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Abstract
Aiming at the current problem of insufficient extraction of small retinal blood vessels, we propose a retinal blood vessel segmentation algorithm that combines supervised learning and unsupervised learning algorithms. In this study, we use a multiscale matched filter with vessel enhancement capability and a U-Net model with a coding and decoding network structure. Three channels are used to extract vessel features separately, and finally, the segmentation results of the three channels are merged. The algorithm proposed in this paper has been verified and evaluated on the DRIVE, STARE, and CHASE_DB1 datasets. The experimental results show that the proposed algorithm can segment small blood vessels better than most other methods. We conclude that our algorithm has reached 0.8745, 0.8903, and 0.8916 on the three datasets in the sensitivity metric, respectively, which is nearly 0.1 higher than other existing methods.
Highlights
The human eyes consist of the following parts: cornea, pupil, iris, vitreous, and retina
This paper proposes a new retinal blood vessel segmentation method, which combines a multiscale matched filter with a U-Net neural network model of deep learning
In order to avoid ignoring the characteristics of small blood vessels, this paper performs multichannel feature extraction and segmentation on retinal blood vessel images
Summary
The human eyes consist of the following parts: cornea, pupil, iris, vitreous, and retina. The extraction of retinal blood vessels and the characterization of morphological properties, such as diameter, shape, distortion, and bifurcation, can be used to screen, evaluate, and treat different ocular abnormalities [2]. Compared with supervised learning, nonsupervised learning methods, such as matched filtering, mathematical morphology operations, blood vessel tracking, and clustering, do not require corresponding image labels but analyze and process based on the existing data. These two types of methods have been applied and innovated by many researchers in recent years.
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