Retinal vessel segmentation method and device based on multi-scale matched filtering and particle swarm optimization

A particle swarm optimization and retinal blood vessel technology, applied in the field of image recognition, can solve the problems of poor segmentation effect, affecting disease diagnosis, loss and fracture of small blood vessels, etc., and achieve the effect of good overall segmentation performance and high sensitivity
CN113269756APending Publication Date: 2021-08-17CHANGCHUN UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
CHANGCHUN UNIV
Publication Date
2021-08-17

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Abstract

The invention discloses a retinal vessel segmentation method and device based on multi-scale matched filtering and particle swarm optimization. The method comprises the following steps: firstly, processing image brightness by using an MSR algorithm to improve the phenomenon of uneven illumination of an eye fundus image; secondly, applying multi-scale Gaussian matched filtering to enhance the contrast of a target blood vessel and a background; and finally, segmenting the image by using a particle swarm optimization OTSU multi-threshold. When a DRIVE data set is segmented, the accuracy rate, sensitivity and specificity of the DRIVE data set are 95.72%, 77.98% and 97.29% respectively. A segmentation result proves that the method is relatively high in accuracy and can be used for segmenting more small blood vessels.
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Description

technical field

[0001] The invention relates to the field of image recognition, in particular to a retinal blood vessel segmentation method based on multi-scale matching filtering and particle swarm optimization. Background technique

[0002] Retinal blood vessel image segmentation is an important topic in medical image research, which can effectively assist doctors in the rapid clinical diagnosis and treatment of cardiovascular diseases, diabetes and other diseases. In recent years, many scholars are studying the research content of retinal blood vessel image segmentation and have achieved certain results. However, accurate retinal vessel image segmentation is still a challenging task due to the complexity of retinal images and the influence of noise and illumination factors during image acquisition. [1] . At present, the retinal vessel segmentation methods proposed at home and abroad are mainly divided into supervised learning-based segmentation methods and unsupervised ...

Claims

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