Detection of Retinal Blood Vessels Based on Nonlinear Projections

An automated method for blood vessel segmentation is presented in this paper. The approach uses the nonlinear orthogonal projection to capture the features of vessel networks, and derives a novel local adaptive thresholding algorithm for vessel detection. By embedding in a kind of image decomposition model, the selection of system parameter which reflects the size of concerned convex set is examined. This approach differs from previously known methods in that it uses matched filtering, vessel tracking or supervised methods. The algorithm was tested on two publicly available databases: the DRIVE and the STARE. By comparison with hand-labeled ground truth, an average accuracy of 96.1% is achieved on the former database, and an average accuracy of 90.8% is achieved on the later database.

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