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Retinal Vessel Segmentation Using Supervised Classification Based on Multi-Scale Vessel Filtering and Gabor Wavelet

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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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  • Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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