High-precision fundus blood vessel extraction method, device, medium, equipment and system

Abstract

The embodiment of the invention provides a high-precision fundus blood vessel extraction method, a device, a medium and equipment. The method comprises the steps of obtaining a blood vessel pre-selection area image according to a fundus image; extracting a blood vessel center line of the blood vessel pre-selected region image; performing image segmentation processing on the blood vessel pre-selected region image to obtain an overall blood vessel region image; obtaining a first fundus blood vessel image according to the overall blood vessel area image and the blood vessel center line; extracting a second fundus blood vessel image from the fundus image according to a deep learning blood vessel feature detection model; and obtaining a final blood vessel segmentation image according to the first fundus blood vessel image and the second fundus blood vessel image. According to the method, based on combination of computer vision and artificial intelligence deep learning, the blood vessel segmentation image of the fundus image is extracted, organic combination and balance of the extraction precision and the extraction speed are achieved, and the robustness of the extraction result is improved.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a high-precision fundus blood vessel extraction method, device, medium, equipment and system. Background technique [0002] Retinal fundus image analysis helps doctors diagnose, screen and treat cardiovascular and cerebrovascular diseases and eye diseases, such as hypertensive retinopathy, arteriosclerosis, glaucoma, macular degeneration, diabetic retinopathy, etc. If left untreated, these diseases can cause blindness and even death. Blood vessel segmentation is one of the basic steps in fundus blood vessel measurement and retinal image analysis, which helps doctors better understand fundus abnormalities, abnormal severity and disease progression, especially for fundus complications caused by cardiovascular and cerebrovascular diseases. In clinical practice, manual labeling and measurement are usually required to obtain a fine-grained understanding of fundus vascular abnormalitie...

AI Technical Summary

Problems solved by technology

In clinical practice, manual labeling and measurement are usually required to obtain a fine-grained understanding of fundus vascular abnormalities, which is not only time-consuming, but also subject to the subjective influence of the labeler.

[0003] In the process of realizing the present invention, the inventors found that there are at least the following problems in the prior art: the existing high-precision fundus blood vessel extraction methods cannot achieve sub-pixel accuracy Level or above accuracy or the extraction of blood vessels is not accurate enough, and the robustness of blood vessel extraction is not strong

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