A shortest path approach to optic disc detection in retinal fundus images

Supplementary Files

Figure 1: Example of algorithm flow.
Figure 2: Sampling of correctly detected images from the DRIVE database.
Figure 3: Correctly identified OD location in images from the DIARETDB1 database.
Figure 4: Correctly detected OD location in images from the Messidor database
Figure 5: All incorrectly detected images.
Figure 6: Corrected Images

How to Cite

Wigdahl J, Guimaraes P, Ruggeri A. A shortest path approach to optic disc detection in retinal fundus images. MAIO [Internet]. 2017 Jul. 7 [cited 2022 Dec. 2];1(4):29-42. Available from: https://www.maio-journal.com/index.php/MAIO/article/view/43

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graph theory; optic disc; retinal imaging; shortest path; template matching


Aim: To evaluate a new algorithm to detect the optic disc in retinal fundus images on a number of publicly available datasets.  Optic Disc detection is an important first step in many automated algorithms, either to be masked out of future processing or for the use in optic disc related disease such as glaucoma and papilledema.

Methods: We propose a new method for optic disc detection that converts the retinal image into a graph and exploits vessel enhancement methods to calculate edge weights in finding the shortest path between pairs of points on the periphery of the image.  The line segment with the maximum number of shortest paths is considered the optic disc location, with refinement from a combination template matching approach in the found region. The method was tested on three publicly available datasets: DRIVE, DIARETDB1, and Messidor consisting of 40, 89, and 1200 images.  All images were acquired at a 45°-50° field of view.

Results: The method achieves an accuracy of 100, 98.88, and 99.42% on the DRIVE, DIARETDB1, and Messidor databases respectively.

Conclusions: The method performs as well or better than state-of-the-art methods on these datasets.  Processing takes an average of 32 seconds (+-1.2) to detect the optic disc, with 26 of those seconds used for the vessel enhancement process.   The accuracy over a wide variety of images shows that the method is robust and would be optimal for retinal analysis systems that perform vessel enhancement as part of their processing.  



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