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Published byClarence Hall Modified over 9 years ago
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Jan Kamenický Mariánská 2008
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We deal with medical images ◦ Different viewpoints - multiview ◦ Different times - multitemporal ◦ Different sensors – multimodal Area-based methods (no features) Transformation model Cost function minimization 3
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Transformation model ◦ Displacement field u(x) 4
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Transformation model ◦ Displacement field u(x) Cost function ◦ Similarity measure (external forces) ◦ Smoothing (penalization) term (internal forces) ◦ Additional constraints (landmarks, volume preservation) 5
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Transformation model ◦ Displacement field u(x) Cost function ◦ Similarity measure (external forces) ◦ Smoothing (penalization) term (internal forces) ◦ Additional constraints (landmarks, volume preservation) Minimization 6
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Main problems ◦ Computationally intensive ◦ Sensitive to initial positioning 7 reference image sensed imagepyramid sampler interpolator optimizer transform pyramid metric multi-resolution resolution level
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Translation Rigid (Euler) ◦ Translation, rotation Similarity ◦ Translation, rotation, scaling Affine B-splines ◦ Control points - regular grid on reference image 8
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Sum of Squared Differences Normalized Correlation Coefficients Mutual Information Normalized Gradient Field 10
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Sum of Squared Differences (SSD) ◦ Equal intensity distribution (same modality) Normalized Correlation Coefficients Mutual Information Normalized Gradient Field 11
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Sum of Squared Differences Normalized Correlation Coefficients (NCC) ◦ Linear relation between intensity values (but still same modality) Mutual Information Normalized Gradient Field 12
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Sum of Squared Differences Normalized Correlation Coefficients Mutual Information ◦ Any statistical dependence Normalized Gradient Field 13
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Mutual Information (MI) ◦ From entropy 14
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Mutual Information (MI) ◦ From Kullback-Leibler distance 15
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Mutual Information (MI) ◦ For images p(x) … normalized image histogram ◦ Normalized Mutual Information (NMI) 16
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Mutual Information (MI) ◦ Joint probability estimation Using B-spline Parzen windows and are defined by the histogram bins widths 17
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Sum of Squared Differences Normalized Correlation Coefficients Mutual Information Normalized Gradient Field (NGF) ◦ Based on edges 18
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Elastic ◦ Elastic potential (motivated by material properties) Fluid ◦ Viscous fluid model (based on Navier-Stokes) Diffusion ◦ Much faster 19
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Curvature ◦ Doesn’t penalize affine transformation Bending energy (Thin plate splines) 20
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21 curvaturediffusion elasticfluid
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Landmarks (fiducial markers) ◦ “Hard” constraint ◦ “Soft” constraint Volume preservation 22
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Full Grid ◦ Used with multi-resolution Random ◦ Random subset of voxels is selected ◦ Improved speed 23
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Nearest Neighbour (NN) Linear ◦ Usually sufficient during optimization N -th order B-spline ◦ Useful for the final image (usually 3 rd order) 24
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Gradient Descent (GD) ◦ Linear rate of convergence Quasi-Newton Nonlinear Conjugate Gradient Stochastic Gradient Descent Evolution Strategy 26
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Gradient Descent Quasi-Newton (QN) ◦ Can be superlinearly convergent Nonlinear Conjugate Gradient Stochastic Gradient Descent Evolution Strategy 27
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Gradient Descent Quasi-Newton Nonlinear Conjugate Gradient (NCG) ◦ Superlinear rate of convergence can be achieved Stochastic Gradient Descent Evolution Strategy 28
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Gradient Descent Quasi-Newton Nonlinear Conjugate Gradient Stochastic Gradient Descent (SGD) ◦ Similar to GD, but uses approximation of the gradient (Kiefer-Wolfowitz, Simultaneous Perturbation, Robbins-Monro) Evolution Strategy 29
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Gradient Descent Quasi-Newton Nonlinear Conjugate Gradient Stochastic Gradient Descent Evolution Strategy (ES) ◦ Covariance matrix adaptation ◦ Tries several possible directions (randomly according to the covariance matrix of the cost function), the best are chosen and their weighted average is used 30
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Data complexity ◦ Gaussian pyramid ◦ Laplacian pyramid ◦ Wavelet pyramid Transformation complexity ◦ Transformation superposition ◦ Different B-spline grid density 31
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Registration toolkit based on ITK Handles many methods ◦ Similarity measures (SSD, NCC, MI, NMI) ◦ Transformations (rigid, affine, B-splines) ◦ Optimizers (GD, SGD-RM) ◦ Samplers, Interpolators, Multi-resolution, … http://elastix.isi.uu.nl 32
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