Adaptive Gradient-Based and Anisotropic Diffusion Equation Filtering Algorithm for Microscopic Image Preprocessing


In image acquisition process, the quality of microscopic images will be degraded by electrical noise, quantizing noise, light illumination etc. Hence, image preprocessing is necessary and important to improve the quality. The background noise and pulse noise are two common types of noise existing in microscopic images. In this paper, a gradient-based anisotropic filtering algorithm was proposed, which can filter out the background noise while preserve object boundary effectively. The filtering performance was evaluated by comparing that with some other filtering algorithms.

Share and Cite:

H. Liu, "Adaptive Gradient-Based and Anisotropic Diffusion Equation Filtering Algorithm for Microscopic Image Preprocessing," Journal of Signal and Information Processing, Vol. 4 No. 1, 2013, pp. 82-87. doi: 10.4236/jsip.2013.41010.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] V. Khryashchev and A. L. Priorov, “Image Denoising Using Adaptive Switching Median Filter,” IEEE International Conference on Image Processing, Vol. 1, 2005, pp. 117-120.
[2] K. NallaperUmal, J. Varghese and S. Saudia, “Adaptive Rank-Ordered Switching Median Filter for Salt & Pepper Impulse Noise Reduction,” IEEE Annual India Conference, New Delhi, 15-17 September 2006, pp. 1-6.
[3] Y. C. Wang, D. Q. Liang, H. Ma and Y. Wang, “An Algorithm for Image Denoising Based on Mixed Filter,” Proceedings of the 6th World Congress on Intelligent Control and Automation, Vol. 2, 2006, pp. 9690-9693.
[4] Y. J. Yu and S. T. Acton, “Speckle Reducing Anisotropic Diffusion,” IEEE Transaction on Image Processing, Vol. 11, No. 11, 2002, pp. 1260-1269. doi:10.1109/TIP.2002.804276
[5] M. E. Izquierdo and M. Ghanbari, “Texture Smoothing and Object Segmentation Using Feature-Adaptive Weighted Gaussian Filtering,” SBT/IEEE International Telecommunications Symposium, Sao Paulo, 9-13 August 1998, pp. 650-655.
[6] M. Basu, “Gaussian-Based Edge-Detection Methods—A Survey,” IEEE Transactions on System, Man and Cybernetics, Vol. 3, No. 32, 2002, pp. 252-260.
[7] S. J. Fu, Q. Q. Ruan, Y. L. Geng and W. Q. Wang, “Feature-Oriented Coupled Bidirection Flow for Image Denoising and Edge Sharping,” Tencon 2005 IEEE Region, Melbourne, 21-24 November 2005, pp. 1-5.
[8] Y. Zhen, “Wavelet Domain Multiresolution Markov Models for Image Segmentation and Denoising Applications,” Proquest Information and Learning Company, 2005, pp. 78-90.
[9] J. Portilla and V. Strela, “Image Denoising Using Scaled Mixtures of Gaussians in the Wavelet Domain,” IEEE Transactions on Image Processing, Vol. 11, No. 12, 2004, pp. 1338-1351.
[10] J. Liu and P. Moulin, “Image Denoising Based on Scale-Space Mixture Modeling of Wavelet Coefficient,” IEEE International Conference on Image Processing, Vol. 1, 1999, pp. 386-390.
[11] C. Dongwook and T. D. Bui, “Multivariate Statistical Approach for Image Denoising,” IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. 4, 2005, pp. 589-592.
[12] K. Rodenacker and P. Aubele, “Groping for Quantitative Digital 3-D Image Analysis: An Approach to Quantitative Fluorescence in Situ Hybridization in Thick Tissue Sections of Prostate Carcinoma,” Analytical Cellular Pathology, Vol. 15, No. 1, 1997, pp. 19-29.
[13] T. Irinopoulo, J. Vassy, M. Beil, P. Nicolopoulo and D. Encaoua, “3-D DNA Image Cytometry by Confocal Scanning Lasermicroscopy in Thick Tissue Blocks of Prostatic Lesions,” Cytometry, Vol. 27, No. 2, 1997, pp. 99-105. doi:10.1002/(SICI)1097-0320(19970201)27:2<99::AID-CYTO1>3.0.CO;2-F
[14] T. Visser, F. Groen and G. Brakenhoff, “Absorption and Scattering Correction in Fluorescence Confocal Microscopy,” Journal of Microscopy, Vol. 163, No. 2, 2007, pp. 189-200. doi:10.1111/j.1365-2818.1991.tb03171.x
[15] A. Liljeborg, M. Czader and A. Porwit, “A Method to Compensate for Light Attenuation with Depth in 3D DNA Image Cytometry Using a Confocal Scanning Laser Microscope,” Journal of Microscopy, Vol. 177, No. 2, 1995, pp. 108-114. doi:10.1111/j.1365-2818.1995.tb03540.x
[16] S. E. Umbaugh, “Computer Imaging: Digital Image Analysis and Processing,” CRC Press, Florida, 2005.
[17] L. Yaroslavsky and M. Eden, “Fundamentals of Digital Optics,” Birkhauser, Boston, 1996. doi:10.1007/978-1-4612-0845-7
[18] T. Zhang, B. Fang, Y. Yuan, Y. Y. Tang, Z. Shang, D. Li and F. Lang, “Multiscale Facial Structure Representation for Face Under Varying Illumination,” Pattern Recognition, Vol. 42, No. 2, 2009, pp. 252-258.
[19] P. Perona, “Steerable-Scalable Kernels for Edge Detection and Junction Analysis,” Image and Vision Computing, Vol. 10, No. 10, 1992, pp. 663-672. doi:10.1016/0262-8856(92)90011-Q

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.