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An ensemble method based on supervised learning for segmenting the retinal vessels in color fundus images is proposed on the basis of previous work of Zhu et al. For each pixel, a 36 dimensional feature vector is extracted, including local features, morphological transformation with multi-scale and multi-orientation, and divergence of vector field which is firstly used to extract feature of retinal...
Manual segmentation of retinal blood vessels in optic fundus images is a tiresome task. Several methods have previously been proposed for the automatic segmentation of retinal blood vessels. In this paper we propose a classifier-based method. First the images are preprocessed so that the within class variability of the vessel and background classes are minimized. Next, the image is scanned with a...
A method is proposed for the representation of localised features using disjoint sub-images taken from several datasets of retinal images for use within an incremental learning system. A tile-based localised adaptive threshold selection method was taken for vessel segmentation based on separate colour components. Arteriole-venous differentiation was done using the composite of these components and...
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