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Retina blood vessels segmentation based on the combination of the supervised and unsupervised methods


The retinal blood vessels segmentation algorithm is a powerful tool for the early detection of ophthalmic and cardiovascular diseases and biometrics of the automatic tracking system. Accurate segmentation of blood vessels from a retinal image plays a significant role in the prudent examination of the vessels. Therefore, a combined algorithm of a supervised generalized linear model and an unsupervised contrast limited adaptive histogram equalization is proposed in this paper. Using a generalized linear model integrated with multi-scale information by Gabor wavelet transform, the proposed supervised process can extract more prominent features of retinal blood vessels. Besides, the contrast limited adaptive histogram equalization uses the local histogram equalization, which can handle the illumination variation and adjust the enlargement of details. The method is evaluated on a publicly available DRIVE dataset, as it contains ground truth images precisely marked by experts. The segmentation results show that the proposed method can segment the blood vessels accurately.

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This work was supported by Natural Science Foundations of China under Grant 61801202.

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Correspondence to Lingling Fang.

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The authors have no relevant financial interests in this paper and no potential conflicts of interest to disclose.

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Fang, L., Zhang, L. & Yao, Y. Retina blood vessels segmentation based on the combination of the supervised and unsupervised methods. Multidim Syst Sign Process 32, 1123–1139 (2021).

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  • Supervised method
  • Unsupervised method
  • Generalized linear model
  • Contrast limited adaptive histogram equalization
  • Retinal blood vessels segmentation