Development of retinal blood vessel segmentation methodology using wavelet transforms for assessment of diabetic retinopathy

David Cornforth, Herbert Jelinek, Jorge Leandro, Joao Soares, Roberto Cesar, Michael Cree, Paul Mitchell, Terence Bossomaier

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel diameter and patterns in the retina. This paper describes the development of segmentation methodology in the processing of retinal blood vessel images obtained using non-mydriatic colour photography. The methods used include wavelet analysis, supervised classifier probabilities and adaptive threshold procedures, as well as morphology-based techniques. We show highly accurate identification of blood vessels for the purpose of studying changes in the vessel network that can be utilized for detecting blood vessel diameter changes associated with the pathophysiology of diabetes. In conjunction with suitable feature extraction and automated classification methods, our segmentation method could form the basis of a quick and accurate test for diabetic retinopathy, which would have huge benefits in terms of improved access to screening people for risk or presence of diabetes.
Original languageEnglish
Pages (from-to)50-61
Number of pages12
JournalComplexity international : an electronic journal of complex systems research
Volume11
Issue numbercornfo02
Publication statusPublished - 2005

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