Automatic model-based tracing algorithm for vessel segmentation and diameter estimation.
An automatic algorithm capable of segmenting the whole vessel tree and calculate vessel diameter and orientation in a digital ophthalmologic image is presented in this work. The algorithm is based on a parametric model of a vessel that can assume arbitrarily complex shape and a simple measure of match that quantifies how well the vessel model matches a given angiographic image. An automatic vessel tracing algorithm is described that exploits the geometric model and actively seeks vessel bifurcation, without user intervention. The proposed algorithm uses the geometric vessel model to determine the vessel diameter at each detected central axis pixel. For this reason, the algorithm is fine tuned using a subset of ophthalmologic images of the publically available DRIVE database, by maximizing vessel segmentation accuracy. The proposed algorithm is then applied to the remaining ophthalmological images of the DRIVE database. The segmentation results of the proposed algorithm compare favorably in terms of accuracy with six other well established vessel detection techniques, outperforming three of them in the majority of the available ophthalmologic images. The proposed algorithm achieves subpixel root mean square central axis positioning error that outperforms the non-expert based vessel segmentation, whereas the accuracy of vessel diameter estimation is comparable to that of the non-expert based vessel segmentation.
Delibasis
Konstantinos K
KK
School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
Kechriniotis
Aristides I
AI
Tsonos
C
C
Assimakis
Nicholas
N
D000465:Algorithms; D001331:Automation; D006801:Humans; D008962:Models, Theoretical; D012171:Retinal Vessels / Q000033:anatomy & histology*