U-Shaped Retinal Vessel Segmentation Algorithm Based on Adaptive Scale Information

Liming, 梁礼明 Liang; Xiaoqi, 盛校棋 Sheng; Zhimin, 蓝智敏 Lan; Guoliang, 杨国亮 Yang; Chen, Xinjian

doi:10.3788/aos201939.0810004

Cited by 3 publications

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“…Liang Liming et al [ 28 ] enhanced the segmentation of retinal blood vessels by employing a densely deformable structure that effectively captures multi-scale information and structural shapes. In 2019, Li et al [ 29 ] introduced the Context-Aware Sensitive U-Net (CASU), which incorporates an integrated attention gate (AGs) before concatenating corresponding features in the encoder and decoder of U-Net.…”

Section: Related Workmentioning

confidence: 99%

Improving automatic segmentation of liver tumor images using a deep learning model

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Heliyon

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Section: Related Workmentioning

confidence: 99%

Improving automatic segmentation of liver tumor images using a deep learning model

Song,

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Heliyon

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The Comparison of Retinal Vessel Segmentation Methods in Fundus Images

Luo

Jia

2020

J. Phys.: Conf. Ser.

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Due to complexity of retinal fundus images, they are usually affected with noise and lighting during image acquisition. It brings difficulty to segment retinal vessels accurately, so accurate retinal vessel segmentation is still a challenging task in fundus images analysis. Five kinds of typical retinal vessel segmentation methods are briefly introduced in this paper, which are based on thresholding, matched filtering, mathematical morphology, tracking, and deep learning. Each method has its own characteristics. The experiment results show that the method based on deep learning is the best segmentation method among them, which can effectively assist doctors to detect and diagnose cardiovascular and ophthalmic diseases in early stage, and provide the decision support for ophthalmic disease computer-aided diagnosis and establishment of large-scale screening system.

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A novel feature fusion model based on non-subsampled shear-wave transform for retinal blood vessel segmentation

Zhang

2023

COMSIS J

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Background: Fundus image is a projection of the inner surface of the eye, which can be used to analyze and judge the distribution of blood vessels on the retina due to its different shape, bifurcation and elongation. Vascular trees are the most stable features in medical images and can be used for biometrics. Ophthalmologists can effectively screen and determine the ophthalmic conditions of diabetic retinopathy, glaucoma and microaneurysms by the morphology of blood vessels presented in the fundus images. Traditional unsupervised learning methods include matched filtering method, morphological processing method, deformation model method, etc. However, due to the great difference in the feature complexity of different fundus image morphology, the traditional methods are relatively simple in coding, poor in the extraction degree of vascular features, poor in segmentation effect, and unable to meet the needs of practical clinical assistance. Methods: In this paper, we propose a new feature fusion model based on non-subsampled shearwave transform for retinal blood vessel segmentation. The contrast between blood vessels and background is enhanced by pre-processing. The vascular contour features and detailed features are extracted under the multi-scale framework, and then the image is postprocessed. The fundus images are decomposed into low frequency sub-band and high frequency sub-band by non-subsampled shear-wave transform. The two feature images are fused by regional definition weighting and guided filtering respectively, and the vascular detection image is obtained by calculating the maximum value of the corresponding pixels at each scale. Finally, the Otsu method is used for segmentation. Results: The experimental results on DRIVE data set show that the proposed method can accurately segment the vascular contour while retaining a large number of small vascular branches with high accuracy. Conclusion: The proposed method has a high accuracy and can perform vascular segmentation well on the premise of ensuring sensitivity.

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U-Shaped Retinal Vessel Segmentation Algorithm Based on Adaptive Scale Information

Cited by 3 publications

References 0 publications

Improving automatic segmentation of liver tumor images using a deep learning model

Improving automatic segmentation of liver tumor images using a deep learning model

The Comparison of Retinal Vessel Segmentation Methods in Fundus Images

A novel feature fusion model based on non-subsampled shear-wave transform for retinal blood vessel segmentation

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