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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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
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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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spelling my.um.eprints.190472021-01-20T08:28:55Z http://eprints.um.edu.my/19047/ Feature-Based Retinal Image Registration Using D-Saddle Feature 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 QA75 Electronic computers. Computer science R Medicine TK Electrical engineering. Electronics Nuclear engineering 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. Hindawi Publishing Corporation 2017 Article PeerReviewed application/pdf en http://eprints.um.edu.my/19047/1/Feature-Based_Retinal_Image_Registration_Using_D-Saddle_Feature.pdf Ramli, Roziana and Idris, Mohd Yamani Idna and Hasikin, Khairunnisa and Karim, Noor Khairiah A. and Wahab, Ainuddin Wahid Abdul and Ahmedy, Ismail and Ahmedy, Fatimah and Kadri, Nahrizul Adib and Arof, Hamzah (2017) Feature-Based Retinal Image Registration Using D-Saddle Feature. Journal of Healthcare Engineering, 2017. pp. 1-15. ISSN 2040-2295 http://dx.doi.org/10.1155/2017/1489524 doi:10.1155/2017/1489524
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
language English
topic QA75 Electronic computers. Computer science
R Medicine
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle QA75 Electronic computers. Computer science
R Medicine
TK Electrical engineering. Electronics Nuclear engineering
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
Feature-Based Retinal Image Registration Using D-Saddle Feature
description 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.
format Article
author 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
author_facet 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
author_sort Ramli, Roziana
title Feature-Based Retinal Image Registration Using D-Saddle Feature
title_short Feature-Based Retinal Image Registration Using D-Saddle Feature
title_full Feature-Based Retinal Image Registration Using D-Saddle Feature
title_fullStr Feature-Based Retinal Image Registration Using D-Saddle Feature
title_full_unstemmed Feature-Based Retinal Image Registration Using D-Saddle Feature
title_sort feature-based retinal image registration using d-saddle feature
publisher Hindawi Publishing Corporation
publishDate 2017
url 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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score 13.18916