Combined retinal vessel segmentation system and method based on fractal dimension number and Gauss filtering

Abstract

The invention discloses a combined retinal vessel segmentation system and method based on the fractal dimension number and Gauss filtering. The system comprises an image pre-processing unit used for improving retina image contrast and improving image quality to reduce interference of imaging problems on the subsequent processing process, a local detection unit used for extracting detailed portions of the retina image after pre-processing to improve fine vessel extraction accuracy, a global detection unit used for extracting a main portion of the retina image after pre-processing to highlight the global information of the retina vessel, a retina image fusion unit used for fusing the detailed portions of the retina image extracted by the local detection unit and the main portion of the retina image extracted by the global detection unit and improving system segmentation accuracy, and a retina image post-processing unit. The system is advantaged in that the fractal dimension number and Gauss filtering are combined, the system is suitable for segmentation of vessels of retina eyeground images, and segmentation accuracy is substantially improved.

Description

technical field [0001] The invention belongs to the field of medical image processing research, in particular to a retinal blood vessel segmentation system and method based on the combination of fractal dimension and Gaussian filter. Background technique [0002] Retinal microvessels are the only deep microvessels in the human body that can be directly observed without trauma. Changes in their state or structure can indicate the occurrence of various diseases, such as hypertension and diabetes. Therefore, the precise segmentation of the fundus retinal vascular structure can be used as an auxiliary means for clinicians to diagnose and treat diseases, and then through the analysis results of retinal blood vessels, the early diagnosis and prevention of some diseases can be realized. have their significance. [0003] However, due to the uneven gray distribution of retinal images, the complex structure of blood vessels, the low contrast between the target blood vessels and the i...

AI Technical Summary

Problems solved by technology

[0004] Most of the existing technologies are based on the above methods or a combination of the above methods. Although most of the retinal blood vessels can be extracted, the segmentation of small blood vessels is very difficult due to the many branch structures of the retinal blood vessel network, the low contrast between the small blood vessels and the background, and the blurred boundaries. , especially for small blood vessels with low contrast, the segmentation accuracy is not ideal and cannot meet the needs of use

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