Title Adaptive eye fundus vessel classification for automatic artery and vein diameter ratio evaluation /
Authors Stabingis, Giedrius ; Bernatavičienė, Jolita ; Dzemyda, Gintautas ; Paunksnis, Alvydas ; Stabingienė, Lijana ; Treigys, Povilas ; Vaičaitienė, Ramutė
DOI 10.15388/Informatica.2018.191
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Is Part of Informatica.. Vilnius : Vilniaus universitetas Matematikos ir informatikos institutas. 2018, vol. 29, no 4, p. 757-771.. ISSN 0868-4952. eISSN 1822-8844
Keywords [eng] automatic vessel classification ; vessel measurement ; artery-vein ratio ; eye fundus images
Abstract [eng] Eye fundus imaging is a useful, non-invasive tool in disease progress tracking, in early detection of disease and other cases. Often, the disease diagnosis is made by an ophthalmologist and automatic analysis systems are used only for support. There are several commonly used features for disease detection, one of them is the artery and vein ratio measured according to the width of the main vessels. Arteries must be separated from veins automatically in order to calculate the ratio, therefore, vessel classification is a vital step. For most analysis methods high quality images are required for correct classification. This paper presents an adaptive algorithm for vessel measurements without the necessity to tune the algorithm for concrete imaging equipment or a specific situation. The main novelty of the proposed method is the extraction of blood vessel features based on vessel width measurement algorithm and vessel spatial dependency. Vessel classification accuracy rates of 0.855 and 0.859 are obtained on publicly available eye fundus image databases used for comparison with another state of the art algorithms for vessel classification in order to evaluate artery-vein ratio (AV R). The method is also evaluated with images that represent artery and vein size changes before and after physical load. Optomed OY digital mobile eye fundus camera SmartscopeM5 PRO is used for image gathering.
Published Vilnius : Vilniaus universitetas Matematikos ir informatikos institutas
Type Journal article
Language English
Publication date 2018
CC license CC license description