Open AccessFeasibility of level-set analysis of enface OCT retinal images in diabetic retinopathy
Author(s) -
Fatimah Mohammad,
Rashid Ansari,
Justin Wanek,
Andrew Francis,
Mahnaz Shahidi
Publication year - 2015
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.6.001904
Subject(s) - diabetic retinopathy , retinal , computer science , level set (data structures) , artificial intelligence , segmentation , pattern recognition (psychology) , level set method , prior probability , set (abstract data type) , image segmentation , measure (data warehouse) , image processing , computer vision , ophthalmology , image (mathematics) , medicine , data mining , diabetes mellitus , bayesian probability , programming language , endocrinology
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.