Application of Low-Scales Vessel Detection in Retinal Vessel Segmentation

Abstract

Retinal image analysis has become the main non-invasive way to diagnose many diseases, and the extraction of blood vessels is the most important step. Supervised learning method has a good effect on blood vessel extraction. In order to further improve the accuracy of detection, a low-scales vessel detection (LVD) algorithm is proposed. In addition to a sub-network for extracting features in the original scale, two sub-networks for extracting features in the low scale are added, and the single output in the low scale is fused with the features in the original size, and the final output result is obtained after dimensionality reduction. Considering the structural characteristics of fundus vessels, an asymmetric depth-fixed sub-network (ADS) with deep layers and fewer parameters is designed in LVD. Tested in the public database DRIVE, only the green component of color fundus image and B-COSFIRE filter response are used as feature input. Its sensitivity, specificity, accuracy and AUC index are 0.8192, 0.9842,0.9695 and 0.9782, respectively, which reach the advanced level.

Keywords

• retinal vascular segmentation
• low-scales vessel detection (lvd)
• b-cosfire
• asymmetric depth-fixed sub-network (ads)