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Non-Rigid Registration

Non-Rigid Registration

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Non-Rigid Registration. Why Non-Rigid Registration. In many applications a rigid transformation is sufficient. (Brain) Other applications: Intra-subject: tissue deformation Inter-subject: anatomical variability across individuals Fast-Moving area: Non-rigid. - PowerPoint PPT Presentation

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Page 1: Non-Rigid Registration

Non-Rigid Registration

Page 2: Non-Rigid Registration

Why Non-Rigid Registration

In many applications a rigid transformation is sufficient. (Brain)

Other applications:

Intra-subject: tissue deformation

Inter-subject: anatomical variability across individuals

Fast-Moving area: Non-rigid

Page 3: Non-Rigid Registration

Registration Framework

In terms of L.Brown.(1992)– Feature Space– Transformation– Similarity Measure– Search Strategy (Optimization)

Rigid vs. Non-rigid in the framework

Page 4: Non-Rigid Registration

Feature Space

Geometric landmarks:

Points

Edges

Contours

Surfaces, etc.Intensities:

Raw pixel values23 35

24 56

Page 5: Non-Rigid Registration

Transformation

Page 6: Non-Rigid Registration

Transformation

Rigid transformation:

3DOF (2D)

6 DOF (3D)Affine transformation:

12 DOF

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Page 7: Non-Rigid Registration

Transformation

Additional DOF.Second order polynomial-30 DOF

Higher order:

third-60, fourth-105,fifth-168Model only global shape changes

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Page 8: Non-Rigid Registration

Transformation

For each pixel (voxel), one 2d(3d) vector to describe local deformation.

Parameters of non-rigid >> that of rigid

Page 9: Non-Rigid Registration

Similarity Measure

Point based

---The distance between features, such as points,curves,or surfaces of corresponding anatomical structure.

--- Feature extraction.Voxel based

---Absolute Difference, Sum of squared differences, Cross correlation, or Mutual information

Page 10: Non-Rigid Registration

Search Strategy

Registration can be formulated as an optimization problem whose goal is to minimize an associated energy or cost function.

General form of cost function: C = -Csimilarity+Cdeformation

Page 11: Non-Rigid Registration

Search Strategy

Powell’s direction set methodDownhill simplex methodDynamic programmingRelaxation matching

Combined withMulti-resolution techniques

Page 12: Non-Rigid Registration

Registration Scheme

Page 13: Non-Rigid Registration

Non-rigid Registration

Feature-based– Control Points: TPS– Curve/Edge/Contour– Surface

Intensity-based– Elastic model– Viscous fluid model– Others

Page 14: Non-Rigid Registration

Thin-plate splines (TPS)

Come from Physics: TPS has the property of minimizing the bending energy.

Page 15: Non-Rigid Registration

TPS (cont.)

Splines based on radial basis functions

Surface interpolation of scattered data

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Page 16: Non-Rigid Registration

Description of the Approach

1. Select the control points in the images.

2. Calculate the coefficients for the TPS.

3. Apply the TPS transformation on the whole image.

Page 17: Non-Rigid Registration

Synthetic Images

T1 T2

Page 18: Non-Rigid Registration

TPS-Results(1)

Page 19: Non-Rigid Registration

TPS-Results(2)

Page 20: Non-Rigid Registration

Rigid and non-rigid registration

Rigid Registration as pre-processing (global alignment)

Non-rigid registration for local alignment

Page 21: Non-Rigid Registration

Next time

Affine-mapping technique