Automated Quality Assessment of Fundus Images via Analysis of Illumination, Naturalness and Structure

• Main Authors: Feng Shao, Yan Yang, Qiuping Jiang, Gangyi Jiang, Yo-Sung Ho
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Fundus image; quality assessment; illumination level; naturalness level; structure level
• Online Access: https://ieeexplore.ieee.org/document/8116608/

In remote medical diagnosis, the percentage of poor-quality fundus images is very high, which requires automated quality assessment of fundus images in the acquisition stage to reduce the retransmission cost. In this paper, we propose a fundus image quality classifier via the analysis of illumination, naturalness, and structure, which use three effective secondary indices (or 5-D feature set) and different classification methods to determine the recommendation indexes of fundus images for further diagnosis. We construct a fundus image database including `accept' and `reject' classes based on the definition of illumination, naturalness, and structure. The model can achieve a sensitivity of 94.69%, specificity of 92.29%, and accuracy of 93.60% for the classifying of the fundus images.