Medical Imaging
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1 Medical Imaging, SS-2010 Mohammad Dawood Medical Imaging Mohammad Dawood Department of Computer Science University of Münster Germany
description
Medical Imaging. Mohammad Dawood Department of Computer Science University of Münster Germany. Image Registration. Registration T : Transformation In this lecture Floating image : The image to be registered Target image : The stationary image. Registration Linear Transformations - PowerPoint PPT Presentation
Transcript of Medical Imaging
Medical Imaging
Mohammad Dawood
Department of Computer Science
University of MünsterGermany
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Medical Imaging, SS-2010
Mohammad Dawood
Image Registration
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Medical Imaging, SS-2010
Mohammad Dawood
Registration
T : Transformation
In this lecture
Floating image : The image to be registered
Target image : The stationary image
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Medical Imaging, SS-2010
Mohammad Dawood
Registration
Linear Transformations
- Translation
- Rotation
- Scaling
- Affine
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Medical Imaging, SS-2010
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Registration
3D Translation
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Medical Imaging, SS-2010
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Registration
3D Rotation
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Medical Imaging, SS-2010
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Registration
3D Scaling
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Medical Imaging, SS-2010
Mohammad Dawood
Registration
Rigid registration
Angles are preserved Parallel lines remain parallel
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Medical Imaging, SS-2010
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Registration
Affine registration
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Medical Imaging, SS-2010
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Registration
Feature Points
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Medical Imaging, SS-2010
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Registration
Feature Points
1. De-mean
2. Compute SVD
3. Calculate the transform
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Medical Imaging, SS-2010
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Registration
Feature Points
Iterative Closest Points Algorithm (ICP)
1. Associate points by the nearest neighbor criteria.2. Estimate transformation parameters using a mean square cost function.3. Apply registration and update parameters.
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Medical Imaging, SS-2010
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Registration
Feature Points
Random Sample Consensus Algorithm (RNSAC)
1. Transformation is calculated from hypothetical inliers2. All other data are then tested against the fitted model and, if a point fits well to the model, also considered as a hypothetical inlier3. The estimated model is reasonably good if sufficiently many points have been classified as hypothetical inliers.4. The model is re-estimated from all assumed inliers5. Finally, the model is evaluated by estimating the error of the inliers relative to the model
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Registration
Phase Correlation
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Medical Imaging, SS-2010
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Registration
Distance Measures
- Sum of Squared Differences (SSD)
- Root Mean Square Difference (RMSD)
- Normalized Cross Correlation (NXCorr)
- Mutual Information (MI)
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Medical Imaging, SS-2010
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Registration
Sum of Squared Differences
SSD(f,t) SSD(20f,t)
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Medical Imaging, SS-2010
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Registration
Root Mean Squared Differences
RMS(f,t) RMS(20f,t)
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Medical Imaging, SS-2010
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Registration
Normalized Cross Correlation
NXCorr(f,t) NXCorr(20f,t)
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Medical Imaging, SS-2010
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Registration
Mutual Information
MI(f,t) MI(20f,t)
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Optical Flow
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Optical flow
Brightness consistency constraint
With Taylor expansion
V : Flow (Motion)
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Medical Imaging, SS-2010
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Optical flow
Lucas Kanade Algorithm: Assume locally constant flow
=>
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Medical Imaging, SS-2010
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Optical flow
Horn Schunck Algorithm: Assume globally smooth flow
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Optical flow
Bruhn’s Non-linear Algorithm
