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Computational and Mathematical Methods in Medicine
Volume 2018, Article ID 1942582, 12 pages
https://doi.org/10.1155/2018/1942582
Research Article

Automatic Optic Disc Detection in Color Retinal Images by Local Feature Spectrum Analysis

1College of Information Science and Engineering, Northeastern University, Shenyang, China
2Engineering Faculty, University of Sydney, NSW 2006, Austria
3Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China
4School of Software, Jiangxi Normal University, Nanchang, China

Correspondence should be addressed to Wei Zhou; moc.kooltuo@ueniewuohz and Chengdong Wu; nc.ude.uen.esi@gnodgnehcuw

Received 2 February 2018; Revised 15 May 2018; Accepted 23 May 2018; Published 14 June 2018

Academic Editor: Po-Hsiang Tsui

Copyright © 2018 Wei Zhou 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.

Abstract

The optic disc is a key anatomical structure in retinal images. The ability to detect optic discs in retinal images plays an important role in automated screening systems. Inspired by the fact that humans can find optic discs in retinal images by observing some local features, we propose a local feature spectrum analysis (LFSA) that eliminates the influence caused by the variable spatial positions of local features. In LFSA, a dictionary of local features is used to reconstruct new optic disc candidate images, and the utilization frequencies of every atom in the dictionary are considered as a type of “spectrum” that can be used for classification. We also employ the sparse dictionary selection approach to construct a compact and representative dictionary. Unlike previous approaches, LFSA does not require the segmentation of vessels, and its method of considering the varying information in the retinal images is both simple and robust, making it well-suited for automated screening systems. Experimental results on the largest publicly available dataset indicate the effectiveness of our proposed approach.