MMM2019: Four Models for Automatic Recognition of Left and Right Eye in Fundus Images

Our MMM2019 paper on recognizing Left / Right Eye in Fundus Images is online.

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 ResNet18, 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.

Xin Lai, Xirong Li, Rui Qian, Dayong Ding, Jun Wu, Jieping Xu (2019): Four Models for Automatic Recognition of Left and Right Eye in Fundus Images. the 25th International Conference on MultiMedia Modeling (MMM), 2019.

 

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