Anisotropic Dynamic-Morphological-Diffusion for Segmentation of Noisy Color Images
Junji Maeda*, Takehiro Harada**, Sato Saga*,
and Yukinori Suzuki*
*Graduate School of Engineering, Muroran Institute of Technology, 27-1 Mizumoto-cho, Muroran, Hokkaido 050-8585, Japan
**Seiko Epson Corporation, 3-3-5 Owa, Suwa, Nagano 392-8502, Japan
In this paper, we propose a modified anisotropic diffusion algorithm using the dynamicmorphological filtering as a new precise edge-preserving smoothing technique for an accurate segmentation of color images with heavy noise. We incorporate a dynamic selection of multiple structuring elements for each pixel in the morphological filter before the anisotropic diffusion in order to improve the ability of edge-preserving smoothing. As a result, noise and unnecessary details of images are effectively smoothed while preserving small image structures before performing a segmentation algorithm. Several simulated examples are presented that demonstrate the effectiveness of the proposed technique.
and Yukinori Suzuki, “Anisotropic Dynamic-Morphological-Diffusion for Segmentation of Noisy Color Images,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.2, pp. 204-212, 2014.
-  N. R. Pal and S. K. Pal, “A Review on Image Segmentation Techniques,” Pattern Recognition, Vol.26, No.9, pp. 1277-1294, 1993.
-  H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, “Color Image Segmentation: Advances and Prospects,” Pattern Recognition, Vol.34, No.12, pp. 2259-2281, 2001.
-  D. E. Ilea and P. F. Whelan, “Image Segmentation Based on the Integration of Colour-Texture Descriptors-A Review,” Pattern Recognition, Vol.44, No.10-11, pp. 2479-2501, 2011.
-  A. Buades, B. Coll, and J.M.Morel, “A Review of Image Denoising Algorithms, with a New One,” Multiscale Modeling & Simulation, Vol.4, No.2, pp. 490-530, 2005.
-  P. Perona and J. Malik, “Scale-Space and Edge Detection Using Anisotropic Diffusion,” IEEE Trans. Pattern Anal. Mach. Intell., Vol.12, No.7, pp. 629-639, 1990.
-  F. Catté, P.-L. Lions, J.-M. Morel, and T. Coll, “Image Selective Smoothing and Edge Detection by Nonlinear Diffusion,” SIAM J. Numer. Anal., Vol.29, No.1, pp. 182-193, 1992.
-  M. Nitzberg and T. Shiota, “Nonlinear Image Filtering with Edge and Corner Enhancement,” IEEE Trans. Pattern Anal. Mach. Intell., Vol.14, No.8, pp. 826-833, 1992.
-  R.Whitaker and S.M. Pizer, “A Multi-Scale Approach to Nonlinear Diffusion,” CVGIP: Image Understanding, Vol.57, No.1, pp. 99-110, 1993.
-  C. A. Segall and S. T. Acton, “Morphological Anisotropic Diffusion,” Proc. of IEEE Int. Conf. on Image Processing, Vol.3, pp. 348-351, 1997.
-  M. J. Black, G. Sapiro, D. H. Marimont, and D. Heeger, “Robust Anisotropic Diffusion,” IEEE Trans. Image Processing, Vol.7, No.3, pp. 421-432, 1998.
-  J. Maeda, T. Iizawa, T. Ishizaka, and Y. Suzuki, “Segmentation of Natural Images Using Anisotropic Diffusion and Linking of Boundary Edges,” Pattern Recognition, Vol.31, No.12, pp. 1993-1999, 1998.
-  J. Monteil and A. Beghdadi, “A New Interpretation and Improvement of the Nonlinear Anisotropic Diffusion for Image Enhancement,” IEEE Trans. Pattern Anal. Mach. Intell., Vol.21, No.9, pp. 940-946, 1999.
-  H. Ling and A. C. Bovik, “Smoothing Low SNR Molecular Images Via Anisotropic Median-Diffusion,” IEEE Trans. on Medical Imaging, Vol.21, No.4, pp. 377-384, 2002.
-  Y. Wang, L. Zhang, and M. Kilmer, “Local Variance-Controlled Forward-and-Backward Diffusion for Image Enhancement and Noise Reduction,” IEEE Trans. Image Processing, Vol.16, No.7, pp. 1854-1864, 2007.
-  S.-M. Chao and D.-M Tsai, “An Improved Anisotropic Diffusion Model for Detail- and Edge-Preserving Smoothing,” Pattern Recognition Letters, Vol.31, No.13, pp. 2012-2023, 2010.
-  D. Chen, S.MacLachlan, and P. Li, “Iterative Parameter-Choice and Multigrid Methods for Anisotropic Diffusion Denoising,” SIAM J. on Scientific Computing, Vol.33, No.15, pp. 2972-2994, 2011.
-  B. Tang, G. Sapiro, and V. Caselles, “Color Image Enhancement Via Chromaticity Diffusion,” IEEE Trans. Image Processing, Vol.10, No.5, pp. 701-707, 2001.
-  D. E. Ilea and P. F. Whelan, “Adaptive Pre-Filtering Techniques for Colour Image Analysis,” Proc. of Int. Conf. on Machine Vision and Image Processing, pp. 150-157, 2007.
-  F. Åström, M. Felsberg, and R. Lenz, “Color Persistent Anisotropic Diffusion of Images,” Proc. of 17th Scandinavian Conf. on Image Analysis, pp. 262-272, 2011.
-  S. Di. Zenzo, “A Note on the Gradient of aMulti-Image,” Computer Vision, Graphics, and Image Processing, Vol.33, No.1, pp. 116-125, 1986.
-  R. C. Gonzalez and R. E.Woods, “Digital Image Processing,” Third Edition, Pearson Prentice Hall, New Jersey, 2008.
-  E. Aptoula and S. Lefèvre, “A Comparative Study on Multivariate Mathematical Morphology,” Pattern Recognition, Vol.40, No.11, pp. 2914-2929, 2007.
-  J. Song and E. J. Delp, “The Analysis of Morphological Filters with Multiple Structuring Elements,” Computer Vision, Graphics, and Image Processing, Vol.50, No.3, pp. 308-328, 1990.
-  J. Astola, P. Haavisto, and Y. Neuv, “Vector Median Filters,” Proc. of the IEEE, Vol.78, No.4, pp. 678-689, 1990.
-  C. Tomasi and R.Manduchi, “Bilateral Filtering for Gray and Color Images,” Proc. of IEEE Int. Conf. on Computer Vision, pp. 839-846, 1998.
-  K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering,” IEEE Trans. Image Processing, Vol.16, No.8, pp. 2080-2095, 2007.
-  Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, “Image Quality Assessment: from Error Visibility to Structural Similarity,” IEEE Trans. Image Processing, Vol.13, No.4, pp. 600-612, 2004.
-  Online Available: http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/ [Accessed January 31, 2014]
-  J. Weickert, “Coherence-Enhancing Diffusion of Colour Images,” Image and Vision Computing, Vol.17, No.3-4, pp. 201-212, 1999.