A novel methodology based on Contrast-Adaptive Threshold Model for accurate optic disc detection in retinal images of premature infants.

IF 2 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Akhilesh Kakade, Rajesh Kumar Dhanaraj, P S Metkewar, Anand Nayyar
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引用次数: 0

Abstract

Identifying the exact location of the optic disc in retinal images is an important task while performing the retinal image analysis. Localization of the optic disc generally fails to detect its exact location due to unclear boundaries and low contrast images, especially in retinal images of infants where the process of retrieving the images should be very quick in low light conditions. The objective of this research paper is to detect the location of optic disc using a segmentation algorithm titled "Contrast-Adaptive Threshold Model" in infant retinal images. The novelty of this approach lies in its two-step strategy: it initially utilizes Adaptive Gamma Correction Color Preserving Framework for image enhancement, followed by the application of CATM. This approach performs the processing of red channel, in which the optic disc region is extracted from the red channel by eliminating non-optic disc pixel values. Furthermore, the method computes an adaptive threshold based on standard deviation of the Gaussian filter and enhanced red channel image ( I RE ), resulting in the precise localization of optic disc region. The results on ROP dataset of 1103 retina images achieved a dice score of 0.8285, accuracy of 0.9894, precision of 0.9958, recall of 0.9875, and specificity of 0.9999. The experimental evaluation of retinal image dataset of ROP infants, consisting of low-contrast, sub-optimal illumination, and false reflections, represents a significant improvement in optic disc localization, thereby contributing valuable support for the early and reliable diagnosis of ROP.

基于对比度自适应阈值模型的早产儿视网膜图像视盘精确检测新方法。
确定视盘在视网膜图像中的准确位置是视网膜图像分析中的一项重要任务。视盘定位由于边界不清,图像对比度低,通常无法检测到其准确位置,特别是在婴儿视网膜图像中,在弱光条件下检索图像的过程应该非常快。本研究的目的是在婴儿视网膜图像中使用一种名为“对比度自适应阈值模型”的分割算法来检测视盘的位置。该方法的新颖之处在于其两步策略:首先利用自适应伽玛校正颜色保持框架进行图像增强,然后应用CATM。该方法对红色通道进行处理,通过消除非视盘像素值,从红色通道提取视盘区域。此外,该方法基于高斯滤波的标准差和增强红通道图像(I RE)计算自适应阈值,从而实现视盘区域的精确定位。在1103张视网膜图像的ROP数据集上,结果的dice得分为0.8285,准确率为0.9894,精密度为0.9958,召回率为0.9875,特异性为0.9999。对ROP婴儿视网膜图像数据集的实验评估,包括低对比度,次优照明和虚假反射,代表视盘定位的显着改善,从而为ROP的早期可靠诊断提供有价值的支持。
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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来源期刊
CiteScore
8.40
自引率
4.50%
发文量
110
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