scholarly journals Evaluation of Interpolation Effects on Upsampling and Accuracy of Cost Functions-Based Optimized Automatic Image Registration

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Amir Pasha Mahmoudzadeh ◽  
Nasser H. Kashou
Keyword(s):  
Magnetic Resonance ◽  
High Resolution ◽  
Image Registration ◽  
Magnetic Resonance Images ◽  
Qualitative Assessment ◽  
Cost Functions ◽  
Low Resolution ◽  
Registration Method ◽  
B Spline ◽  
Joint Histogram

Interpolation has become a default operation in image processing and medical imaging and is one of the important factors in the success of an intensity-based registration method. Interpolation is needed if the fractional unit of motion is not matched and located on the high resolution (HR) grid. The purpose of this work is to present a systematic evaluation of eight standard interpolation techniques (trilinear, nearest neighbor, cubic Lagrangian, quintic Lagrangian, hepatic Lagrangian, windowed Sinc, B-spline 3rd order, and B-spline 4th order) and to compare the effect of cost functions (least squares (LS), normalized mutual information (NMI), normalized cross correlation (NCC), and correlation ratio (CR)) for optimized automatic image registration (OAIR) on 3D spoiled gradient recalled (SPGR) magnetic resonance images (MRI) of the brain acquired using a 3T GE MR scanner. Subsampling was performed in the axial, sagittal, and coronal directions to emulate three low resolution datasets. Afterwards, the low resolution datasets were upsampled using different interpolation methods, and they were then compared to the high resolution data. The mean squared error, peak signal to noise, joint entropy, and cost functions were computed for quantitative assessment of the method. Magnetic resonance image scans and joint histogram were used for qualitative assessment of the method.

Functional information from magnetic resonance imaging

1995 ◽  
Vol 53 ◽  
pp. 84-85
Author(s):  
Alan P. Koretsky ◽  
Afonso Costa e Silva ◽  
Yi-Jen Lin
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
High Resolution ◽  
Cancer Biology ◽  
Imaging Modality ◽  
Magnetic Resonance Images ◽  
Great Success ◽  
Functional Information ◽  
Resonance Imaging ◽  
High Resolution Mri

Magnetic resonance imaging (MRI) has become established as an important imaging modality for the clinical management of disease. This is primarily due to the great tissue contrast inherent in magnetic resonance images of normal and diseased organs. Due to the wide availability of high field magnets and the ability to generate large and rapidly switched magnetic field gradients there is growing interest in applying high resolution MRI to obtain microscopic information. This symposium on MRI microscopy highlights new developments that are leading to increased resolution. The application of high resolution MRI to significant problems in developmental biology and cancer biology will illustrate the potential of these techniques.In combination with a growing interest in obtaining high resolution MRI there is also a growing interest in obtaining functional information from MRI. The great success of MRI in clinical applications is due to the inherent contrast obtained from different tissues leading to anatomical information.

scholarly journals An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3425
Author(s):  
Xin Zhao ◽  
Hui Li ◽  
Ping Wang ◽  
Linhai Jing
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Feature Descriptor ◽  
Remote Sensing Images ◽  
Residual Network ◽  
Scale Invariant ◽  
Registration Method ◽  
Sensing Applications ◽  
Feature Based

Accurate registration for multisource high-resolution remote sensing images is an essential step for various remote sensing applications. Due to the complexity of the feature and texture information of high-resolution remote sensing images, especially for images covering earthquake disasters, feature-based image registration methods need a more helpful feature descriptor to improve the accuracy. However, traditional image registration methods that only use local features at low levels have difficulty representing the features of the matching points. To improve the accuracy of matching features for multisource high-resolution remote sensing images, an image registration method based on a deep residual network (ResNet) and scale-invariant feature transform (SIFT) was proposed. It used the fusion of SIFT features and ResNet features on the basis of the traditional algorithm to achieve image registration. The proposed method consists of two parts: model construction and training and image registration using a combination of SIFT and ResNet34 features. First, a registration sample set constructed from high-resolution satellite remote sensing images was used to fine-tune the network to obtain the ResNet model. Then, for the image to be registered, the Shi_Tomas algorithm and the combination of SIFT and ResNet features were used for feature extraction to complete the image registration. Considering the difference in image sizes and scenes, five pairs of images were used to conduct experiments to verify the effectiveness of the method in different practical applications. The experimental results showed that the proposed method can achieve higher accuracies and more tie points than traditional feature-based methods.

Analysis of serial magnetic resonance images of mouse brains using image registration

NeuroImage ◽  
2009 ◽  
Vol 44 (3) ◽  
pp. 692-700 ◽  
Author(s):  
Satheesh Maheswaran ◽  
Hervé Barjat ◽  
Simon T. Bate ◽  
Paul Aljabar ◽  
Derek L.G. Hill ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Image Registration ◽  
Magnetic Resonance Images

Adaptive filtering for high resolution magnetic resonance images

1996 ◽  
Vol 6 (2) ◽  
pp. 367-377 ◽  
Author(s):  
Kui Ying ◽  
Bradley D. Clymer ◽  
Petra Schmalbrock
Keyword(s):  
Magnetic Resonance ◽  
High Resolution ◽  
Adaptive Filtering ◽  
Magnetic Resonance Images

Anatomy of the Orbital Apex and Cavernous Sinus on High-Resolution Magnetic Resonance Images

2000 ◽  
Vol 44 (4) ◽  
pp. 303-323 ◽  
Author(s):  
Armin Ettl ◽  
Karin Zwrtek ◽  
Albert Daxer ◽  
Erich Salomonowitz
Keyword(s):  
Magnetic Resonance ◽  
High Resolution ◽  
Cavernous Sinus ◽  
Magnetic Resonance Images ◽  
Orbital Apex
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