Dataset Information

Feature-Based Retinal Image Registration Using D-Saddle Feature.

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.

SUBMITTER: Ramli R

PROVIDER: S-EPMC5674727 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Publications

Feature-Based Retinal Image Registration Using D-Saddle Feature.

Ramli Roziana R Idris Mohd Yamani Idna MYI Hasikin Khairunnisa K A Karim Noor Khairiah NK Abdul Wahab Ainuddin Wahid AW Ahmedy Ismail I Ahmedy Fatimah F Kadri Nahrizul Adib NA Arof Hamzah H

Journal of healthcare engineering 20171024

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 ...[more]

PMID: 29204257

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Dataset Information

Feature-Based Retinal Image Registration Using D-Saddle Feature.

Publications

Feature-Based Retinal Image Registration Using D-Saddle Feature.

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