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Journal of Healthcare Engineering
Volume 2017, Article ID 1489524, 15 pages
https://doi.org/10.1155/2017/1489524
Research Article

Feature-Based Retinal Image Registration Using D-Saddle Feature

1Department of Computer System & Technology, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia
2Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
3Regenerative Medicine Cluster and Imaging Unit, Advanced Medical & Dental Institute, Universiti Sains Malaysia, Pulau Pinang, Malaysia
4Faculty of Medicine & Health Sciences, Universiti Malaysia Sabah, Sabah, Malaysia
5Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

Correspondence should be addressed to Mohd Yamani Idna Idris; ym.ude.mu@inamay

Received 9 June 2017; Revised 9 August 2017; Accepted 23 August 2017; Published 24 October 2017

Academic Editor: Robert Koprowski

Copyright © 2017 Roziana Ramli 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

Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle) to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE) Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%), Harris-PIIFD (4%), H-M (16%), and Saddle (16%). Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman) with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle.