision, and Materials Science, Cambridge University Press, Cambridge, UK, 1996.
  51. K. Siddiqi, B. B. Kimia, and C.-W. Shu, “Geometric shock-capturing ENO schemes for subpixel interpolation, computation and curve evolution,” Graphical Models and Image Processing, vol. 59, no. 5, pp. 278–301, 1997. View at Publisher · View at Google Scholar
  52. B. Jawerth, P. Lin, and E. Sinzinger, “Lattice Boltzmann models for anisotropic diffusion of images,” Journal of Mathematical Imaging and Vision, vol. 11, no. 3, pp. 231–237, 1999. View at Publisher · View at Google Scholar
  53. U. S. Ranjan and K. R. Ramakrishnan, “A stochastic scale space for multiscale image representation,” in Scale-Space Theories in Computer Vision, M. Nielsen, P. Johansen, O. F. Olsen, and J. Weickert, Eds., vol. 1682 of Lecture Notes in Computer Science, pp. 441–446, Springer, Berlin, Germany, 1999. View at Google Scholar
  54. G. W. Wei, “Discrete singular convolution for the solution of the Fokker-Planck equation,” The Journal of Chemical Physics, vol. 110, no. 18, pp. 8930–8942, 1999. View at Publisher · View at Google Scholar
  55. G. Bao, G. W. Wei, and S. Zhao, “Local spectral time-domain method for electromagnetic wave propagation,” Optics Letters, vol. 28, no. 7, pp. 513–515, 2003. View at Google Scholar
  56. Z. J. Hou and G. W. Wei, “A new approach to edge detection,” Pattern Recognition, vol. 35, no. 7, pp. 1559–1570, 2002. View at Publisher · View at Google Scholar
  57. Z. Shao, G. W. Wei, and S. Zhao, “DSC time-domain solution of Maxwell's equations,” Journal of Computational Physics, vol. 189, no. 2, pp. 427–453, 2003. View at Publisher · View at Google Scholar
  58. G. W. Wei, “Discrete singular convolution for the sine-Gordon equation,” Physica D: Nonlinear Phenomena, vol. 137, no. 3-4, pp. 247–259, 2000. View at Publisher · View at Google Scholar
  59. G. W. Wei, “Vibration analysis by discrete singular convolution,” Journal of Sound and Vibration, vol. 244, no. 3, pp. 535–553, 2001. View at Publisher · View at Google Scholar
  60. G. W. Wei, Y. B. Zhao, and Y. Xiang, “The determination of natural frequencies of rectangular plates with mixed boundary conditions by discrete singular convolution,” International Journal of Mechanical Sciences, vol. 43, no. 8, pp. 1731–1746, 2001. View at Publisher · View at Google Scholar
  61. S. Zhao and G. W. Wei, “Comparison of the discrete singular convolution and three other numerical schemes for solving Fisher's equation,” SIAM Journal on Scientific Computing, vol. 25, no. 1, pp. 127–147, 2003. View at Publisher · View at Google Scholar
  62. Y. C. Zhou and G. W. Wei, “High resolution conjugate filters for the simulation of flows,” Journal of Computational Physics, vol. 189, no. 1, pp. 159–179, 2003. View at Publisher · View at Google Scholar
  63. Y. C. Zhou, B. S. V. Patnaik, D. C. Wan, and G. W. Wei, “DSC solution for flow in a staggered double lid driven cavity,” International Journal for Numerical Methods in Engineering, vol. 57, no. 2, pp. 211–234, 2003. View at Publisher · View at Google Scholar
  64. S. N. Yu, S. Zhao, and G. W. Wei, “Local spectral time splitting method for first- and second-order partial differential equations,” Journal of Computational Physics, vol. 206, no. 2, pp. 727–780, 2005. View at Publisher · View at Google Scholar
  65. P. Lazaridis, G. Debarge, and P. Gallion, “Split-step-Gauss-Hermite algorithm for fast and accurate simulation of soliton propagation,” International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, vol. 14, no. 4, pp. 325–329, 2001. View at Publisher · View at Google Scholar
  66. J. Korevaar, “Pansions and the theory of Fourier transforms,” Transactions of the American Mathematical Society, vol. 91, no. 1, pp. 53–101, 1959. View at Publisher · View at Google Scholar
  67. D. K. Hoffman, N. Nayar, O. A. Sharafeddin, and D. J. Kouri, “Analytic banded approximation for the discretized free propagator,” Journal of Physical Chemistry, vol. 95, no. 21, pp. 8299–8305, 1991. View at Publisher · View at Google Scholar
  68. M. Jiang and G. Wang, “Convergence of the simultaneous algebraic reconstruction technique (SART),” IEEE Transactions on Image Processing, vol. 12, no. 8, pp. 957–961, 2003. View at Publisher · View at Google Scholar
  69. M. Jiang and G. Wang, “Convergence studies on iterative algorithms for image reconstruction,” IEEE Transactions on Medical Imaging, vol. 22, no. 5, pp. 569–579, 2003. View at Publisher · View at Google Scholar
  70. C. Bouman and K. Sauer, “A generalized Gaussian image model for edge-preserving MAP estimation,” IEEE Transactions on Image Processing, vol. 2, no. 3, pp. 296–310, 1993. View at Publisher · View at Google Scholar
  71. G. Demoment, “Image reconstruction and restoration: overview of common estimation structures and problems,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 37, no. 12, pp. 2024–2036, 1989. View at Publisher · View at Google Scholar
