Extraction method and electronic equipment of blood vessels in fundus image based on multi-feature fusion

Abstract

Embodiments of the present invention provide a method and electronic equipment for extracting blood vessels in fundus images based on multi-feature fusion. features, and perform feature fusion on the multiple different features; based on the comprehensive features after feature fusion, use the trained denseCRF model to obtain the segmented image of the fundus blood vessel; perform morphological analysis on the segmented image, extract A binary image of the fundus blood vessels. The embodiment of the present invention extracts the blood vessel image in the fundus image through the multi-feature fusion of the fundus blood vessel, and can extract the blood vessel edge image more effectively and accurately, including the fundus blood vessel edge image of the blurred image.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of image processing, and more specifically, to a method and electronic equipment for extracting blood vessels in fundus images based on multi-feature fusion. Background technique [0002] For the analysis of fundus images, more and more researchers pay attention to image semantic segmentation methods, such as threshold segmentation, edge segmentation, gene coding segmentation, wavelet transform segmentation, cluster segmentation, etc. With the development of artificial intelligence recognition technology, neural network (CNN) has attracted extensive attention of researchers and has been applied in the field of image segmentation. [0003] At present, there are more image semantic segmentation methods based on the full convolutional network FCN+CRF, and the image segmentation algorithm represented by FCN+denseCRF. The training samples are trained by the FCN model to obtain the spatial sh...

AI Technical Summary

Problems solved by technology

Because the training features are relatively single, the classification results obtained by the trained model are not fine enough and are not sensitive to the details in the image, so the extraction accuracy is not good enough, and the edge accuracy is not high, especially for blood vessel edge images of some blurred images. Insufficient detail extraction

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