Abstract
Fundus image analysis is crucial for eye condition screening and diagnosis and consequently personalized health management in a long term. This paper targets at left and right eye recognition, a basic module for fundus image analysis. We study how to automatically assign left-eye/right-eye labels to fundus images of posterior pole. For this under-explored task, four models are developed. Two of them are based on optic disc localization, using extremely simple max intensity and more advanced Faster R-CNN, respectively. The other two models require no localization, but perform holistic image classification using classical Local Binary Patterns (LBP) features and fine-tuned ResNet-18, respectively. The four models are tested on a real-world set of 1,633 fundus images from 834 subjects. Fine-tuned ResNet-18 has the highest accuracy of 0.9847. Interestingly, the LBP based model, with the trick of left-right contrastive classification, performs closely to the deep model, with an accuracy of 0.9718.
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31 January 2019
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 61672523), the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (No. 18XNLG19).
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Lai, X., Li, X., Qian, R., Ding, D., Wu, J., Xu, J. (2019). Four Models for Automatic Recognition of Left and Right Eye in Fundus Images. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11295. Springer, Cham. https://doi.org/10.1007/978-3-030-05710-7_42
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DOI: https://doi.org/10.1007/978-3-030-05710-7_42
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