Feature-Based Retinal Image Registration Using D-Saddle Feature

Ramli, R. and Idris, Mohd Yamani Idna and Hasikin, K. and Karim, N.K.A. and Wahab, A.W.A. and Ahmedy, I. and Ahmedy, F. and Kadri, N.A. and Arof, Hamzah (2017) Feature-Based Retinal Image Registration Using D-Saddle Feature. Journal of Healthcare Engineering, 2017. pp. 1-15. ISSN 2040-2295

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Official URL: http://dx.doi.org/10.1155/2017/1489524

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.

Item Type: Article
Uncontrolled Keywords: Diagnosis; Eye protection; Image registration; Image segmentation; Ophthalmology
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 30 Aug 2018 08:48
Last Modified: 11 Oct 2018 04:07
URI: http://eprints.um.edu.my/id/eprint/19047

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