Fast B-spline Transforms for Continuous Image Representation and Interpolation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of image registration techniques
ACM Computing Surveys (CSUR)
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Efficient implementation of image warping on a multimedia processor
Real-Time Imaging
Within-modality registration using intensity-based cost functions
Handbook of medical imaging
Curves and surfaces for CAGD: a practical guide
Curves and surfaces for CAGD: a practical guide
IPMI '93 Proceedings of the 13th International Conference on Information Processing in Medical Imaging
Eddy-Current Distortion Correction and Robust Tensor Estimation for MR Diffusion Imaging
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
An Image Registration Approach to Automated Calibration for Freehand 3D Ultrasound
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Algebraic decoding of a class of binary cyclic codes via Lagrange interpolation formula
IEEE Transactions on Information Theory
B-spline signal processing. II. Efficiency design and applications
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
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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 ofmotion 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. Themean 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.