Abstract
The wavelet-domain Hidden Markov Tree Model can properly describe the dependence and correlation of fundus angiographic images’ wavelet coefficients among scales. Based on the construction of the fundus angiographic images 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.
Highlights
As the incidence of diabetes has steadily increased in recent years, there are more and more patients of diabetic retinopathy (DR)
The experimented Fluorescein fundus angiography (FFA) images were collected by KAWA VX- 3 Ocular Fundus Digital Angiographic Processing System
The data was handled with the MATLAB2013 Software in the DELL Image Processing Station with a main frequency of 3.4GHz and a memory of 16GB [19,20,21]
Summary
As the incidence of diabetes has steadily increased in recent years, there are more and more patients of diabetic retinopathy (DR). Traditional image denoising methods, such as the median filter and the threshold algorithm, would lose quite a little high frequency information in the denoising, which caused the edge blur of images and the missing of detailed information [5,6,7]. Wavelet transform could properly maintain the edge and features of details of images at the same time of denoising [8, 9]. ( ) tain a good image edge and details at the same time, it can ensure the performance s t improvement of wavelet denoising algorithm. In this paper the wavelet-domain HMT was applied to describe the images and calculate the correlation of wavelet coefficients in neighboring scales [13]; and a Bayesian estimation was implemented to estimate image models, which can help to properly maintain the edge information for images in the process of effective fundus angiographic images denoising. As the experimental result showed, the peak signal noise ratio (PSNR) and the visual quality of FFA denoising images were obviously improved
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