Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy
JOSA A, Vol. 24, Issue 5, pp. 1448-1456 (2007)
http://dx.doi.org/10.1364/JOSAA.24.001448
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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.
© 2007 Optical Society of America
OCIS Codes
(070.5010) Fourier optics and signal processing : Pattern recognition
(100.5010) Image processing : Pattern recognition
ToC Category:
Clinical Applications of Retinal Imaging
History
Original Manuscript: August 15, 2006
Revised Manuscript: November 21, 2006
Manuscript Accepted: December 20, 2006
Published: April 11, 2007
Virtual Issues
Vol. 2, Iss. 6 Virtual Journal for Biomedical Optics
Citation
Herbert F. Jelinek, Michael J. Cree, Jorge J. G. Leandro, João V. B. Soares, Roberto M. Cesar, and A. Luckie, "Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy," J. Opt. Soc. Am. A 24, 1448-1456 (2007)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=josaa-24-5-1448
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