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

Herbert F. Jelinek, Michael J. Cree, Jorge J.G. Leandro, João V.B. Soares, Roberto M. Cesar, A. Luckie

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

Proliferative diabetic retinopathy can lead to blindness. However, early recognition allows appropriate, timely intervention. Fluorescein-labeled retinal blood vessels of 27 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% confidence interval). The wavelet method was able to segment retinal blood vessels and classify the images according to the presence or absence of proliferative retinopathy.

Original languageBritish English
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
StatePublished - May 2007

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