Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy

Herbert Jelinek, Cree Michael, Jorge Leandro, João Soares, Roberto M Cesar, Alan Luckie

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)
271 Downloads (Pure)

Abstract

Proliferative diabetic retinopathy can lead to blindness. However early recognition allows appropriate, timely intervention. Fluorescein-labelled retinal blood vessels of twenty-seven digital images were automatically segmented using the Gabor wavelet transform and classified using traditional features such as area, perimeter and an additional five morphological features based on the derivatives-of-Gaussian wavelet derived data. Discriminant analysis indicated that traditional features do not detect early proliferative retinopathy. The best single feature for discrimination was the wavelet curvature with an area under the curve (AUC) of 0.76. Linear discriminant analysis with a selection of six features achieved an AUC of 0.90 (0.73'0.97 95% CI). The wavelet method was able to segment retinal blood vessels and classify the images into proliferative retinopathy present or absent.
Original languageEnglish
Pages (from-to)1448-1456
Number of pages9
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Volume24
Issue number5
DOIs
Publication statusPublished - May 2007

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