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Feature-Based Retinal Image Registration Using D-Saddle Feature

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 cons...

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Bibliographic Details
Main Authors: Ramli, Roziana, Idris, Mohd Yamani Idna, Hasikin, Khairunnisa, Karim, Noor Khairiah A., Wahab, Ainuddin Wahid Abdul, Ahmedy, Ismail, Ahmedy, Fatimah, Kadri, Nahrizul Adib, Arof, Hamzah
Format: Article
Language:English
Published: Hindawi Publishing Corporation 2017
Subjects:
QA75 Electronic computers. Computer science
R Medicine
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.um.edu.my/19047/1/Feature-Based_Retinal_Image_Registration_Using_D-Saddle_Feature.pdf
http://eprints.um.edu.my/19047/
http://dx.doi.org/10.1155/2017/1489524
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Summary: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.

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