Table of Contents
ISRN Machine Vision
Volume 2012 (2012), Article ID 424671, 6 pages
Research Article

Vessel Extraction of Conjunctival Images Using LBPs and ANFIS

1Machine Vision Laboratory, Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad 9177948944, Iran
2Mashhad University of Medical Sciences, Mashhad, Iran

Received 18 July 2011; Accepted 14 August 2011

Academic Editor: C.-C. Han

Copyright © 2012 Seyed Mohsen Zabihi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The main goal of medical imaging applications is to diagnose some diseases, try to prevent the progression of them, and actually cure the patients. The number of people that suffer from diabetes is growing very fast these recent years in many countries and it is needed to diagnose this disease in the beginning to prevent the subsequent side effects like blindness and so on. One of the first ways to detect this disease is analysis of vessels in some parts of the eye such as retina and conjunctiva. Some studies have been done on effects of vessel changes of conjunctiva in diabetes diagnosis and it is proved that conjunctival vessel extraction and analysis is a good way for this purpose. In this paper, we proposed a method to detect and extract the vessels of conjunctiva automatically. It is the first stage of the process of diabetes diagnosis. We first extract some textural features from each pixel of the conjunctiva image using LBP and then classify each pixel to vessels or nonvessels according to the features vector based on a supervised classifier, ANFIS. We tested the proposed algorithm on 40 conjunctival images to show the performance and efficiency of our method.