Automated identification of retinal vessels using a multiscale directional contrast quantification (MDCQ) strategy

Abstract

Purpose:A novel algorithm is presented to automatically identify the retinal vessels depicted in color fundus photographs. Methods: The proposed algorithm quantifies the contrast of each pixel in retinal images at multiple scales and fuses the resulting consequent contrast images in a progressive manner by leveraging their spatial difference and continuity. The multiscale strategy is to deal with the variety of retinal vessels in width, intensity, resolution, and orientation; and the progressive fusion is to combine consequent images and meanwhile avoid a sudden fusion of image noise and/or artifacts in space. To quantitatively assess the performance of the algorithm, we tested it on three publicly available databases, namely, DRIVE, STARE, and HRF. The agreement between the computer results and the manual delineation in these databases were quantified by computing their overlapping in both area and length (centerline). The measures include sensitivity, specificity, and accuracy. Results: For the DRIVE database, the sensitivities in identifying vessels in area and length were around 90% and 70%, respectively, the accuracy in pixel classification was around 99%, and the precisions in terms of both area and length were around 94%. For the STARE database, the sensitivities in identifying vessels were around 90% in area and 70% in length, and the accuracy in pixel classification was around 97%. For the HRF database, the sensitivities in identifying vessels were around 92% in area and 83% in length for the healthy subgroup, around 92% in area and 75% in length for the glaucomatous subgroup, around 91% in area and 73% in length for the diabetic retinopathy subgroup. For all three subgroups, the accuracy was around 98%. Conclusions:The experimental results demonstrate that the developed algorithm is capable of identifying retinal vessels depicted in color fundus photographs in a relatively reliable manner.

Significant Enhancement of Segmentation Efficiency of Retinal Images Using Texture-Based Gabor Filter Approach Followed by Optimization Algorithm

“…The proposed model was applied and tested on DRIVE database (used in this work also), and showed average sensitivity as 77% while the average accuracy as 93.2%. Zhen et al (2014) worked on automatically identifying retinal vessels from fundus images using multi-scale directional contrast quantification (MDCQ) strategy and obtained moderate segmentation results on various retinal image databases.…”

Retinal Vascular Imaging in Vascular Cognitive Impairment: Current and Future Perspectives

“…Semi-automated computer-based methods to assess retinal vessel morphology are being developed, 20 which provide quantification of the arteriole-to-venule ratio, tortuosity index, and mean fractal dimension, between other parameters. Automatic techniques are also developed for retinal fundus photographs to serve retinal vessels identification, 57 segmentation, 58 quantitative assessment of retinal arteriolar central light reflex and vessel width, 29 , 59 hard exudates, 60 and retinal arteriovenous nicking. 61 Artificial intelligence methods demonstrate diabetic retinopathy detection and are potentially ready for prime use for retinal screening in patients with diabetes mellitus in primary care settings.…”