A blood vessel segmentation method for fundus images

Abstract

The embodiment of the present invention relates to a method for segmenting blood vessels in a fundus image. Each blood vessel feature processing layer sends an up-sampled image obtained by this layer to a connected blood vessel feature optimization layer; the lowest blood vessel feature optimization layer also acquires the lowest blood vessel feature. The feature image of the extraction layer; the blood vessel feature processing layer of the highest layer sends the up-sampled image obtained by this layer to the blood vessel feature optimization layer of the lowest layer through the backward short connection; each blood vessel feature optimization layer sequentially acquires each image. Performing blood vessel feature extraction and non-linear processing to obtain a non-linear image corresponding to each image; each blood vessel feature optimization layer sends each acquired image to a higher blood vessel feature optimization layer through forward short connections. In the embodiment of the present invention, the high-level information is sent to the low-level through the backward short connection, and the low-level information is sent to the high-level through the forward short connection, so that the features of all levels are fully integrated, and the blood vessel segmentation is more accurate.

Description

technical field [0001] Embodiments of the present invention relate to the field of computer technology, and in particular to a method for segmenting blood vessels in fundus images. Background technique [0002] Retinal fundus image analysis helps ophthalmologists to deal with the diagnosis, screening and treatment of cardiovascular and ophthalmic diseases, such as macular degeneration, diabetic retinopathy, glaucoma, hypertension, etc. These diseases can lead to blindness if left untreated. Vessel segmentation is a fundamental step in retinal image analysis and helps localize diabetic retinopathy and foveal regions. However, in clinical practice, manual labeling of blood vessels in retinal images is time-consuming and requires a lot of experience. Therefore, automatic retinal vessel segmentation is necessary to reduce labeling time. [0003] The automatic segmentation schemes of retinal image vessels in recent decades can be divided into two categories: unsupervised schem...

AI Technical Summary

Problems solved by technology

In the supervised scheme, the neural network model needs to extract image features layer by layer, and a lot of useful information is lost, resulting in the parameters learned by the neural network model not being able to fully describe the characteristics of blood vessels.

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