Non-Rigid Registration on SPIE 2002 First Part: TPS.

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Presentation transcript:

Non-Rigid Registration on SPIE 2002 First Part: TPS

List of related papers 

Warping Registration Three-dimensional warping registration of the pelvis and prostate Baowei Fei, Corey Kemper,David L Wilson Case Western Reserve University University Hospitals of Cleveland

Warping Registration  Automatic rigid body registration to get global transformation  Local warping registration Object: Pelvis MR image

Warping Registration  Similarity measurements Mutual Information Correlation Coefficient

Warping Registration  Rigid Body Registration with Special Features Multi-resolution approach Low Resolution:CC Simplex Full Resolution:MI Restart: local maximum

Warping Registration  Control Point selection Manual  Control Point optimization Volume of Interest Simplex VOI  TPS warping 3-D TPS transformation

Warping Registration  Experiments Control Point Selection: 180  Comparison Warping & Rigid registration  Condition of robust CC & MI restarting mechanism

Mammogram registration Nonrigid mammogram registration using mutual information Michael A.Wirth, Jay Narhan, Derek Gray University of Guelph

Mammogram registration  Object: Mammogram Images  Comparative Analysis--- mammograms are inspected for characteristic signs of potential cancer through the comparison of various analogous bilateral or temporal mammograms

Mammogram registration  Mammogram from MIAS digital mammogram database.  Website

Mammogram registraion  Deformable behavior of breast acquisition process: 3D positioning of the breast, amount and geometry of compression applied to it. Breast is deformable, mobile, soft-tissue structure

Mammogram Registration  Mutual Information  Thin-Plate Splines n Control Points

Mammogram registration  Hierarchical registration algorithm Global Registration Localized (rigid) sub-image registration Nonrigid registration TPS Registered mammograms Displacement vector field

Motion model Based Shape-adapted motion model based on thin- plate splines and point clustering for point ser registration Julian Mattes, Hohannes Fieres, R.Eils German Cancer Research Center

Motion Model Based  Registration Cost functional Optimization strategy The specification of deformation---- Motion Model

Motion model based  Landmarks are placed to adapt to the shape  DOF  Best-fitting deformation with minimal DOF

Motion model based  Distance Cluster Method  Octree Method  Annealing Method