Abstract
Fundus blood vessel image segmentation plays an important role in the diagnosis and treatment of diseases and is the basis of computer-aided diagnosis. Feature information from the retinal blood vessel image is relatively complicated, and the existing algorithms are sometimes difficult to perform effective segmentation with. Aiming at the problems of low accuracy and low sensitivity of the existing segmentation methods, an improved U-shaped neural network (MRU-NET) segmentation method for retinal vessels was proposed. Firstly, the image enhancement algorithm and random segmentation method are used to solve the problems of low contrast and insufficient image data of the original image. Moreover, smaller image blocks after random segmentation are helpful to reduce the complexity of the U-shaped neural network model; secondly, the residual learning is introduced into the encoder and decoder to improve the efficiency of feature use and to reduce information loss, and a feature fusion module is introduced between the encoder and decoder to extract image features with different granularities; and finally, a feature balancing module is added to the skip connections to resolve the semantic gap between low-dimensional features in the encoder and high-dimensional features in decoder. Experimental results show that our method has better accuracy and sensitivity on the DRIVE and STARE datasets (accuracy (ACC) = 0.9611, sensitivity (SE) = 0.8613; STARE: ACC = 0.9662, SE = 0.7887) than some of the state-of-the-art methods.
Highlights
Retinal blood vessel images are often used by doctors as a window to observe the response of various diseases, such as hypertension, coronary heart disease, and diabetes, and vessel abnormalities can reflect the severity of these diseases
Among them are (a) preprocessed images, (b) gold standard images manually segmented by experts, (c) images obtained by segmentation using U-Net models, and (d) images segmented by our proposed method
Through the comparison of experimental results, it can be found that adding feature fusion module can effectively improve the sensitivity (SE) of model segmentation and that the fundus image can be more detailed and effective segmentation; the addition of the Res-Dilated module and Resnet module can reduce the semantic gap between encoder and decoder and effectively improve the transmission of features and reduce the loss of features, so that the overall evaluation index of the model is improved
Summary
Retinal blood vessel images are often used by doctors as a window to observe the response of various diseases, such as hypertension, coronary heart disease, and diabetes, and vessel abnormalities can reflect the severity of these diseases. The fundus camera can directly take images of fundus blood vessels and can respond clearly to microvessels and lesions. In order to make an effective diagnosis of the disease, it is necessary to accurately segment blood vessels in the fundus. Because the retinal blood vessel image may have problems such as lesions, uneven lighting, noise, and low contrast between small blood vessels and the background, it is difficult to completely segment the fundus blood vessel image. Fundus blood vessel image segmentation has become a hot topic at home and abroad
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