A framework for retinal vasculature segmentation based on matched filters. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Meng, Xianjing; Yin, Yilong; Yang, Gongping; Han, Zhe; Yan, Xiaowei
• Is Part Of: Biomedical engineering online. Volume 14:Issue 1(2015)
• Page Start: 1
• Page End: 20
• Publication Date: 2015-12
• Subjects: Improved gabor filter -- Multi-directional multi-scale second derivation of Gaussian -- Elongating filters -- Retinal vasculature segmentation; Biomedical engineering -- Periodicals; 610.2805
• DOI: 10.1186/s12938-015-0089-2
• Languages: English
• ISSNs: 1475-925X

Abstract

Abstract Background Automatic fundus image processing plays a significant role in computer-assisted retinopathy diagnosis. As retinal vasculature is an important anatomical structure in ophthalmic images, recently, retinal vasculature segmentation has received considerable attention from researchers. A segmentation method usually consists of three steps: preprocessing, segmentation, post-processing. Most of the existing methods emphasize on the segmentation step. In our opinion, the vessels and background can be easily separable when suitable preprocessing exists. Methods This paper represents a new matched filter-based vasculature segmentation method for 2-D retinal images. First of all, a raw segmentation is acquired by thresholding the images preprocessed using weighted improved circular gabor filter and multi-directional multi-scale second derivation of Gaussian. After that, the raw segmented image is fine-tuned by a set of novel elongating filters. Finally, we eliminate the speckle like regions and isolated pixels, most of which are non-vessel noises and miss-classified fovea or pathological regions. Results The performance of the proposed method is examined on two popularly used benchmark databases: DRIVE and STARE. The accuracy values are 95.29 and 95.69 %, respectively, without a significant degradation of specificity and sensitivity. Conclusion The performance of the proposed method is significantly better than almost all unsupervised methods, in addition, comparable to most of the existing supervised vasculature segmentation methods.