Retinal Image Enhancement using Curvelet Transform Combine Non-Linear Diffusion Filter and Minimax Optimization Algorithm
Main Article Content
Abstract
The retina image is an important area for medical treatment of the disease. By observing the changes in the blood vessels in the retina lines help doctors diagnose diseases, to collect and analyze the symptoms and the development of related treatments. Consequently, improve retinal image quality is an important preprocessing step. And to improve retinal image quality several techniques have been proposed such as Histogram Equalization [1,2,3], Local Normalization [4], Contrast Limit Adaptive Histogram Equalization [5,6], Laplacian [7], ... but still can not provide high efficiency by persists high noise and poor image results. Therefore, in this paper, we propose a method of raising the quality of retinal images using filter change curvelet combines nonlinear diffusion and minimum Minimax algorithm. By the analysis and calculation results in picture quality parameters through experimental treatment, we will draw conclusions indicate that the proposed method improves the image quality better than previous methods.
Keywords
Retinal image enhancement, Minimax optimization algorithm, Curvelet transform, Non-linear diffusion filter
Article Details
References
[1] Hum, Yan Chai; Lai, Khin Wee; Mohamad Salim, Maheza Irna (11 October 2014). “Bihistogram equalization for image contrast enhancement.” Complexity, 20(2): 22–36.
[2] Laughlin, S.B. (1981). “A simple coding procedure enhances a neuron's information capacity.” Z. Naturforsch. 9–10(36): 910–2.
[3] Ji-Hee Han, Sejung Yang, Byung-Uk Lee (2011). “A Novel 3-D Color Histogram Equalization Method with Uniform 1-D Gray Scale Histogram.” IEEE Trans. on Image Processing, Vol. 20, No. 2, pp. 506–512.
[4] Staal, J. J.; Abramoff, M. D.; Niemeijer, M., et al., “Ridge based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imaging, vol. 23, no. 4, pp. 501–509, 2004.
[5] WANG Zhiming, TAO Jianhua (2006). “A Fast Implementation of Adaptive Histogram Equalization,” in Proc. of ICSP, pp. 16–20.
[6] A. W. Setiawan, T. R. Mengko, O. S. Santosa, A. B. Suksmono (2013). “Color Retinal Image Enhancement using CLAHE,” in International Conference in ICT for smart society, Indonesia, pp. 1–3.
[7] Sylvain Paris, Samuel W. Hasinoff, Jan Kautz (2011). “Local Laplacian Filters: Edge-aware Image Processing with a Laplacian Pyramid,” ACM Transactions on Graphics, vol. 30, no. 4, pp. 1–11.
[8] E. Cândes, D. Donoho (2003). “Continuous curvelet transform: I. Resolution of the wavefront set,” Appl. Comput. Harmon. Anal., 19(2003): 162–197.
[9] http://en.wikipedia.org/wiki/Anisotropic_diffusion
[10] https://www.mathworks.com/help/optim/examples/minimax-optimization.html
[11] Joachim Weickert (1998). “Anisotropic Diffusion in Image Processing,” ECMI Series, Teubner-Verlag, Stuttgart, Germany.
[12] DRIVE database. (CrossRef Link).
[13] Sendur, L.; Selesnick, I. W. (2002). “Bivariate shrinkage functions for Wavelet-based denoising exploiting interscale dependency,” IEEE Trans. Signal Processing, 50(2002): 2744–2756.
[14] François G. Meyer (2003). “Wavelet-Based Estimation of a Semiparametric Generalized Linear Model of FMRI Time-Series,” IEEE Trans. on Medical Imaging, 22(2003).
[2] Laughlin, S.B. (1981). “A simple coding procedure enhances a neuron's information capacity.” Z. Naturforsch. 9–10(36): 910–2.
[3] Ji-Hee Han, Sejung Yang, Byung-Uk Lee (2011). “A Novel 3-D Color Histogram Equalization Method with Uniform 1-D Gray Scale Histogram.” IEEE Trans. on Image Processing, Vol. 20, No. 2, pp. 506–512.
[4] Staal, J. J.; Abramoff, M. D.; Niemeijer, M., et al., “Ridge based vessel segmentation in color images of the retina,” IEEE Trans. Med. Imaging, vol. 23, no. 4, pp. 501–509, 2004.
[5] WANG Zhiming, TAO Jianhua (2006). “A Fast Implementation of Adaptive Histogram Equalization,” in Proc. of ICSP, pp. 16–20.
[6] A. W. Setiawan, T. R. Mengko, O. S. Santosa, A. B. Suksmono (2013). “Color Retinal Image Enhancement using CLAHE,” in International Conference in ICT for smart society, Indonesia, pp. 1–3.
[7] Sylvain Paris, Samuel W. Hasinoff, Jan Kautz (2011). “Local Laplacian Filters: Edge-aware Image Processing with a Laplacian Pyramid,” ACM Transactions on Graphics, vol. 30, no. 4, pp. 1–11.
[8] E. Cândes, D. Donoho (2003). “Continuous curvelet transform: I. Resolution of the wavefront set,” Appl. Comput. Harmon. Anal., 19(2003): 162–197.
[9] http://en.wikipedia.org/wiki/Anisotropic_diffusion
[10] https://www.mathworks.com/help/optim/examples/minimax-optimization.html
[11] Joachim Weickert (1998). “Anisotropic Diffusion in Image Processing,” ECMI Series, Teubner-Verlag, Stuttgart, Germany.
[12] DRIVE database. (CrossRef Link).
[13] Sendur, L.; Selesnick, I. W. (2002). “Bivariate shrinkage functions for Wavelet-based denoising exploiting interscale dependency,” IEEE Trans. Signal Processing, 50(2002): 2744–2756.
[14] François G. Meyer (2003). “Wavelet-Based Estimation of a Semiparametric Generalized Linear Model of FMRI Time-Series,” IEEE Trans. on Medical Imaging, 22(2003).