Department of Information Science and Technology, Anna University, Chennai, India.
Sathyabama Institute of Science and Technology, Sathyabama University, Chennai, India.
Int J Comput Assist Radiol Surg. 2022 Jul;17(7):1367-1377. doi: 10.1007/s11548-022-02668-2. Epub 2022 Jun 2.
Automatic retinal fundus image quality analysis is one of the most essential preliminary stages in automatic computer-aided retinal disease diagnosis system, which allows good-quality fundus images for accurate disease prediction through localization and segmentation of retinal regions. This paper presents new feature extraction methods using full-reference and no-reference image quality metrics for image quality classification.
Basic image features, reference and no-reference features are extracted from the fundus image and applied through different classification techniques to determine the image quality for further diagnosis. In this paper, human-made categorization including good and non-good-quality fundus image classification is constructed by considering major features of retinal fundus images are illumination, clarity, image intensity, contrast and region visibility. The proposed system presented fundus image quality classification by automatic extraction of features from fundus images through image processing techniques and automatic classification of image quality through different classification algorithm.
This system was thoroughly investigated on 2674 retinal fundus images from publically available datasets, namely MESSIDOR, Drishti-GS1, DRIVE, HRF, DRIONS-DB, DIARETDB0, DIARETDB1, IDRiD, INSPIRE-AVR, CHASE-DB1, ONHSD, DRIMDB and e-ophtha-EX with better performance results in terms of sensitivity, accuracy, precision and F1 score of 99.36%, 96.79%, 96.29% and 97.79%, respectively.
The proposed system results were compared to the existing state-of-the-art approaches and outperform existing methods for image quality assessment representing the efficiency and robustness of our system is most suitable for automatic image analysis during retinal disease diagnosis.
自动视网膜眼底图像质量分析是自动计算机辅助视网膜疾病诊断系统中最重要的初步阶段之一，它通过定位和分割视网膜区域，为准确的疾病预测提供高质量的眼底图像。本文提出了使用全参考和无参考图像质量指标的新特征提取方法，用于图像质量分类。
从眼底图像中提取基本图像特征、参考特征和无参考特征，并通过不同的分类技术应用这些特征，以确定图像质量，从而进行进一步的诊断。在本文中，通过考虑视网膜眼底图像的主要特征，如照明、清晰度、图像强度、对比度和区域可见度，构建了包括良好和非良好质量眼底图像分类的人为分类。该系统通过图像处理技术从眼底图像中自动提取特征，并通过不同的分类算法自动对图像质量进行分类，实现了眼底图像质量分类。
该系统在来自公共数据集的 2674 张视网膜眼底图像上进行了深入研究，即 MESSIDOR、Drishti-GS1、DRIVE、HRF、DRIONS-DB、DIARETDB0、DIARETDB1、IDRiD、INSPIRE-AVR、CHASE-DB1、ONHSD、DRIMDB 和 e-ophtha-EX，在灵敏度、准确性、精度和 F1 分数方面的性能结果分别为 99.36%、96.79%、96.29%和 97.79%。
将提出的系统结果与现有的最先进方法进行了比较，在图像质量评估方面优于现有的方法，这表明我们的系统的效率和稳健性，最适合在视网膜疾病诊断中的自动图像分析。
