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Computational and Mathematical Methods in Medicine
Volume 2018, Article ID 6084798, 8 pages
https://doi.org/10.1155/2018/6084798
Research Article

Automated Segmentation Methods of Drusen to Diagnose Age-Related Macular Degeneration Screening in Retinal Images

Department of Biomedical Engineering, Gachon University College of Medicine, Incheon, Republic of Korea

Correspondence should be addressed to Kwang Gi Kim; rk.ca.nohcag@gkmik

Received 10 October 2017; Revised 13 January 2018; Accepted 6 February 2018; Published 12 March 2018

Academic Editor: Fumiharu Togo

Copyright © 2018 Young Jae Kim and Kwang Gi Kim. 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

Existing drusen measurement is difficult to use in clinic because it requires a lot of time and effort for visual inspection. In order to resolve this problem, we propose an automatic drusen detection method to help clinical diagnosis of age-related macular degeneration. First, we changed the fundus image to a green channel and extracted the ROI of the macular area based on the optic disk. Next, we detected the candidate group using the difference image of the median filter within the ROI. We also segmented vessels and removed them from the image. Finally, we detected the drusen through Renyi’s entropy threshold algorithm. We performed comparisons and statistical analysis between the manual detection results and automatic detection results for 30 cases in order to verify validity. As a result, the average sensitivity was 93.37% (80.95%~100%) and the average DSC was 0.73 (0.3~0.98). In addition, the value of the ICC was 0.984 (CI: 0.967~0.993, ), showing the high reliability of the proposed automatic method. We expect that the automatic drusen detection helps clinicians to improve the diagnostic performance in the detection of drusen on fundus image.