Feature-Based Retinal Image Registration by Enforcing Transformation-Guided and Robust Estimation

2017 ◽  
Author(s):  
Lifang Wei ◽  
Dong Heng ◽  
Changcai Yang ◽  
Riqing Chen
Keyword(s):  
Image Registration ◽  
Robust Estimation ◽  
Retinal Image ◽  
Feature Based

scholarly journals Feature-Based Retinal Image Registration Using D-Saddle Feature

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Roziana Ramli ◽  
Mohd Yamani Idna Idris ◽  
Khairunnisa Hasikin ◽  
Noor Khairiah A. Karim ◽  
Ainuddin Wahid Abdul Wahab ◽  
...  
Keyword(s):  
Image Registration ◽  
Retinal Image ◽  
Retinal Diseases ◽  
Feature Points ◽  
Registration Accuracy ◽  
Gaussian Pyramid ◽  
Difference Of Gaussian ◽  
Lower Success Rate ◽  
Feature Based ◽  
Image Pairs

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.

scholarly journals Non-rigid image registration using a modified fuzzy feature-based inference system for 3D cardiac motion estimation

2021 ◽  
Vol 205 ◽  
pp. 106085
Author(s):  
Monire Sheikh Hosseini ◽  
Mahammad Hassan Moradi ◽  
Mahdi Tabassian ◽  
Jan D'hooge
Keyword(s):  
Image Registration ◽  
Motion Estimation ◽  
Cardiac Motion ◽  
Inference System ◽  
Feature Based

A Comparison of Image Registration Methods Used for Printed Circuit Board Inspection

2008 ◽  
Vol 381-382 ◽  
pp. 295-298
Author(s):  
Shin Chieh Lin ◽  
C.T. Chen ◽  
C.H. Chou
Keyword(s):  
Image Registration ◽  
Printed Circuit Board ◽  
Circuit Board ◽  
Loading Test ◽  
Area Method ◽  
Printed Circuit ◽  
Feature Based ◽  
Position And Orientation ◽  
Inspection Machine

In this study, registration methods used to estimate both position and orientation differences between two images had been evaluated. This is an important issue since that there are always some position and orientation differences when loading test samples on the inspection machine. These differences should be calculated and compensated before further analysis. Registration methods tested including one area method and three feature based method. It was shown that the area method had better performance than other feature based method in these cases studied. And it is shown that it is much easy to detect defect by analyzing the subtracted image with position and orientation compensation instead of those without compensation.

scholarly journals Efficient feature-based image registration by mapping sparsified surfaces

2018 ◽  
Vol 55 ◽  
pp. 561-571 ◽  
Author(s):  
Chun Pang Yung ◽  
Gary P.T. Choi ◽  
Ke Chen ◽  
Lok Ming Lui
Keyword(s):  
Image Registration ◽  
Feature Based

Gaussian field estimator with manifold regularization for retinal image registration

2019 ◽  
Vol 157 ◽  
pp. 225-235 ◽  
Author(s):  
Jiahao Wang ◽  
Jun Chen ◽  
Huihui Xu ◽  
Shuaibin Zhang ◽  
Xiaoguang Mei ◽  
...  
Keyword(s):  
Image Registration ◽  
Retinal Image ◽  
Manifold Regularization ◽  
Gaussian Field
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