RESEARCH ARTICLE


The Application of Wavelet-domain Hidden Markov Tree Model in Diabetic Retinal Image Denoising



Dong Cui1, *, Minmin Liu2, Lei Hu3, Keju Liu2, Yongxin Guo1, Qing Jiao1
1 Department of Radiology, Taishan Medical University, Taiwan 271016, P.R. China
2 Ophthalmology, Hospital of Traditional Chinese Medicine, Taiwan 271000, P.R. China
3 Ophthalmology, Hospital Affiliated to Taishan Medical University, Taiwan 271000, P.R. China


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Creative Commons License
© Cui et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by/4.0/legalcode) which permits unrestricted, noncommercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Department of Radiology, Taishan Medical University, Taiwan 271016, P.R. China; Tel: 18053811660; E-mail: cuidong_cd@126.com


Abstract

The wavelet-domain Hidden Markov Tree Model can properly describe the dependence and the correlation of fundus angiographic images’ wavelet coefficients among scales. Based on the construction of the fundus angiographic images from Hidden Markov Tree Models and Gaussian Mixture Models, this paper applied expectation-maximum algorithm to estimate the wavelet coefficients of original fundus angiographic images and the Bayesian estimation to achieve the goal of fundus angiographic images denoising. As is shown in the experimental result, compared with the other algorithms as mean filter and median filter, this method effectively improved the peak signal to noise ratio of fundus angiographic images after denoising and preserved the details of vascular edge in fundus angiographic images.

Keywords: Fundus images, HIDDEN Markov Tree Model (HMT Model), image denoising, wavelet transform.