a survey of medical image registration j.b.maintz,m.a viergever medical image analysis,1998
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A Survey of Medical Image Registration
J.B.Maintz,M.A ViergeverMedical Image Analysis,1998
Medical Image SPECT (Single Photon Emission
Computed Tomography) PET (Positron Emission
Tomography) MRI (Magnetic Resonance Image) CT (Computed Tomography)
Image Modalities AnatomicalDepicting primarily morphology
(MRI,CT,X-ray) FunctionalDepicting primarily information on
the metabolism of the underlying anatomy (SPECT,PET)
Medical Image Integration Registration Bring the modalities involved into
spatial alignment Fusion Integrated display of the data
involved
Matching, Integration,Correlation,…
Registration procedure Problem statement Registration paradigm Optimization procedure
Pillars and criteria are heavily interwined and have many cross-influences
Classification of Registration Methods
Dimensionality
Nature of Registration basis
Nature of transformation
Domain of transformation
InteractionOptimization procedure
Modalities involved
Subject Object
Dimensionality Spatial dimensions only
2D/2D 2D/3D 3D/3D
Time series(more than two images), with spatial dimensions
2D/2D 2D/3D 3D/3D
Spatial registration methods 3D/3D registration of two images 2D/2D registration Less complex by an order of magnitude both
where the number of parameters and the volume of the data are concerned.
2D/3D registration Direct alignment of spatial data to projective
data, or the alignment of a single tomographic slice to spatial data
Registration of time seriesTime series of images are required for various
reasons Monitoring of bone growth in children (long time
interval) Monitoring of tumor growth (medium interval) Post-operative monitoring of healing (short
interval) Observing the passing of an injected bolus through
a vessel tree (ultra-short interval)
Two images need to be compared.
Nature of registration basis Image based
Extrinsicbased on foreign objects introduced into the imaged space
Intrinsicbased on the image information as generated by the patient
Non-image based (calibrated coordinate systems)
Extrinsic registration methods Advantage
registration is easy, fast, and can be automated.
no need for complex optimization algorithms.
Disadvantage Prospective character must be made in the pre-
acquisition phase. Often invasive character of the marker objects. Non-invasive markers can be used, but less accurate.
Extrinsic registration methods Invasive
Stereotactic frameFiducials (screw markers)
Non-invasiveMould,frame,dental adapter,etcFiducials (skin markers)
Extrinsic registration methods The registration transformation is
often restricted to be rigid (translations and rotations only)
Rigid transformation constraint, and various practical considerations, use of extrinsic 3D/3D methods are limited to brain and orthopedic imaging
Intrinsic registration methods
Landmark based Segmentation based Voxel property based
Landmark based registration Anatomical
salient and accurately locatable points of the morphology of the visible anatomy, usually identified by the user
Geometricalpoints at the locus of the optimum of some geometric property,e.g.,local curvature extrema,corners,etc, generally localized in an automatic fashion.
Landmark based registration The set of registration points is sparse
---fast optimization procedures
Optimize Measures Average distance between each landmark Closest counterpart (Procrustean Metric) Iterated minimal landmark distances
Algorithm Iterative closest point (ICP) Procrustean optimum Quasi-exhaustive searches, graph matching and
dynamic programming approaches
Segmentation based registration
Rigid model based Anatomically the same structures(mostly surfaces) are extracted from both images to be registered, and used as the sole input for the alignment procedure.
Deformable model based An extracted structure (also mostly surfaces, and curves) from one image is elastically deformed to fit the second image.
Rigid model based “head-hat” method
rely on the segmentation of the skin surface from CT,MR, and PET images of the head
Chamfer matchingalignment of binary structures by means of a distance transform
Deformable model based Deformable curves
Snakes, active contours,nets(3D)
Data structureLocal functions, i.e., splines
Deformable model approachTemplate model defined in one imagetemplate is deformed to match second image
segmented structure unsegmented
Voxel property based registration
Operate directly on the image grey values
Two approaches: Immediately reduce the image grey value
content to a representative set of scalars and orientations
Use the full image content throughout the registration process
Principal axes and moments based
Image center of gravity and its principal orientations (principal axes) are computed from the image zeroth and first order moment
Align the center of gravity and the principal orientations Principal axes :Easy implementation, no
high accuracy Moment based: require pre-segmentation
Full image content based Use all of the available information
throughout the registration process.
Automatic methods presented
Paradigms reported Cross-correlation Fourier domain
based .. Minimization of
variance of grey values within segmentation
Minimization of the histogram entropy of difference images
Histogram clustering and minimization of histogram dispersion
Maximization of mutual information
Minimization of the absolute or squared intensity differences
…
Non-image based registration
Calibrated coordinate system If the imaging coordinate systems of the
two scanners involved are somehow calibrated to each other, which necessitates the scanners to be brought in to he same physical location
Registering the position of surgical tools mounted on a robot arm to images
Nature of Transformation Rigid Affine Projective Curved
Domain of transformation
GlobalApply to entire
image
LocalSubsections have
their own
Rigid case equation
Rigid or affine 3D transformation equation jiji xay
Rotation matrix
rotates the image around axis i by an angle
)(iri
Transformation Many methods require a pre-
registration (initialization) using a rigid or affine transformation
Global rigid transformation is used most frequently in registration applications
Application: Human head
Interaction Interactive Semi-automatic Automatic
Minimal interaction and speed, accuracy, or robustness
Interaction Extrinsic methods
Automated Semi-automatic
Intrinsic methods Semi-automatic
Anatomical landmark Segmentation based
Automated Geometrical landmark Voxel property based
Optimization procedure
Parameters for registration transformation
Parameters computed
Parameters searched for
Optimization techniques Powell’s method Downhill simplex method Levenberg-Marquardt optimization Simulated annealing Genetic methods Quasi-exhaustive searching
Optimization techniques
Frequent additions:Multi-resolution and multi-scale
approaches
More than one techniquesFast & coarse one followed by accurate & slow one
Modalities involved Monomodal Multimodal Modality to model Patient to modality
Subject Intrasubject Intersubject Atlas
Object Different areas of the body
Related issues
How to use the registration Registration & visualization Registration & segmentation
ValidationValidation of the registrationAccuracy,…