Model-based Image Registration

R. S. Schestowitz, C. J. Twining,

T. F. Cootes and C. J. Taylor

Overview

Non-rigid registration (NRR)

Registration and models

model-based similarity measure

automatic construction of appearance models

Experiments

synthetic data

Results

Conclusions

Non-Rigid Registration

Overlapping analogous structures

Transforming (warping) images

Evaluation using similarity measure (objective function)

Issues with NRR

Results are arbitrary (not unique)

many different warps give equally good solutions

result depends on method of optimisation

Registration of sets of images

taking systematic account of all the images

choosing a reference

need for a principled approach

Building an Appearance Model

For each of a training set of examples

Building an Appearance Model

Principal component analysis

shape model:

texture model:

Columns of Pr form shape and texture bases

Parameters br control modes of variation

Appearance Model of Brain Slice

Registration and Models

Relationship between NRR and model building

NRR gives dense correspondence

dense correspondence  combined appearance model

Model-based similarity measure

set of warps that lead to best model

best model defined by Minimum Description Length

MDL approach for shapes

extended to combined models

Model-based Objective Function

Description length of image set using model

Approximated by determinant of model covariance

product of non-zero Eigen-values

volume of distribution in model space

Experiments - Data

One dimensional synthetic data

Bumps that vary in

position

width

height

Correct solution is known

Validation wrt correct solution

Experiments - Optimisation

Localised random warps

clamped-plate splines

random width and position

Optimise one image at a time

Objective function optimised wrt warp magnitude

Results of Registration

Before registration

After registration Objective function

Result approaches known correct solution

Resulting Models

Combined model captures variability

Three modes of variation

Before registration At correspondence After registration

A Subset Approach

Stochastically chosen subsets

more robust optimisation

solution reached more rapidly (time  N3)

Conclusions

Feasibility of model-based registration

Provides unique solution

Principled approach to registering sets of images

Automatic building of combined appearance models

Enhancements to efficiency of optimisation

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2004-06-01T09:06:51

ISBE