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

作者：Shao, Feng^*; Yang, Yan; Jiang, Qiuping; Jiang, Gangyi; Ho, Yo-Sung

来源：IEEE Access, 2018, 6: 806-817.

DOI：10.1109/ACCESS.2017.2776126

• Fundus image
• quality assessment
• illumination level
• naturalness level
• structure level

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.

• 出版日期2018
• 单位宁波大学