Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIBGRAPI '01 Proceedings of the 14th Brazilian Symposium on Computer Graphics and Image Processing
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MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
IEEE Transactions on Information Technology in Biomedicine
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IEEE Transactions on Information Technology in Biomedicine
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Retinal vessel segmentation using a multi-scale medialness function
Computers in Biology and Medicine
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This paper studies the retinal vessel radius estimation and proposes a segmentation method for vessel center lines based on ridge descriptors. The study on radius estimation reveals that the radius estimation by the matched filters based on the second order derivatives of Gaussian kernels is only correct at the vessel center. The relation between the vessel radius and the scale of the Gaussian kernel in the estimation method based on the normalized largest curvature is also studied. The ridge descriptor proposed in this paper contains the normalized largest curvature and the orientations of gradients in the local neighborhood. For vessels of a certain scale, the distribution of the descriptors is assumed to be a normal distribution and is learned from a training set with known truth. Vessel center line segmentation can be then performed based on the distance between the ridge descriptor at candidate pixels and the learned model. Evaluation of the vessel center line segmentation based on the descriptors is done on both DRIVE and STARE databases using the receiver operating characteristic (ROC) curves. The areas under the ROC curves on DRIVE and STARE databases are 0.9584 and 0.9421 respectively.