scholarly journals Comparison of image registration methods for composing spectral retinal images

2017 ◽  
Vol 36 ◽  
pp. 234-245 ◽  
Author(s):  
Lauri Laaksonen ◽  
Ela Claridge ◽  
Pauli Fält ◽  
Markku Hauta-Kasari ◽  
Hannu Uusitalo ◽  
...  
Keyword(s):  
Image Registration ◽  
Retinal Images

scholarly journals Comparison of image registration methods for composing spectral retinal images

2014 ◽  
Author(s):  
Lauri Laaksonen ◽  
Ela Claridge ◽  
Pauli Falt ◽  
Markku Hauta-Kasari ◽  
Hannu Uusitalo ◽  
...  
Keyword(s):  
Image Registration ◽  
Retinal Images

Combining Feature Correspondence with Parametric Chamfer Alignment: Hybrid Two-Stage Registration for Ultra-Widefield Retinal Images

2021 ◽  
Author(s):  
Li Ding ◽  
Tony Kang ◽  
Ajay E. Kuriyan ◽  
Rajeev S. Ramchandran ◽  
Charles C. Wykoff ◽  
...  
Keyword(s):  
Image Registration ◽  
Fluorescein Angiography ◽  
Geometric Transformation ◽  
Retinal Images ◽  
Local Refinement ◽  
Registration Accuracy ◽  
Feature Correspondence ◽  
Geometric Distortions ◽  
Global Registration ◽  
Large Sets

<div>We propose a novel hybrid framework for registering retinal images in the presence of extreme geometric distortions that are commonly encountered in ultra-widefield (UWF) fluorescein angiography. Our approach consists of two stages: a feature-based global registration and a vessel-based local refinement. For the global registration, we introduce a modified RANSAC algorithm that jointly identifies robust matches between feature keypoints in reference and target images and estimates a polynomial geometric transformation consistent with the identified correspondences. Our RANSAC modification particularly improves feature point matching and the registration in peripheral regions that are most severely impacted by the geometric distortions. The second local refinement stage is formulated in our framework as a parametric chamfer alignment for vessel maps obtained using a deep neural network. Because the complete vessel maps contribute to the chamfer alignment, this approach not only improves registration accuracy but also aligns with clinical practice, where vessels are typically a key focus of examinations. We validate the effectiveness of the proposed framework on a new UWF fluorescein angiography (FA) dataset and on the existing narrow-field FIRE (fundus image registration) dataset and demonstrate that it significantly outperforms prior retinal image registration methods. The proposed approach enhances the utility of large sets of longitudinal UWF images by enabling: (a) automatic computation of vessel change metrics and (b) standardized and co-registered examination that can better highlight changes of clinical interest to physicians.</div>

scholarly journals Combining Feature Correspondence with Parametric Chamfer Alignment: Hybrid Two-Stage Registration for Ultra-Widefield Retinal Images

2021 ◽  
Author(s):  
Li Ding ◽  
Tony Kang ◽  
Ajay E. Kuriyan ◽  
Rajeev S. Ramchandran ◽  
Charles C. Wykoff ◽  
...  
Keyword(s):  
Image Registration ◽  
Fluorescein Angiography ◽  
Geometric Transformation ◽  
Retinal Images ◽  
Local Refinement ◽  
Registration Accuracy ◽  
Feature Correspondence ◽  
Geometric Distortions ◽  
Global Registration ◽  
Large Sets

<div>We propose a novel hybrid framework for registering retinal images in the presence of extreme geometric distortions that are commonly encountered in ultra-widefield (UWF) fluorescein angiography. Our approach consists of two stages: a feature-based global registration and a vessel-based local refinement. For the global registration, we introduce a modified RANSAC algorithm that jointly identifies robust matches between feature keypoints in reference and target images and estimates a polynomial geometric transformation consistent with the identified correspondences. Our RANSAC modification particularly improves feature point matching and the registration in peripheral regions that are most severely impacted by the geometric distortions. The second local refinement stage is formulated in our framework as a parametric chamfer alignment for vessel maps obtained using a deep neural network. Because the complete vessel maps contribute to the chamfer alignment, this approach not only improves registration accuracy but also aligns with clinical practice, where vessels are typically a key focus of examinations. We validate the effectiveness of the proposed framework on a new UWF fluorescein angiography (FA) dataset and on the existing narrow-field FIRE (fundus image registration) dataset and demonstrate that it significantly outperforms prior retinal image registration methods. The proposed approach enhances the utility of large sets of longitudinal UWF images by enabling: (a) automatic computation of vessel change metrics and (b) standardized and co-registered examination that can better highlight changes of clinical interest to physicians.</div>

Comparative study of NOTES alone vs. NOTES guided by a new image registration system for navigation in the mediastinum: A study in a porcine model

Endoscopy ◽  
2012 ◽  
Vol 44 (10) ◽  
Author(s):  
H Córdova ◽  
R San José Estépar ◽  
A Rodríguez-D'Jesús ◽  
G Martínez-Pallí ◽  
P Arguis ◽  
...  
Keyword(s):  
Image Registration ◽  
Comparative Study ◽  
Porcine Model ◽  
Registration System

Improved 3D Scene Modeling for Image Registration in Change Detection

2019 ◽  
Vol 2019 (7) ◽  
pp. 465-1-465-7
Author(s):  
Sjors van Riel ◽  
Dennis van de Wouw ◽  
Peter de With
Keyword(s):  
Image Registration ◽  
Change Detection ◽  
Scene Modeling ◽  
3D Scene
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