Abstract
We present a methodology for extracting the vascular network in the human retina using Dijkstra’s shortest-path algorithm. Our method preserves vessel thickness, requires no manual intervention, and follows vessel branching naturally and efficiently. To test our method, we constructed a retinal video indirect ophthalmoscopy (VIO) image database from pediatric patients and compared the segmentations achieved by our method and state-of-the-art approaches to a human-drawn gold standard. Our experimental results show that our algorithm outperforms prior state-of-the-art methods, for both single VIO frames and automatically generated, large field-of-view enhanced mosaics. We have made the corresponding dataset and source code freely available online.
Highlights
Accurate segmentation and evaluation of the anatomical and pathological features of retinal vessels are critical for the diagnosis and study of many ocular diseases
We have recently developed such a technique to combine several low-quality video indirect ophthalmoscopy (VIO) frames into a high-quality, large field-of-view (FOV) composite [29]
While Az is an adequate measure of classifier robustness, as we argue in Appendix A, we believe the F-measure is a much more appropriate measure than accuracy for analyzing segmentation results in this type of data
Summary
Accurate segmentation and evaluation of the anatomical and pathological features of retinal vessels are critical for the diagnosis and study of many ocular diseases. Increased dilation and tortuosity of the blood vessels in the posterior pole (called pre-plus in intermediate, and plus in severe circumstances) is an important indicator of ROP severity. Manual segmentation of retinal images is demanding for experts and excessively time-consuming for clinical use, but is inherently subjective, and different annotators often yield different results [4] To address these difficulties, different approaches for automated segmentation of retinal vessels have been tried, with varying levels of success
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