A novel vessel segmentation algorithm for pathological en-face images based on matched filter

Abstract

The vascular information in fundus images can provide important basis for detection and prediction of retina-related diseases. However, the presence of lesions such as Coroidal Neovascularization can seriously interfere with normal vascular areas in optical coherence tomography (OCT) fundus images. In this paper, a novel method is proposed for detecting blood vessels in pathological OCT fundus images. First of all, an automatic localization and filling method is used in preprocessing step to reduce pathological interference. Afterwards, in terms of vessel extraction, a pore ablation method based on capillary bundle model is applied. The ablation method processes the image after matched filter feature extraction, which can eliminate the interference caused by diseased blood vessels to a great extent. At the end of the proposed method, morphological operations are used to obtain the main vascular features. Experimental results on the dataset show that the proposed method achieves 0.88 ± 0.03, 0.79 ± 0.05, 0.66 ± 0.04, results in DICE, PRECISION and TPR, respectively. Effective extraction of vascular information from OCT fundus images is of great significance for the diagnosis and treatment of retinal related diseases.