| 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 wavelet transform methodology in the processing of images obtained using non-mydriatic colour photography. Development of vessel segmentation incorporates wavelet analysis, supervised classifier probabilities, adaptive threshold procedures as well as morphology-based techniques. We show highly accurate identification of blood vessels for the purpose of identifying 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 of people at risk of diabetes or people with diabetes. |
| Authors: |
D. J. Cornforth, H. J. Jelinek, J. J. G. Leandro, J. V. B. Soares, R. M .Cesar, Jr., M. J. Cree, P. Mitchell, T. Bossomaier |
| Affiliation: |
Charles Sturt University, Australia; Computer Science, University of Sao Paulo, Brazil; Physics and Electronic Engineering, University of Waikato, New Zealand; University of Sydney Department of Ophthalmology, Westmead Hospital, Australia |