Retinal Vessel Segmentation Using Supervised Classification Based on Multi-Scale Vessel Filtering and Gabor Wavelet
We present an automated segmentation method for blood vessels in images of the ocular fundus. The method uses a supervised classification of vessels at each pixel based on its feature vectors. The feature vectors include the responses of the pixel to the multi-scale vessel enhancement filtering and Gabor filtering at multiple scales and multiple orientations. We use a support vector machine to extract the vessels. The performance of the proposed method is evaluated on a DRIVE database. The accuracy of the vessel segmentation reaches more than 95%, which indicates the effectiveness of the proposed method.
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Document Type: Research Article
Publication date: December 1, 2015
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