GAN-based retinal vessel image intelligent segmentation method

Abstract

The invention discloses a GAN-based retinal vessel image intelligent segmentation method, and the method comprises the following steps: 1, giving a retinal image set, and dividing the set into a training set and a test set; 2, designing a generator network G and a discriminator network D, and constructing an Adam optimizer; 3, inputting the training set into G; 4, generating a blood vessel segmentation image through the G; 5, discriminating and calculating the segmented image generated by the G through the D; 6, updating G and D parameters; 7, evaluating the G and obtaining an optimal model G', and repeating the steps 3-7 until iteration is finished; and 8, inputting the retina image into the G' to generate a blood vessel segmentation image. According to the invention, a large receptive field network model is used to carry out intelligent segmentation on a retinal image to obtain a final retinal vessel segmentation image. The network model has good robustness, and the obtained blood vessel segmentation image contains less noise and is superior to an existing retinal blood vessel image segmentation method on the whole.

Description

technical field [0001] The invention belongs to the field of intelligent segmentation of retinal blood vessel images, in particular to a GAN-based intelligent segmentation method of retinal blood vessel images, which better solves the problem of low accuracy of retinal blood vessel image segmentation in existing retinal segmentation methods. Background technique [0002] In clinical medicine, doctors often analyze ophthalmology and some systemic diseases, such as diabetes, glaucoma, hypertension, cardiovascular and cerebrovascular diseases, etc., by observing the morphological characteristics of retinal images. The occurrence of these diseases generally affects the shape of the human retina, such as the number, branch, width and angle of retinal blood vessels. Therefore, the segmentation of color retinal images has become an important condition for ophthalmologists to judge diseases. However, using manual methods to segment color retinal images is not only time-consuming and...

AI Technical Summary

Problems solved by technology

[0003] The present invention is aimed at the problems of strong subjectivity and low efficiency in manual segmentation of retinal blood vessel images, and the problem of low segmentation accuracy in existing supervised retinal segmentation methods, and proposes a GAN-based intelligent retinal vessel image segmentation method

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