Abstract
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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References
Cheng, J., et al.: Structure-preserving guided retinal image filtering and its application for optic disk analysis. IEEE Trans. Med. Imaging 37(11), 2536–2546 (2018)
Fu, H., et al.: Disc-aware ensemble network for glaucoma screening from fundus image. IEEE Trans. Med. Imaging 37(11), 2493–2501 (2018)
Fu, H., et al.: Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans. Med. Imaging 37(7), 1597–1605 (2018)
Gu, Z., et al.: CE-Net: context encoder network for 2D medical image segmentation. IEEE TMI (2019, in press). https://doi.org/10.1109/TMI.2019.2903562
He, K., et al.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Huang, G., et al.: Densely connected convolutional networks. In: CVPR, pp. 2261–2269 (2017)
Köhler, T., et al.: Automatic no-reference quality assessment for retinal fundus images using vessel segmentation. In: IEEE International Symposium on Computer-Based Medical Systems, pp. 95–100 (2013)
MacGillivray, T.J., et al.: Suitability of UK Biobank retinal images for automatic analysis of morphometric properties of the vasculature. PLoS One 10(5), 1–10 (2015)
Mishkin, D., et al.: Systematic evaluation of convolution neural network advances on the imagenet. Comput. Vis. Image Underst. 161, 11–19 (2017)
Niemeijer, M., et al.: Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening. Med. Image Anal. 10(6), 888–898 (2006)
Pires, R., et al.: Retinal image quality analysis for automatic diabetic retinopathy detection. In: SIBGRAPI Conference on Graphics, Patterns and Images, pp. 229–236 (2012)
Pires Dias, J.M., et al.: Retinal image quality assessment using generic image quality indicators. Inf. Fusion 19, 73–90 (2014)
Sevik, U., et al.: Identification of suitable fundus images using automated quality assessment methods. J. Biomed. Opt. 19(4), 1–11 (2014)
Shao, F., et al.: Automated quality assessment of fundus images via analysis of illumination, naturalness and structure. IEEE Access 6, 806–817 (2017)
Tobin, K.W., et al.: Elliptical local vessel density: a fast and robust quality metric for retinal images. In: EMBC, pp. 3534–3537 (2008)
Wang, S., et al.: Human visual system-based fundus image quality assessment of portable fundus camera photographs. IEEE Trans. Med. Imaging 35(4), 1046–1055 (2016)
Yu, F., et al.: Image quality classification for DR screening using deep learning. In: EMBC, pp. 664–667 (2017)
Zago, G.T., et al.: Retinal image quality assessment using deep learning. Comput. Biol. Med. 103, 64–70 (2018)
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Fu, H. et al. (2019). Evaluation of Retinal Image Quality Assessment Networks in Different Color-Spaces. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_6
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DOI: https://doi.org/10.1007/978-3-030-32239-7_6
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