Image Medical Registration: A Survey
Aiming Lu
Outline
Introduction Transformation Algorithms Visualization Validation Conclusion
Introduction
Image registration
matching two images so that corresponding coordinate points in the two images correspond to the same physical region of the scene being imaged also referred to as image fusion, superimposition, matching or merge
MR
SPECT
registered
Applications
Diagnosis
Combining information from multiple imaging modalities
Studying disease progression
Monitoring changes in size, shape, position or image intensity over time
Image guided surgery or radiotherapy
Relating pre-operative images and surgical plans to the physical reality of the patient
Patient comparison or atlas construction
Relating one individuals anatomy to a standardized atlas
Classification
Dimensionality
2D-2D, 3D-3D, 2D-3D
Subject:
Intrasubject Intersubject Atlas
Nature of registration basis
Image based
Extrinsic, Intrinsic
Non-image based
Nature of the transformation
Rigid, Affine, Projective, Curved
Domain of transformation Local, global Optimization procedure Object
Interaction
Interactive, Semi-automatic, Automatic
Modalities involved
Monomodal, Multimodal, Modality to model
Transformation
Relates the position of features in two images
Rigid
translations and rotations
Affine
Also allows scaling and shearing
curved
Allows the mapping of straight lines to curves
perspective
The parallelism of lines need not be preserved
Registration algorithms
Method used to find the transformation Rigid & affine
Landmark based Edge based Voxel intensity based Information theory based
Non-rigid
Registration using basis functions Registration using splines Physics based
Elastic, Fluid, Optical flow, etc.
Landmark based
Identifying corresponding points in the images and inferring the image transformation Types of landmarks
Extrinsic
artificial objects attached to the patient
Intrinsic
internal anatomical structures
Computing the average or centroid of each set of points translation Rotated this point set about the new centroid until the sum of the squared distances between each corresponding point pair is minimized
Surfaced based
Method
Extracting corresponding surfaces Computing the transformation by minimizing some measure of distance between the two surfaces
Algorithms used
The Head and Hat Algorithm The Iterative Closest Point Algorithm Registration using crest lines
Voxel intensity based
Method
Calculating the registration transformation by optimizing some measure calculated directly from the voxel values in the images
Algorithms used
Registration by minimizing intensity difference Correlation techniques Ratio image uniformity Partitioned Intensity Uniformity
Information theory based
Image registration is considered as to maximize the amount of shared information in two images
reducing the amount of information in the combined image
Algorithms used
Joint entropy
Joint entropy measures the amount of information in the two images combined
Mutual information
measure A of how well one image explains the other, and is maximized at the optimal alignment
Normalized Mutual Information
Registration using basis functions
Represent the deformation field using a set of basis functions
Fourier (trigonometric) basis functions or wavelet basis functions. Implement smoothness constraint by linear combination of basis functions The trigonometric basis functions corresponds to a spectral representation of the deformation field where each basis function describes a particular frequency of the deformation.
Registration using splines
Assumption
a set of corresponding points or landmarks (control points) can be identified
At control points, interpolate or approximate the displacements to map the location of the control points in both images Between control points, they provide a smoothly varying displacement field
Elastic registration
Model the deformation as a physical process resembling the stretching of an elastic material
The physical process is governed by the internal force & external force described by the Navier linear elastic partial differential equation
The external force drives the registration process
The external force can be the gradient of a similarity measure
e.g. local correlation measure based on intensities, intensity differences or intensity features such as edge and curvature
Or the distance between the curves and surfaces of corresponding anatomical structures.
Other physics based registration
Fluid registration
The image was modeled as a highly viscous fluid
Registration using mechanical models
using a three-component model to simulate the properties of rigid, elastic and fluid structures.
Registration using optical flow
Optimization
Many registration algorithms require an iterative approach
an initial estimate of the transformation is gradually ...