Accurate retinal blood vessels segmentation is an important step in the clinical diagnosis of ophthalmic diseases. Many deep learning frameworks have come up for retinal blood vessels segmentation tasks. However, the complex vascular structure and uncertain pathological features make blood vessel segmentation still very challenging. This paper proposes a novel multimodule concatenation via a U-shaped network for retinal vessels segmentation, which is based on atrous convolution and multikernel pooling. The proposed network structure retains three layers of the essential structure of U-Net, in which the atrous convolution combining the multikernel pooling blocks are designed to obtain more contextual information. The spatial attention module is concatenated with the dense atrous convolution module and the multikernel pooling module to form a multimodule concatenation. And different dilation rates are selected by cascading to acquire a larger receptive field in atrous convolution. Adequate comparative experiments are conducted on these public retinal datasets: DRIVE, STARE, and CHASE_DB1. The results show that the proposed method is effective, especially for microvessels. The code will be released at https://github.com/rocklijun/MC-UNet.

Many eye-related diseases will lead to blindness or worse when it is lack of treatment in the early stages of the disease. Retinal vessel is important for doctors to detect eye diseases, even though the increase of some thin vessels may also mean the occurrence of certain diseases. Therefore, automatic retinal vessel segmentation is of great help to doctors in diagnosing diseases. In this paper, an automatic vessel segmentation method is proposed for retinal image, which is based on support vector machine combining multi-scale feature fusion model and B-COSFIRE filter response. Firstly, the inverted green channel image is enhanced by B-COSFIRE filter to strengthen bar-like vessel structures. Then the features are extracted by means of line operator in a multiresolution way, namely that each filtered image is down-sampled to cover a wider area, hence each sampled pixels can obtain not only the global but also local information. Then the final obtained features from three scales together along the depth direction are combined to train the SVM model. Finally, we use the classifier model to predict blood vessels. The proposed algorithm is evaluated on the public available fundus images datasets (DRIVE: Precision = 0.8657, Se = 0.7088, Sp = 0.9660 and ACC = 0.9900; STARE: Precision = 0.8782, Se = 0.6189, Sp = 0.9908 and ACC = 0.9494). The experiment results show that our proposed algorithm has effects on retinal vessels segmentation.

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