Abstract
A macular fovea is a physiological structure of the human retina, which is an essential optical center. The distance between the lesion area and the foveal center determines the severity degree of visual impacts. Therefore, accurate fovea localization is the basis of the computer-aided ophthalmic diagnosis and vision screening. A simple but effective fovea localization algorithm based on the Faster R-CNN and physiological structure prior is presented. First, a fovea localization model and an optic disc localization model are trained separately. Then, for each fundus photograph, both candidate areas of the fovea and the location of the optic disc are predicted using two pre-trained models. Next, prior knowledge of the physiological adjacent relationship between a fovea and an optic disc is applied to eliminate unreasonable candidate bounding boxes. Finally, the ultimate bounding box of the fovea is determined by the best candidate. Experiments were conducted on a private dataset with 5,203 fundus photographs and the public Messidor dataset including 1200 fundus photographs. The accuracy of the foveal location in the offset scale of 1/2 optic disc diameter on the Messidor is 99.58%, which is 0.71% higher than the state-of-the-art (98.87%).
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Notes
- 1.
Empirically, dd is about 80 pixels in a 500\(\times \)500 fundus photograph [17].
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Acknowledgements
This work is supported by the CSC State Scholarship Fund (201806295014), NSFC (No. 61672523, No. 61771468), CAMS Initiative for Innovative Medicine (2018-I2M-AI-001), Beijing Natural Science Foundation (No. 4192029), Beijing Hospitals Authority Youth Programme (QML20170206); The priming scientific research foundation for the junior researcher in Beijing Tongren Hospital, Capital Medical University (2018-YJJ-ZZL-052).
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Wu, J. et al. (2019). Fovea Localization in Fundus Photographs by Faster R-CNN with Physiological Prior. In: Fu, H., Garvin, M., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2019. Lecture Notes in Computer Science(), vol 11855. Springer, Cham. https://doi.org/10.1007/978-3-030-32956-3_19
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