  72. S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 721–741, 1984. View at Google Scholar
  73. A. K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, Englewood Cliffs, NJ, USA, 1989.
  74. M. Jiang, G. Wang, M. W. Skinner, J. T. Rubinstein, and M. W. Vannier, “Blind deblurring of spiral CT images,” IEEE Transactions on Medical Imaging, vol. 22, no. 7, pp. 837–845, 2003. View at Publisher · View at Google Scholar
  75. S. G. Mallat, “Multifrequency channel decompositions of images and wavelet models,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 37, no. 12, pp. 2091–2110, 1989. View at Publisher · View at Google Scholar
  76. M. R. Banham, N. P. Galatsanos, H. L. Gonzalez, and A. K. Katsaggelos, “Multichannel restoration of single channel images using a wavelet-based subband decomposition,” IEEE Transactions on Image Processing, vol. 3, no. 6, pp. 821–833, 1994. View at Publisher · View at Google Scholar
  77. P. Charbonnier, L. Blanc-Féraud, and M. Barlaud, “Noisy image restoration using multiresolution Markov random fields,” Journal of Visual Communication and Image Representation, vol. 3, no. 4, pp. 338–346, 1992. View at Publisher · View at Google Scholar
  78. D. L. Donoho and I. M. Johnstone, “Adapting to unknown smoothness via wavelet shrinkage,” Journal of the American Statistical Association, vol. 90, no. 432, pp. 1200–1224, 1995. View at Publisher · View at Google Scholar
  79. J.-L. Starck and A. Bijaoui, “Filtering and deconvolution by the wavelet transform,” Signal Processing, vol. 35, no. 3, pp. 195–211, 1994. View at Publisher · View at Google Scholar
  80. Y. Y. Li and F. Santosa, “A computational algorithm for minimizing total variation in image restoration,” IEEE Transactions on Image Processing, vol. 5, no. 6, pp. 987–995, 1996. View at Publisher · View at Google Scholar
  81. D. C. Dobson and C. R. Vogel, “Convergence of an iterative method for total variation denoising,” SIAM Journal on Numerical Analysis, vol. 34, no. 5, pp. 1779–1791, 1997. View at Publisher · View at Google Scholar
  82. T. F. Chan, G. H. Golub, and P. Mulet, “A nonlinear primal-dual method for total variation-based image restoration,” SIAM Journal on Scientific Computing, vol. 20, no. 6, pp. 1964–1977, 1999. View at Publisher · View at Google Scholar
  83. J. F. Meinel Jr., G. Wang, M. Jiang, T. Frei, M. W. Vannier, and E. A. Hoffman, “Spatial variation of resolution and noise in multi-detector row spiral CT,” Academic Radiology, vol. 10, no. 6, pp. 607–613, 2003. View at Publisher · View at Google Scholar
  84. J. Astola and P. Kuosmanen, Fundamentals of Nonlinear Digital Filtering, CRC Press LLC, Boca Raton, Fla, USA, 1997.
  85. K. Egiazarian, J. Astola, M. Helsingius, and P. Kuosmanen, “Adaptive denoising and lossy compression of images in transform domain,” Journal of Electronic Imaging, vol. 8, no. 3, pp. 233–245, 1999. View at Publisher · View at Google Scholar
  86. S. G. Chang, B. Yu, and M. Vetterli, “Adaptive wavelet thresholding for image denoising and compression,” IEEE Transactions on Image Processing, vol. 9, no. 9, pp. 1532–1546, 2000. View at Publisher · View at Google Scholar
  87. R. R. Coifman and D. L. Donoho, “Translation-invariant de-noising,” in Wavelets and Statistics, vol. 103 of Lecture Notes in Statistics, pp. 125–150, Springer, Heidelberg, Germany, 1995. View at Google Scholar
  88. R. T. Ogden, Essential Wavelets for Statistical Applications and Data Analysis, Birkhäuser Boston, Boston, Mass, USA, 1997.
