New Vector Field Regularization Techniques for Nonrigid Image Registration

2003 ◽  
pp. 1-10
Author(s):  
Pascal Cathier ◽  
Nicholas Ayache
Keyword(s):  
Vector Field ◽  
Image Registration ◽  
Nonrigid Image Registration ◽  
Regularization Techniques

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

Self-similarity weighted mutual information: A new nonrigid image registration metric

2014 ◽  
Vol 18 (2) ◽  
pp. 343-358 ◽  
Author(s):  
Hassan Rivaz ◽  
Zahra Karimaghaloo ◽  
D. Louis Collins
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Self Similarity ◽  
Nonrigid Image Registration

Nonrigid Image Registration Algorithm Based on Mutual Information Active Demons

2020 ◽  
Vol 57 (16) ◽  
pp. 161009
Author(s):  
张丹 Zhang Dan ◽  
黄欢 Huang Huan ◽  
尚振宏 Shang Zhenhong
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Nonrigid Image Registration ◽  
Registration Algorithm ◽  
Image Registration Algorithm

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

scholarly journals Nonrigid Registration of Lung CT Images Based on Tissue Features

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Rui Zhang ◽  
Wu Zhou ◽  
Yanjie Li ◽  
Shaode Yu ◽  
Yaoqin Xie
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Feature Detection ◽  
Ct Images ◽  
Image Process ◽  
Scale Invariant ◽  
Nonrigid Image Registration ◽  
Breathing Motion ◽  
Key Points ◽  
High Gradients

Nonrigid image registration is a prerequisite for various medical image process and analysis applications. Much effort has been devoted to thoracic image registration due to breathing motion. Recently, scale-invariant feature transform (SIFT) has been used in medical image registration and obtained promising results. However, SIFT is apt to detect blob features. Blobs key points are generally detected in smooth areas which may contain few diagnostic points. In general, diagnostic points used in medical image are often vessel crossing points, vascular endpoints, and tissue boundary points, which provide abundant information about vessels and can reflect the motion of lungs accurately. These points generally have high gradients as opposed to blob key points and can be detected by Harris. In this work, we proposed a hybrid feature detection method which can detect tissue features of lungs effectively based on Harris and SIFT. In addition, a novel method which can remove mismatched landmarks is also proposed. A series of thoracic CT images are tested by using the proposed algorithm, and the quantitative and qualitative evaluations show that our method is statistically significantly better than conventional SIFT method especially in the case of large deformation of lungs during respiration.

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