We propose an algorithm for vessel extraction in retinal images. The first step consists of applying anisotropic diffusion filtering\nin the initial vessel network in order to restore disconnected vessel lines and eliminate noisy lines. In the second step, a multiscale\nline-tracking procedure allows detecting all vessels having similar dimensions at a chosen scale. Computing the individual image\nmaps requires different steps. First, a number of points are preselected using the eigenvalues of the Hessian matrix. These points are\nexpected to be near to a vessel axis. Then, for each preselected point, the response map is computed from gradient information of\nthe image at the current scale. Finally, the multiscale image map is derived after combining the individual image maps at different\nscales (sizes). Two publicly available datasets have been used to test the performance of the suggested method. The main dataset is\nthe STARE project�s dataset and the second one is the DRIVE dataset.The experimental results, applied on the STARE dataset, show\na maximum accuracy average of around 94.02%. Also, when performed on the DRIVE database, the maximum accuracy average\nreaches 91.55%.
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