计算机科学
人工智能
RGB颜色模型
图像质量
色空间
可用的
特征向量
计算机视觉
质量评定
模式识别(心理学)
特征(语言学)
图像(数学)
评价方法
多媒体
哲学
语言学
工程类
可靠性工程
作者
Huazhu Fu,Boyang Wang,Jianbing Shen,Shiyong Cui,Yanwu Xu,Jiang Liu,Ling Shao
标识
DOI:10.1007/978-3-030-32239-7_6
摘要
Retinal image quality assessment (RIQA) is essential for controlling the quality of retinal imaging and guaranteeing the reliability of diagnoses by ophthalmologists or automated analysis systems. Existing RIQA methods focus on the RGB color-space and are developed based on small datasets with binary quality labels (i.e., ‘Accept’ and ‘Reject’). In this paper, we first re-annotate an Eye-Quality (EyeQ) dataset with 28,792 retinal images from the EyePACS dataset, based on a three-level quality grading system (i.e., ‘Good’, ‘Usable’ and ‘Reject’) for evaluating RIQA methods. Our RIQA dataset is characterized by its large-scale size, multi-level grading, and multi-modality. Then, we analyze the influences on RIQA of different color-spaces, and propose a simple yet efficient deep network, named Multiple Color-space Fusion Network (MCF-Net), which integrates the different color-space representations at both a feature-level and prediction-level to predict image quality grades. Experiments on our EyeQ dataset show that our MCF-Net obtains a state-of-the-art performance, outperforming the other deep learning methods. Furthermore, we also evaluate diabetic retinopathy (DR) detection methods on images of different quality, and demonstrate that the performances of automated diagnostic systems are highly dependent on image quality.
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