A modular supervised algorithm for vessel segmentation in red-free retinal images
Computers in Biology and Medicine
Characterisation of Retinal Feature Points Applied to a Biometric System
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Characterisation of Feature Points in Eye Fundus Images
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
FABC: retinal vessel segmentation using adaboost
IEEE Transactions on Information Technology in Biomedicine
Accurate image registration for quadrature tomographic microscopy
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Computer Methods and Programs in Biomedicine
Detection of retinal vascular bifurcations by trainable V4-like filters
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Retina mosaicing using local features
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Miar'06 Proceedings of the Third international conference on Medical Imaging and Augmented Reality
Detection of retinal vascular bifurcations by rotation- and scale-invariant COSFIRE filters
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Pattern Recognition Letters
Retinal artery-vein caliber grading using color fundus imaging
Computer Methods and Programs in Biomedicine
Retinal artery and venular caliber grading: A semi-automated evaluation tool
Computers in Biology and Medicine
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A model-based algorithm, termed exclusion region and position refinement (ERPR), is presented for improving the accuracy and repeatability of estimating the locations where vascular structures branch and cross over, in the context of human retinal images. The goal is two fold. First, accurate morphometry of branching and crossover points (landmarks) in neuronal/vascular structure is important to several areas of biology and medicine. Second, these points are valuable as landmarks for image registration, so improved accuracy and repeatability in estimating their locations and signatures leads to more reliable image registration for applications such as change detection and mosaicing. The ERPR algorithm is shown to reduce the median location error from 2.04 pixels down to 1.1 pixels, while improving the median spread (a measure of repeatability) from 2.09 pixels down to 1.05 pixels. Errors in estimating vessel orientations were similarly reduced from 7.2° down to 3.8°.