A Computationally Efficient Approach for 2D-3D Image Registration
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Transcript of A Computationally Efficient Approach for 2D-3D Image Registration
A Computationally Efficient Approach for 2D-3D Image
Registration
Juri Minxha Medical Image AnalysisProfessor Benjamin Kimia
Spring 2011
Brown University
Review of Registration
Similarity Metric Optimization
1. Similarity Metric Mutual Information, Cross-Correlation, Correlation Ratio,
Cross Correlation Residual Entropy
2. Optimization, Non-gradient vs. GradientGauss-Newton, steepest descent, Levenberg-Larquardt, simplex method etc.
The main challenge is:Minimize computation time
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Review of the SoCV similarity metric
1. Similarity Measure: Sum of Conditional Variances
2. Optimization Algorithm: Gauss-Newton
1. Requires computation of gradient2. Fast convergence
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Similarity Metric: SoCV
I0 Ro
R0 =100·ln(256-I0)-300
1. Quantize images to 64 possible values2. Each pixel in the image on the left corresponds to
a bin in the histogram (64 x 64 bins)3. Notice the non-linear relationship between
I and R
Similarity Metric: SoCV
What happens if I0 is translated to the right?
For each value of R, we have a range of values in I’
Similarity Metric: SoCV
Compute the conditional expectation/mean of this distribution
Replace each value in R with the conditional mean
Similarity Metric: SoCV
Optimization: Gauss-NewtonGoal: Find values of 3D rigid-body transform that minimize S
Registration with CT, Fluoroscopy
I) Last time: registration of MRI-MRI
2) This time: registration of CT and fluoroscopy
1. CT volume (512x512x369) of alligator bone
2. A fluroroscopy video (30 seconds, 30 FPS)
3D-visualization of CT data (Slicer)
The big bone must be removed before projection!
Fluoroscopy video
1. ~30 seconds at 30 frames per second
2. The location of the bone is unknown throughout the video
3. Bone is being translated and Rotated
4. All artifacts must be removed before registration
Segmentation of smaller bone from CT
Individual frames Stacking the layers
Thresholding to remove background and onlyTaking the frames that correspond to top bone
Ray-Casting: Generating the DRR
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Projecting the volume data onto the Three main axes.
Preparing the fluoroscopy image
Measure of Fit
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Optimization over three parameters: Rotation in x Rotation in y Rotation in z
Manually adjusted three parameters: Translation in xTranslation in yTranslation in z
Shortcomings1. There is no ground truth2. Optimization was over a subset of the 6 parameters
required for rigid body motion3. The initial conditions are extremely important
1. The initial position has to be very close the final position
2. I manually translated the image, a procedure that should be automated
3. Speed optimization 4. Computational needs are excessive
References• A computationally efficient approach for 2D-3D image registration
Haque, M.N.; Pickering, M.R.; Biswas, M.; Frater, M.R.; Scarvell, J.M.; Smith, P.N.; 2010 Annual International Conference of the IEEEEngineering in Medicine and Biology Society (EMBC), Issue Date: Aug. 31 2010-Sept. 4 2010, On page(s): 6268 – 6271
• M. Pickering, A. Muhit, J. Scarvell, and P. Smith, "A new multimodalsimilarity measure for fast gradient-based 2D-3D imageregistration," in Proc. IEEE Int. Conf. on Engineering in Medicineand Biology (EMBC), Minneapolis, USA, 2009, pp. 5821-5824.
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