Retinal Vessel Segmentation Based on B-COSFIRE Filters in Fundus Images - Li, Wenjing; Xiao, Yalong; Hu, Hangyu; Zhu, Chengzhang; Wang, Han; Liu, Zixi; Sangaiah, Arun Kumar (Frontiers in Public Health, 2022)
Retinal vessel extraction plays an important role in the diagnosis of several medical pathologies such as diabetic retinopathy, glaucoma and so on. In this paper, we propose an efficient method based on B-COSFIRE filter to tackle two challanging problems in fundus vessels segmentation, which are (i) Difficulties in improving segmentation performance and time efficiency together and (ii) Difficulties in distinguishing the thin vessel from the vessel like noise. In the proposed method, firstly we used Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement, then excerpted Region of Interest (ROI) by thresholding the luminosity plane of the CIELab version of the original RGB image. We employed a set of B-COSFIRE filters to detect vessels and morphological filters to remove noise. Binary threshold was used for vessels segmentation. Finally, a post-processing method based on connected domains was used to eliminate unconnected non-vessel pixels and to obtain the final vessel image. Based on the binary vessel map obtained, we attempt to evaluate the performance of the proposed algorithm on three publicly available databases (DRIVE, STARE and CHASEDB1 database) of manually labeled images. The proposed method requires little processing time (around 12 seconds for each image) and results in the average accuracy, sensitivity and specificity of 0.9604, 0.7339 and 0.9847 for DRIVE database, 0.9558, 0.8003 and 0.9705 for STARE database respectively. The results demonstrate that the proposed method has potential capability in computer-aided diagnosis.