Feature-Based Retinal Image Registration Using D-Saddle Feature

Roziana Ramli, Mohd Yamani Idna Idris*, Khairunnisa Hasikin, Noor Khairiah A. Karim, Ainuddin Wahid Abdul Wahab, Ismail Ahmedy, Fatimah Ahmedy, Nahrizul Adib Kadri, Hamzah Arof

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)
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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.

Original languageEnglish
Article number1489524
Number of pages16
JournalJournal of Healthcare Engineering
Volume2017
DOIs
Publication statusPublished - 24 Oct 2017
Externally publishedYes

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