Classification of artery and vein in retinal fundus images based on the context-dependent features

Yang Yan, Dunwei Wen, M. Ali Akber Dewan, Wen Bo Huang

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

3 Citations (Scopus)

Abstract

In this paper, we present an automatic method based on context-dependent characteristics for the detection and classification of arterial vessels and venous vessels in retinal fundus images. It provides a non-invasive opportunity and effective foundation for the diagnosis of several medical pathologies. In the proposed method, a combination of shifted filter responses is used, which can selectively respond to vessels. It achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussian filters, whose supports are aligned in a collinear manner. We then configure two combinations of shifted filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel detection by summing up the responses of the two filters. Then we extract the morphology and topological characteristics based on the vessel segmentation, and specifically present context-dependent features of blood vessels, including the shape, structure, relative position, context information and other important features. Based on these features, we use JointBoost classifier to construct potential function for conditional random fields (CRFs) model, and train the labeled samples to classify arteriovenous blood vessels in retinal images. The training and testing data sets were prepared according to the results based on DRIVE dataset provided by Estrada et al. The experimental results show that the accuracy of the proposed method for vein and artery detection is 91.1% and 94.5%, respectively, which is superior to that of the state-of-the-art methods. It can be used as a clinical reference for computer-assisted quantitative analysis of fundus images.

Original languageEnglish
Title of host publicationDigital Human Modeling
Subtitle of host publicationApplications in Health, Safety, Ergonomics, and Risk Management: Ergonomics and Design - 8th International Conference, DHM 2017 Held as Part of HCI International 2017, Proceedings
EditorsVincent G. Duffy
Pages198-213
Number of pages16
DOIs
Publication statusPublished - 2017
Event8th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management, DHM 2017, held as part of 19th International Conference on Human-Computer Interaction, HCI 2017 - Vancouver, Canada
Duration: 9 Jul. 201714 Jul. 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10286 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management, DHM 2017, held as part of 19th International Conference on Human-Computer Interaction, HCI 2017
Country/TerritoryCanada
CityVancouver
Period9/07/1714/07/17

Keywords

  • Classification of artery and vein
  • Combination of shifted filter responses
  • Conditional random fields
  • Context-dependent features
  • Retinal image analysis
  • Vessel segmentation

Fingerprint

Dive into the research topics of 'Classification of artery and vein in retinal fundus images based on the context-dependent features'. Together they form a unique fingerprint.

Cite this