Registration Foundations

National Alliance for Medical Image Computing http://na-mic.org

Registration Foundations

• Bring multiple image data sets into anatomical agreement


The Registration Problem

Tinit

Tk

Tfinal

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Provided by Lilla Zollei


• multi-modality fusion (same patient?)• time-series processing

– e.g.: MS, fMRI experiments, cardiac ultrasound

• warping across patients to atlas for labeling• accommodate tissue deformations in image-

guided surgery• image-guided surgery of organs other than head

Applications


Manual Registration

• Not too bad with a few data sets

• Re-Position one data set for visual agreement


Medical image data sets

Transform (move around)

Compare with objective function

Optimization algorithminitial value

motion parameters

score

Automated Medical Image Registration

Provided by Lilla Zollei


Estimate Relationship Among two Signals

• U : a signal

• V : another signal, transformed by



• If p(U,V) is Gaussian– Then best f is correlation (or

squared difference)



• If p(U,V) is UNKNOWN– Look for strongest statistical

relationship among the signals

I : Mutual Information


Mutual Information (MI)

• H : entropy– measures information content

• I : Mutual Information - a statistic that measures lack of statistical independence


MI Registration

• Default Method for Multi-Modal Medical Image Registration

• Viola Wells et al. circa 96– Collignon, and Hill & Hawkes

• Pluim et al. Survey, 2003: More than 160 published applications


Example MRT Rigid Registration

Pre-operative SPGR MRI Intra-operative T2-weighted MRI

Provided by D. Gering


Before Registration After Registration

Provided by D. Gering

Example MRT Rigid Registration


Real 3D CT data


3D MR data


“Real” CT-MR registration:3D starting position


CT-MR registration final result


•The end.


3D Slicer Design

• Cross-platform• Built on VTK

– Open source platform for visualization– GE, industrial strength– C++, Tck/TK GUI

• Open GL – Library interface to graphics hardware

• Easily extended• Open source• Available free: www.slicer.org


Estimate intensity correctionusing residuals based on current posteriors.

Compute tissue posteriors using current intensity correction.

M-Step

E-Step

EM-Segmentation

Provided by T Kapur


EM Segmentation…

PD, T2 Data

Seg Resultw/o EM

Seg ResultWith EM


EM Segmentation: MS Example

Data provided by Charles Guttmann

PD T2


EM Segmentation: MS Example

Seg w/o EM Seg with EM

Registration Foundations

Documents

Transcript of Registration Foundations