A manually-labeled, artery/vein classified benchmark for the DRIVE dataset

@article{Qureshi2013AMA,
  title={A manually-labeled, artery/vein classified benchmark for the DRIVE dataset},
  author={Touseef Ahmad Qureshi and Maged Habib and Andrew Hunter and Bashir Al-Diri},
  journal={Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems},
  year={2013},
  pages={485-488}
}
  • T. A. QureshiM. Habib B. Al-Diri
  • Published 20 June 2013
  • Computer Science
  • Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems
The classification of retinal vessels into arteries and veins is an important step for the analysis of retinal vascular trees, for which the scientists have proposed several classification methods. [] Key Method The labeling criterion is set after a careful analysis of the physiological facts about the retinal vascular system. In addition, the labeling process also includes several versions of original images to get certainty.

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References

SHOWING 1-10 OF 10 REFERENCES

An improved system for the automatic estimation of the Arteriolar-to-Venular diameter Ratio (AVR) in retinal images

An improved system to compute Arteriolar-to-Venular diameter Ratio (AVR) in a totally automatic way is developed, related to post-processing algorithms to enhance vessel tracking and a totally new artery/vein discrimination technique.

Blood vessel classification into arteries and veins in retinal images

An improved method for vessel classification is proposed that compares two feature extraction methods and two classification methods based on support vector machines and neural networks and achieves 95.32% correctly classified vessel pixels.

Ridge-based vessel segmentation in color images of the retina

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.

Automatic localization of the optic disc in retinal fundus images using multiple features

The proposed algorithm involves prior domain knowledge such as the optic disc size, cup-to-disc ratio and vessel convergence feature to evaluate the confidence level for the candidate region(s) at each thresholding level and heuristically decides whether or not to opt for multi-scheme policy for a given image.

DIARETDB 0 : Evaluation Database and Methodology for Diabetic Retinopathy Algorithms

In this study, problems and issues related to the database are discussed from medical, image processing, and security perspectives and an evaluation methodology is proposed and a prototype image database with the ground truth is described.

REVIEW - A reference data set for retinal vessel profiles

This paper describes REVIEW, a new retinal vessel reference dataset. This dataset includes 16 images with 193 vessel segments, demonstrating a variety of pathologies and vessel types. The vessel

Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels

An automated method to locate the optic nerve in images of the ocular fundus using a novel algorithm the authors call fuzzy convergence to determine the origination of the blood vessel network is described.

Detection of Retinal Vascular Bifurcations by Trainable V4-Like Filters

A novel method to detect vascular bifurcations in retinal fundus images that is implemented in trainable filters that mimic the properties of shape-selective neurons in area V4 of visual cortex is proposed.

Automated Measurements of Retinal Bifurcations

The angles and relative diameters of blood vessels in 230 bifurcations were measured using a new automated procedure, and used to calculate the values of several features with known theoretical properties, which agree with theoretical prediction measurements with slightly different bias.

Manual measurement of retinal bifurcation features

A new computerized tool for accurate manual measurement of features of retinal bifurcation geometry, designed for use in investigating correlations between measurement features and clinical conditions is introduced.