Gabor Feature-Based LogDemons With Inertial Constraint for Nonrigid Image Registration

2020 ◽  
Vol 29 ◽  
pp. 8238-8250 ◽  
Author(s):  
Ying Wen ◽  
Cheng Xu ◽  
Yue Lu ◽  
Qingli Li ◽  
Haibin Cai ◽  
...  
Keyword(s):  
Image Registration ◽  
Nonrigid Image Registration ◽  
Gabor Feature ◽  
Feature Based

Investigation of Feature-Based Nonrigid Image Registration Using Gaussian Process

2020 ◽  
pp. 156-162 ◽  
Author(s):  
Siming Bayer ◽  
Ute Spiske ◽  
Jie Luo ◽  
Tobias Geimer ◽  
William M. Wells III ◽  
...  
Keyword(s):  
Image Registration ◽  
Gaussian Process ◽  
Nonrigid Image Registration ◽  
Feature Based

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

scholarly journals Fully Automated Targeting Using Nonrigid Image Registration Matches Accuracy and Exceeds Precision of Best Manual Approaches to Subthalamic Deep Brain Stimulation Targeting in Parkinson Disease

Neurosurgery ◽  
2015 ◽  
Vol 76 (6) ◽  
pp. 756-765 ◽  
Author(s):  
Srivatsan Pallavaram ◽  
Pierre-François D'Haese ◽  
Wendell Lake ◽  
Peter E. Konrad ◽  
Benoit M. Dawant ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Image Registration ◽  
Parkinson Disease ◽  
Brain Stimulation ◽  
Red Nucleus ◽  
Nonrigid Image Registration ◽  
Direct Targeting ◽  
Fully Automatic ◽  
Stn Dbs ◽  
Deep Brain

Abstract BACKGROUND: Finding the optimal location for the implantation of the electrode in deep brain stimulation (DBS) surgery is crucial for maximizing the therapeutic benefit to the patient. Such targeting is challenging for several reasons, including anatomic variability between patients as well as the lack of consensus about the location of the optimal target. OBJECTIVE: To compare the performance of popular manual targeting methods against a fully automatic nonrigid image registration-based approach. METHODS: In 71 Parkinson disease subthalamic nucleus (STN)-DBS implantations, an experienced functional neurosurgeon selected the target manually using 3 different approaches: indirect targeting using standard stereotactic coordinates, direct targeting based on the patient magnetic resonance imaging, and indirect targeting relative to the red nucleus. Targets were also automatically predicted by using a leave-one-out approach to populate the CranialVault atlas with the use of nonrigid image registration. The different targeting methods were compared against the location of the final active contact, determined through iterative clinical programming in each individual patient. RESULTS: Targeting by using standard stereotactic coordinates corresponding to the center of the motor territory of the STN had the largest targeting error (3.69 mm), followed by direct targeting (3.44 mm), average stereotactic coordinates of active contacts from this study (3.02 mm), red nucleus-based targeting (2.75 mm), and nonrigid image registration-based automatic predictions using the CranialVault atlas (2.70 mm). The CranialVault atlas method had statistically smaller variance than all manual approaches. CONCLUSION: Fully automatic targeting based on nonrigid image registration with the use of the CranialVault atlas is as accurate and more precise than popular manual methods for STN-DBS.

Simultaneous Multiresolution Strategies for Nonrigid Image Registration

2013 ◽  
Vol 22 (12) ◽  
pp. 4905-4917 ◽  
Author(s):  
Wei Sun ◽  
Wiro J. Niessen ◽  
Marijn van Stralen ◽  
Stefan Klein
Keyword(s):  
Image Registration ◽  
Nonrigid Image Registration

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
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