Next Article in Journal / Special Issue
Applying a Dynamic Resource Supply Model in a Smart Grid
Previous Article in Journal / Special Issue
A Novel Contrast Enhancement Technique on Palm Bone Images
Article Menu

Export Article

Open AccessArticle
Algorithms 2014, 7(3), 456-470; doi:10.3390/a7030456

A Fovea Localization Scheme Using Vessel Origin-Based Parabolic Model

1
Department of Digital Living Innovation, Nan Kai University of Technology, 568, Chung-Cheng Rd., TsaoTun 542, Nantou County, Taiwan
2
Department of Computer Science and Engineering, National Chung-Hsing University, 250, Kuo-Kuang Rd., Taichung 402, Taiwan
3
Department of Electronic Engineering, National Chin-Yi University of Technology, 35, Lane 215, Sec. 1, Chung-Shan Rd., Taiping, Taichung 411, Taiwan
*
Author to whom correspondence should be addressed.
Received: 15 June 2014 / Revised: 28 August 2014 / Accepted: 28 August 2014 / Published: 10 September 2014
(This article belongs to the Special Issue Advanced Data Processing Algorithms in Engineering)
View Full-Text   |   Download PDF [1122 KB, uploaded 11 September 2014]   |  

Abstract

At the center of the macula, fovea plays an important role in computer-aided diagnosis. To locate the fovea, this paper proposes a vessel origin (VO)-based parabolic model, which takes the VO as the vertex of the parabola-like vasculature. Image processing steps are applied to accurately locate the fovea on retinal images. Firstly, morphological gradient and the circular Hough transform are used to find the optic disc. The structure of the vessel is then segmented with the line detector. Based on the characteristics of the VO, four features of VO are extracted, following the Bayesian classification procedure. Once the VO is identified, the VO-based parabolic model will locate the fovea. To find the fittest parabola and the symmetry axis of the retinal vessel, an Shift and Rotation (SR)-Hough transform that combines the Hough transform with the shift and rotation of coordinates is presented. Two public databases of retinal images, DRIVE and STARE, are used to evaluate the proposed method. The experiment results show that the average Euclidean distances between the located fovea and the fovea marked by experts in two databases are 9.8 pixels and 30.7 pixels, respectively. The results are stronger than other methods and thus provide a better macular detection for further disease discovery. View Full-Text
Keywords: vessel origin; vessel segmentation; parabolic model; fovea; Hough transform; feature selection vessel origin; vessel segmentation; parabolic model; fovea; Hough transform; feature selection
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Yu, C.-Y.; Liu, C.-C.; Yu, S.-S. A Fovea Localization Scheme Using Vessel Origin-Based Parabolic Model. Algorithms 2014, 7, 456-470.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top