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Article Abstract
Pathology segmentation in retinal images of patients with diabetic retinopathy is important to help better understand disease processes. We propose an automated level-set method with Fourier descriptor-based shape priors. A cost function measures the difference between the current and expected output. We applied our method to enface images generated for seven retinal layers and determined correspondence of pathologies between retinal layers. We compared our method to a distance-regularized level set method and show the advantages of using well-defined shape priors. Results obtained allow us to observe pathologies across multiple layers and to obtain metrics that measure the co-localization of pathologies in different layers.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4467721 | PMC |
| http://dx.doi.org/10.1364/BOE.6.001904 | DOI Listing |
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