Abstract
In 2003, a health IT programme for clinical decision support started in Lithuania. An initial goal was to create databases for ophthalmology images and to develop processing algorithms to extract diagnostically valuable information from images. We have investigated how vectors, consisting of the parameters derived from fundus images, are distributed and whether they form specific groups. When analysing the multidimensional patient data vectors, comprising all the 27 image parameters, it was impossible to separate the healthy eyes from the diseased ones. However, it was possible to simplify the system by eliminating redundant parameters and introducing new ones that represent a subset of parameters from the initial group. Thus it may prove possible to identify glaucoma using this system of parameters.
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