Table of Contents
Journal of Medical Engineering
Volume 2013, Article ID 989712, 12 pages
Research Article

Application of Principal Component Analysis in Automatic Localization of Optic Disc and Fovea in Retinal Images

1Department of Physics and Applied Mathematics, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad 45650, Pakistan
2Isotope Application Division, Pakistan Institute of Nuclear Science and Technology, Nilore, Islamabad 45650, Pakistan

Received 12 November 2012; Revised 26 April 2013; Accepted 2 May 2013

Academic Editor: Nicusor Iftimia

Copyright © 2013 Asloob Ahmad Mudassar and Saira Butt. 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.


A retinal image has blood vessels, optic disc, fovea, and so forth as the main components of an image. Segmentation of these components has been investigated extensively. Principal component analysis (PCA) is one of the techniques that have been applied to segment the optic disc, but only a limited work has been reported. To our knowledge, fovea segmentation problem has not been reported in the literature using PCA. In this paper, we are presenting the segmentation of optic disc and fovea using PCA. The PCA was trained on optic discs and foveae using ten retinal images and then applied on seventy retinal images with a success rate of 97% in case of optic discs and 94.3% in case of fovea. Conventional algorithms feed one patch at a time from a test retinal image, and the next patch separated by one pixel part is fed. This process is continued till the full image area is covered. This is time consuming. We are suggesting techniques to cut down the processing time with the help of binary vessel tree of a given test image. Results are presented to validate our idea.