Performance analysis of cone detection algorithms
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Date
2015-04-01Author
Mariotti, Letizia
Devaney, Nicholas
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Mariotti, Letizia, & Devaney, Nicholas. (2015). Performance analysis of cone detection algorithms. Journal of the Optical Society of America A, 32(4), 497-506. doi: 10.1364/JOSAA.32.000497
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Abstract
Many algorithms have been proposed to help clinicians evaluate cone density and spacing, as these may be
related to the onset of retinal diseases. However, there has been no rigorous comparison of the performance
of these algorithms. In addition, the performance of such algorithms is typically determined by comparison
with human observers. Here we propose a technique to simulate realistic images of the cone mosaic. We
use the simulated images to test the performance of two popular cone detection algorithms and we introduce
an algorithm which is used by astronomers to detect stars in astronomical images. We use Free Response
Operating Characteristic (FROC) curves to evaluate and compare the performance of the three algorithms.
This allows us to optimize the performance of each algorithm. We observe that performance is signicantly
enhanced by up-sampling the images. We investigate the eect of noise and image quality on cone mosaic
parameters estimated using the dierent algorithms, nding that the estimated regularity is the most sensitive
parameter.