Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Separation of the retinal vascular graph in arteries and veins based upon structural knowledge
Image and Vision Computing
Graph-based methods for retinal mosaicing and vascular characterization
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Properties and performance of a center/surround retinex
IEEE Transactions on Image Processing
A multiscale retinex for bridging the gap between color images and the human observation of scenes
IEEE Transactions on Image Processing
Precise segmentation of the optic disc in retinal fundus images
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
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A precise characterization of the retinal vessels into veins and arteries is necessary to develop automatic tools for diagnosis support. As medical experts, most of the existing methods use the vessel lightness or color for the classification, since veins are darker than arteries. However, retinal images often suffer from inhomogeneity problems in lightness and contrast, mainly due to the image capturing process and the curved retina surface. This fact and the similarity between both types of vessels make difficult an accurate classification, even for medical experts. In this paper, we propose an automatic approach for the retinal vessel classification that combines an image enhancement procedure based on the retinex theory and a clustering process performed in several overlapped areas within the retinal image. Experimental results prove the accuracy of our approach in terms of miss-classified and unclassified vessels.