• Users Online: 479
  • Home
  • Print this page
  • Email this page
Home Current issue Ahead of print Search About us Abstracting and Indexing Editorial board Archives Submit article Instructions Subscribe Contacts Login 
ORIGINAL ARTICLE
Year : 2010  |  Volume : 5  |  Issue : 1  |  Page : 20-26

An Automated Tracking Approach for Extraction of Retinal Vasculature in Fundus Images


Computer Science Department, Shahid Chamran University, Ahvaz, Iran

Correspondence Address:
Alireza Osareh
Associate Professor of Computer Engineering; Computer Engineering Group, Engineering Faculty, Shahid Chamran University, Ahvaz
Iran
Login to access the Email id

Source of Support: None, Conflict of Interest: None


Rights and PermissionsRights and Permissions

Purpose: To present a novel automated method for tracking and detection of retinal blood vessels in fundus images. Methods: For every pixel in retinal images, a feature vector was computed utilizing multiscale analysis based on Gabor filters. To classify the pixels based on their extracted features as vascular or non-vascular, various classifiers including Quadratic Gaussian (QG), K-Nearest Neighbors (KNN), and Neural Networks (NN) were investigated. The accuracy of classifiers was evaluated using Receiver Operating Characteristic (ROC) curve analysis in addition to sensitivity and specificity measurements. We opted for an NN model due to its superior performance in classification of retinal pixels as vascular and non-vascular. Results: The proposed method achieved an overall accuracy of 96.9%, sensitivity of 96.8%, and specificity of 97.3% for identification of retinal blood vessels using a dataset of 40 images. The area under the ROC curve reached a value of 0.967. Conclusion: Automated tracking and identification of retinal blood vessels based on Gabor filters and neural network classifiers seems highly successful. Through a comprehensive optimization process of operational parameters, our proposed scheme does not require any user intervention and has consistent performance for both normal and abnormal images.


[PDF]*
Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed329    
    Printed14    
    Emailed0    
    PDF Downloaded76    
    Comments [Add]    

Recommend this journal