Eye fundus image vessel segmentation method based on local enhancement active contour module

Abstract

The invention relates to an eye fundus image vessel segmentation method based on a local enhancement active contour module. The method comprises: according to a feature vector of a Hessian matrix, vessel enhancement in an eye fundus image is carried out; curvature distribution statistics is carried out on the enhanced image to obtain an eyeball edge and get rid of the eyeball edge; and with a local enhancement active contour module, enhanced image segmentation is carried out by combining global energy information, thereby extracting an eye fundus vessel. According to the invention, on the basis of gray level distribution characteristics of the vessel in the medical eye fundus image, a local energy function is established and a global energy functional item is combined, so that the curve evolution process becomes stable; the speed is accelerated; and the vessel in the eye fundus image can be extracted precisely and effectively.

Description

technical field [0001] The invention relates to a blood vessel segmentation method of a fundus image, in particular to a blood vessel segmentation method of a fundus image based on a local enhanced active contour model. It is mainly used in the fields of medical images, computer vision and digital image processing technology. Background technique [0002] The morphological and structural characteristics of the retinal vascular network directly reflect the health of the fundus, as well as the course, severity, and prognosis of cardiovascular and cerebrovascular diseases such as diabetes and arteriosclerosis. Therefore, the results of blood vessel segmentation in fundus images directly affect the accuracy of fundus detection, and also affect the results of other subsequent processing and analysis. [0003] At present, researchers have proposed a large number of different algorithms, although most of these algorithms have achieved certain success in varying degrees, but there ...

AI Technical Summary

Problems solved by technology

However, the active contour model usually used for fundus blood vessel segmentation in medical images only uses the global grayscale features of the image, and has a good segmentation result for uniform grayscale fundus images, but it is difficult to achieve accurate segmentation for uneven grayscale fundus images.

[0004] Affected by factors such as the complex shape of blood vessels, low contrast of blood vessel edges, and image noise in medical fundus images, there is currently no general blood vessel segmentation algorithm that can be applied to all medical fundus images.

Moreover, for fundus images with lesions, the segmentation of the vascular network is more difficult due to the interference of the lesion area (such as hemorrhage, macula, etc.)

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