Abstract
Imaging is increasingly used for the diagnosis of retinal normality and the monitoring of retinal abnormalities. Many retinal vessel properties, such as small artery aneurysms, narrowing of incisions, etc., are related to systemic diseases. The morphology of retinal blood vessels themselves is related to cardiovascular disease and coronary artery disease in adults. The fundus image can intuitively reflect the retinal vessel lesions, and the computer-based image processing method can be used for auxiliary medical diagnosis. In this paper, a retinal vessel segmentation model, named as MLFF, is proposed to effectively extract and fuse multiple low-level features. Firstly, there are 25 low-level feature maps of fundus retinal vessel images that are analyzed and extracted. Then, the feature maps are fused by an AdaBoost classifier. Finally, the MLFF is trained and evaluated on public fundus images for vessel extraction dataset (DRIVE). The qualitative and quantitative experimental results show that our model can effectively detect the retinal vessels and outperforms other models including deep learning-based models.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (62106208), the Sichuan Science and Technology Program (2020JDRC0031) and the China Postdoctoral Science Foundation (2021TQ0272, 2021M702715).
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Deng, T., Huang, Y., Zhang, J. (2022). MLFF: Multiple Low-Level Features Fusion Model for Retinal Vessel Segmentation. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2021. Communications in Computer and Information Science, vol 1566. Springer, Singapore. https://doi.org/10.1007/978-981-19-1253-5_20
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