A new contrast enhancement method of retinal images in Diabetic Screening System

N. S. Datta, P. Saha, H. Dutta, D. Sarkar, S. Biswas, P. Sarkar
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引用次数: 14

Abstract

A new retinal image contrast enhancement method for Diabetic Screening System is presented here. The proposed method evaluated by comparing the retinal image quality with different contrast enhancement methods which are applied in numerous papers. The proposed method produces better image quality and also preserves the mean brightness of the input images which is very important for medical image analysis. Publicly available datasets like DRIVE, STARE, DIARETDB0, and DIARETDB1 are used for testing purpose. The low contrast noisy retinal images are collected from the Hospital and performance of the proposed contrast enhancement is also measured. The SSIM (Structure Similarity Index Measurement) and AAMBE (Average Absolute Mean Brightness Error) are the important performance measurement parameters applied here to examine the image quality and brightness preserving ability respectively. The average SSIM is reported as 0.82(Std. Dev=0.011) and AAMBE is 0.023(Std. Dev=0.030). The success rate on low contrast noisy retinal image analysis is showing the importance of the proposed method.
一种新的糖尿病筛查系统视网膜图像对比度增强方法
本文提出了一种新的用于糖尿病筛查系统的视网膜图像对比度增强方法。通过比较不同对比度增强方法对视网膜图像质量的影响,对该方法进行了评价。该方法在保持输入图像平均亮度的基础上,提高了图像质量,对医学图像分析具有重要意义。公开可用的数据集,如DRIVE、STARE、DIARETDB0和DIARETDB1用于测试目的。从医院收集了低对比度噪声视网膜图像,并测量了所提出的对比度增强的性能。SSIM (Structure Similarity Index Measurement)和AAMBE (Average Absolute Mean Brightness Error)分别是检验图像质量和亮度保持能力的重要性能度量参数。平均SSIM为0.82(Std)。Dev=0.011), AAMBE = 0.023(Std。Dev = 0.030)。低对比度噪声视网膜图像分析的成功率表明了该方法的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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