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一种基于对比度自适应阈值模型的新方法,用于在早产儿视网膜图像中准确检测视盘。

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

作者信息

Kakade Akhilesh, Dhanaraj Rajesh Kumar, Metkewar P S, Nayyar Anand

机构信息

Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University) (SIU), Model Colony, Pune, Maharashtra, India.

Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University) (SIU), Pune, India.

出版信息

Phys Eng Sci Med. 2025 Jun 3. doi: 10.1007/s13246-025-01565-7.

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 ( ), 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.

摘要

在进行视网膜图像分析时,确定视盘在视网膜图像中的精确位置是一项重要任务。由于边界不清晰和图像对比度低,视盘的定位通常无法检测到其精确位置,尤其是在婴儿的视网膜图像中,在低光照条件下获取图像的过程必须非常迅速。本研究论文的目的是使用一种名为“对比度自适应阈值模型”的分割算法来检测婴儿视网膜图像中视盘的位置。这种方法的新颖之处在于其两步策略:它首先利用自适应伽马校正颜色保留框架进行图像增强,然后应用对比度自适应阈值模型(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中提取视盘区域。此外,该方法基于高斯滤波器的标准差和增强后的红色通道图像计算自适应阈值,从而实现对视盘区域的精确定位。在1103张视网膜图像的ROP数据集中,结果的骰子系数为0.8285,准确率为0.9894,精确率为0.9958,召回率为0.9875,特异性为0.9999。对由低对比度、次优照明和虚假反射组成的ROP婴儿视网膜图像数据集的实验评估表明,视盘定位有了显著改进,从而为ROP的早期可靠诊断提供了有价值的支持。

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