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
Article Information
Identifiers and Pagination:
Year: 2015Volume: 9
First Page: 194
Last Page: 198
Publisher ID: TOBEJ-9-194
DOI: 10.2174/1874120701509010194
Article History:
Received Date: 10/4/2015Revision Received Date: 20/5/2015
Acceptance Date: 15/6/2015
Electronic publication date: 31/8/2015
Collection year: 2015
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.
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.