
Exploratory Dijkstra forest based automatic vessel segmentation: applications in video indirect ophthalmoscopy (VIO).
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.
Duke Scholars
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- 5102 Atomic, molecular and optical physics
- 4003 Biomedical engineering
- 3212 Ophthalmology and optometry
- 0912 Materials Engineering
- 0205 Optical Physics
Citation

Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- 5102 Atomic, molecular and optical physics
- 4003 Biomedical engineering
- 3212 Ophthalmology and optometry
- 0912 Materials Engineering
- 0205 Optical Physics