  89. Z. Shi, G. W. Wei, D. J. Kouri, D. K. Hoffman, and Z. Bao, “Lagrange wavelets for signal processing,” IEEE Transactions on Image Processing, vol. 10, no. 10, pp. 1488–1508, 2001. View at Publisher · View at Google Scholar
  90. B. Vidakovic, Statistical Modeling by Wavelets, Wiley Series in Probability and Statistics, John wiley & Sons, New York, NY, USA, 1999.
  91. J. B. Weaver, Y. S. Xu, D. M. Healy Jr., and L. D. Cromwell, “Filtering noise from images with wavelet transforms,” Magnetic Resonance in Medicine, vol. 21, no. 2, pp. 288–295, 1991. View at Publisher · View at Google Scholar
  92. D. Marr and E. Hildreth, “Theory of edge detection,” Proceedings of the Royal Society of London. Series B, Biological Sciences, vol. 207, no. 1167, pp. 187–217, 1980. View at Publisher · View at Google Scholar
  93. R. M. Haralick and L. T. Watson, “A facet model for image data,” Computer Graphics and Image Processing, vol. 15, no. 2, pp. 113–129, 1981. View at Publisher · View at Google Scholar
  94. V. S. Nalwa and T. O. Binford, “On detecting edges,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 699–714, 1986. View at Google Scholar
  95. J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679–698, 1986. View at Google Scholar
  96. R. Deriche, “Optimal edge detection using recursive filtering,” in Proceedings of 1st IEEE International Conference on Computer Vision (ICCV '87), pp. 501–505, London, UK, June 1987.
  97. G. Deng and J.-C. Pinoli, “Differentiation-based edge detection using the logarithmic image processing model,” Journal of Mathematical Imaging and Vision, vol. 8, no. 2, pp. 161–180, 1998. View at Publisher · View at Google Scholar
  98. R. P. Johnson, “Contrast based edge detection,” Pattern Recognition, vol. 23, no. 3-4, pp. 311–318, 1990. View at Publisher · View at Google Scholar
  99. S. S. Iyengar and W. Deng, “An efficient edge detection algorithm using relaxation labeling technique,” Pattern Recognition, vol. 28, no. 4, pp. 519–536, 1995. View at Publisher · View at Google Scholar
  100. R. Gordon and R. M. Rangayyan, “Feature enhancement of film mammograms using fixed and adaptive neighborhoods,” Applied Optics, vol. 23, no. 4, pp. 560–564, 1984. View at Google Scholar
  101. A. P. Dhawan, G. Buelloni, and R. Gordon, “Enhancement of mammographic features by optimal adaptive neighborhood image processing,” IEEE Transactions on Medical Imaging, vol. MI-5, no. 1, pp. 8–15, 1986. View at Google Scholar
  102. A. P. Dhawan and R. Gordon, “Reply to comments on enhancement of mammographic features by optimal adaptive neighborhood image processing,” IEEE Transactions on Medical Imaging, vol. MI-6, no. 1, pp. 82–83, 1987. View at Google Scholar
  103. A. P. Dhawan and E. Le Royer, “Mammographic feature enhancement by computerized image processing,” Computer Methods and Programs in Biomedicine, vol. 27, no. 1, pp. 23–35, 1988. View at Publisher · View at Google Scholar
  104. P. G. Tahoces, J. Correa, M. Souto, C. Gonzalez, L. Gomez, and J. J. Vidal, “Enhancement of chest and breast radiographs by automatic spatial filtering,” IEEE Transactions on Medical Imaging, vol. 10, no. 3, pp. 330–335, 1991. View at Publisher · View at Google Scholar
  105. H. P. Chan, K. Doi, C. J. Vyborny, K. L. Lam, and R. A. Schmidt, “Computer-aided detection of microcalcifications in mammograms. Methodology and preliminary clinical study,” Investigative Radiology, vol. 23, no. 9, pp. 664–671, 1988. View at Google Scholar