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Ridge-based vessel segmentation in color images of the retina
TLDR
A method is presented for automated segmentation of vessels in two-dimensional color images of the retina based on extraction of image ridges, which coincide approximately with vessel centerlines, which is compared with two recently published rule-based methods. Expand
Comparative study of retinal vessel segmentation methods on a new publicly available database
TLDR
This work compares the performance of a number of vessel segmentation algorithms on a newly constructed retinal vessel image database and defines the segmentation accuracy with respect to the gold standard as the performance measure. Expand
Automatic detection of red lesions in digital color fundus photographs
TLDR
A novel red lesion detection method is presented based on a hybrid approach, combining prior works by Spencer et al. (1996) and Frame (1998) with two important new contributions, including a new red lesions candidate detection system based on pixel classification. Expand
Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs
TLDR
The overall results show that microaneurysm detection is a challenging task for both the automatic methods as well as the human expert, and there is room for improvement as the best performing system does not reach the performance of thehuman expert. Expand
Automated Measurement of the Arteriolar-to-Venular Width Ratio in Digital Color Fundus Photographs
TLDR
An automated method to estimate the AVR in retinal color images by detecting the location of the optic disc, determining an appropriate region of interest (ROI), classifying vessels as arteries or veins, estimating vessel widths, and calculating the A VR is presented. Expand
Fast detection of the optic disc and fovea in color fundus photographs
TLDR
A fully automated, fast method to detect the fovea and the optic disc in digital color photographs of the retina is presented and combines cues measured directly in the image with cues derived from a segmentation of the retinal vasculature. Expand
Segmentation of the Optic Disc, Macula and Vascular Arch in Fundus Photographs
TLDR
An automatic system is presented to find the location of the major anatomical structures in color fundus photographs; the optic disc, the macula, and the vascular arch by fitting a single point-distribution-model to the image, that contains points on each structure. Expand
Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.
TLDR
A deep-learning enhanced algorithm for the automated detection of DR, achieves significantly better performance than a previously reported, otherwise essentially identical, algorithm that does not employ deep learning. Expand
On Combining Computer-Aided Detection Systems
TLDR
Generic methods to combine multiple CAD systems and investigate what kind of performance increase can be expected are presented. Expand
Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening
TLDR
A system is presented that can automatically determine whether the quality of a retinal screening image is sufficient for automatic analysis, based on the assumption that an image of sufficient quality should contain particular image structures according to a certain pre-defined distribution. Expand
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