Faculty of Medicine at the University of Iceland, Sæmundargata 2, 102, Reykjavík, Iceland; Faculty of Electrical and Computer Engineering at the University of Iceland, Sæmundargata 2, 102, Reykjavík, Iceland.
Faculty of Electrical and Computer Engineering at the University of Iceland, Sæmundargata 2, 102, Reykjavík, Iceland.
Comput Biol Med. 2021 Feb;129:104114. doi: 10.1016/j.compbiomed.2020.104114. Epub 2020 Nov 12.
Fundus photography is commonly used for screening, diagnosis, and monitoring of various diseases affecting the eye. In addition, it has shown promise in the diagnosis of brain diseases and evaluation of cardiovascular risk factors. Good image quality is important if diagnosis is to be accurate and timely. Here, we propose a method that automatically grades image quality on a continuous scale which is more flexible than binary quality classification. The method utilizes random forest regression models trained on image features discovered automatically by combining basic image filters using simulated annealing as well as features extracted with the discrete Fourier transform. The method was developed and tested on images from two different fundus camera models. The quality of those images was rated on a continuous scale from 0.0 to 1.0 by five experts. In addition, the method was tested on DRIMDB, a publicly available dataset with binary quality ratings. On the DRIMDB dataset the method achieves an accuracy of 0.981, sensitivity of 0.993 and specificity of 0.958 which is consistent with the state of the art. When evaluating image quality on a continuous scale the method outperforms human raters.
眼底摄影常用于筛查、诊断和监测各种眼部疾病。此外，它在诊断脑部疾病和评估心血管危险因素方面也显示出了潜力。如果要进行准确和及时的诊断，那么获得良好的图像质量非常重要。在这里，我们提出了一种方法，可以自动对连续尺度的图像质量进行分级，这种方法比二进制质量分类更灵活。该方法利用随机森林回归模型，通过使用模拟退火结合基本图像滤波器自动发现的图像特征以及使用离散傅里叶变换提取的特征进行训练。该方法是在两种不同眼底相机模型的图像上开发和测试的。这些图像的质量由五名专家在 0.0 到 1.0 的连续尺度上进行评分。此外，该方法还在 DRIMDB 上进行了测试，DRIMDB 是一个具有二进制质量评级的公开数据集。在 DRIMDB 数据集上，该方法的准确率为 0.981，灵敏度为 0.993，特异性为 0.958，与现有技术相当。在对连续尺度的图像质量进行评估时，该方法的表现优于人类评分者。
