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A Novel Retinal Image Color Texture Enhancement Method Based on Multi-regression Analysis

机译:基于多元回归分析的视网膜图像彩色纹理增强方法

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Characteristics of retinal tissue shown in retinal images are very important bases for ophthalmologists in clinical diagnosis. Among them, retinal vessel is the most important character. However, the quality of retinal image is usually poor due to the non-perfect imaging environment, making ophthalmologists difficult to read. This paper introduces a retinal vessel enhancement algorithm using multiple regression analysis (MRA) based color transformation scheme to enhance the contrast between retinal vessels and background area in retinal images. This method uses the color texture of another appropriate image as a reference to enforce color transformation on the desired target image. This paper focus on investigating the vessel enhancement effect with different reference images. The proposed method has been validated with STARE (Structure Analysis of the Retina) database. The experimental results show that, using images with lots of green and a little yellow as references can upgrade the average value of contrast to 0.2921 and 3.25 times more than the original one in STARE database. It demonstrates that the proposed method can effectively enhance the contract between retinal vessels and background area in retinal images.
机译:视网膜图像中显示的视网膜组织特征是眼科医生在临床诊断中非常重要的基础。其中,视网膜血管是最重要的特征。但是,由于成像环境不理想,视网膜图像的质量通常较差,从而使眼科医生难以阅读。本文介绍了一种基于多重回归分析(MRA)的颜色转换方案的视网膜血管增强算法,以增强视网膜图像背景中视网膜血管与背景区域之间的对比度。此方法使用另一个适当图像的颜色纹理作为参考,以对所需目标图像执行颜色转换。本文重点研究不同参考图像对血管的增强作用。所提出的方法已通过STARE(视网膜结构分析)数据库进行了验证。实验结果表明,使用大量绿色和少量黄色作为参考的图像可以将对比度的平均值提升到STARE数据库中原始对比度的0.2921和3.25倍。说明该方法可以有效增强视网膜图像中视网膜血管与背景区域之间的收缩。

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