The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Flux Maximizing Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Patch Alignment for Dimensionality Reduction
IEEE Transactions on Knowledge and Data Engineering
Binary sparse nonnegative matrix factorization
IEEE Transactions on Circuits and Systems for Video Technology
Efficient implementation for spherical flux computation and its application to vascular segmentation
IEEE Transactions on Image Processing
IEEE Transactions on Information Technology in Biomedicine
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A steerable complex wavelet construction and its application to image denoising
IEEE Transactions on Image Processing
A Variational Method for Geometric Regularization of Vascular Segmentation in Medical Images
IEEE Transactions on Image Processing
Footwear for Gender Recognition
IEEE Transactions on Circuits and Systems for Video Technology
Sparse Representation Classifier for microaneurysm detection and retinal blood vessel extraction
Information Sciences: an International Journal
Automatic detection of optic disc from retinal fundus images using dynamic programming
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Blood vessel segmentation methodologies in retinal images - A survey
Computer Methods and Programs in Biomedicine
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Automatic segmentation of retinal blood vessels has become a necessary diagnostic procedure in ophthalmology. The blood vessels consist of two types of vessels, i.e., thin vessels and wide vessels. Therefore, a segmentation method may require two different processes to treat different vessels. However, traditional segmentation algorithms hardly draw a distinction between thin and wide vessels, but deal with them together. The major problems of these methods are as follows: (1) If more emphasis is placed on the extraction of thin vessels, the wide vessels tend to be over detected; and more artificial vessels are generated, too. (2) If more attention is paid on the wide vessels, the thin and low contrast vessels are likely to be missing. To overcome these problems, a novel scheme of extracting the retinal vessels based on the radial projection and semi-supervised method is presented in this paper. The radial projection method is used to locate the vessel centerlines which include the low-contrast and narrow vessels. Further, we modify the steerable complex wavelet to provide better capability of enhancing vessels under different scales, and construct the vector feature to represent the vessel pixel by line strength. Then, semi-supervised self-training is used for extraction of the major structures of vessels. The final segmentation is obtained by the union of the two types of vessels. Our approach is tested on two publicly available databases. Experiment results show that the method can achieve improved detection of thin vessels and decrease false detection of vessels in pathological regions compared to rival solutions.