Identification of noise in the fundus images

A. Hani, T. Soomro, I. Faye, N. Kamel, N. Yahya
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引用次数: 11

Abstract

Analysis of the tiny retinal vasculatures in retinal fundus images becomes difficult due to very low and varied contrast between the retinal vasculature and the background. Fundus fluorescein angiogram overcomes these problems and provides an excellent visualization of the retinal vasculature; however it is an invasive procedure requiring injection of contrasting agents. Further investigation of the RETICA method, a non-invasive method of image enhancement developed earlier, is reported in this paper. It was found that noise is present in the Retinex image. Thus, the identification of the noise in the Retinex image and its removal has been the focus of this research paper. The method used to identify noise is based on adaptive wiener filters (additive, multiplicative, and additive plus multiplicative filters) and the fundus model image and real fundus images are applied to these filters. It is observed that retinal fundus images contained both additive and multiplicative noise. The noise is reduced by using adaptive wiener filter (additive plus multiplicative adaptive wiener filter) based method which resulted in 2.84db an improvement in PSNR.
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眼底图像噪声的识别
由于视网膜血管与背景之间的对比度非常低且变化很大,因此对视网膜眼底图像中微小的视网膜血管进行分析变得困难。眼底荧光素血管造影克服了这些问题,并提供了一个很好的可视化视网膜血管;然而,这是一种需要注射对比剂的侵入性手术。本文报道了对较早发展起来的一种非侵入性图像增强方法RETICA方法的进一步研究。结果发现视网膜图像中存在噪声。因此,视网膜图像中噪声的识别与去除一直是本文研究的重点。该方法基于自适应维纳滤波器(加性、乘性和加性+乘性滤波器),并将眼底模型图像和真实眼底图像应用于这些滤波器。观察到视网膜眼底图像同时包含加性噪声和乘性噪声。采用基于自适应维纳滤波(加性+乘性自适应维纳滤波)的降噪方法,使PSNR提高2.84db。
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