Registration Algorithms for MR-Mammography
This page contains references and short descriptions
of algorithms for the intra-visit
registration of contrast-enhanced MR breast images.
Please
email me
about any algorithm I've missed. No comments are given for
references marked with * since I couldn't get hold of them.
M. H. Davis,
A. Khotanzad,
(Southern Methodist Univeristy, Dallas, USA)
D. P. Flamig (Los Alamos National Laboratories, Los Alamos, USA),
S. E. Harms (University of Arkansas for Medical Sciences, Little Rock, USA)
- Davis95 [1]*
- Davis97 [2]
AIM: to find realistic transformation for given correspondance of
anatomic landmarks
METHODS: elastic body spline, physical model of homogenous, isotropic
3D elastic body,
actual control point pairing by block matching method based on regional
normalized correlation,
TESTS:
tested on MRI of breast without contrast agent, simulated
deformations (2nd order Taylor series warp) and
random selected landmarks,
results assessed based on image similarity (mean squared
error, mean absolute error, correlation coefficient),
comparison of elastic body spline to thin plate spline and volume
spline, test of influence of number of control-points and type of
initial transformation (none, rigid, affine)
M. S. NessAiver,
T. Krebs, J. Wong, B. S. Fernandes, M. Steverson
(University of Maryland at Baltimore)
- NessAiver95 [3]*
- NessAiver96 [4]
METHODS: individual rigid transformation per 2D slice,
minimize standard deviation of
subtracted images
TESTS: standard deviation of subtracted images,
visual quality of subtracted images
R. Kumar,
J. C. Asmuth, K. Hanna, J. Bergen (David Sarnoff Research Center,
Princton, USA)
C. Hulka, D. B. Kopans, R. Weisskoff, R. Moore (Massachusetts General Hospital,
Boston, USA)
- Kumar95 [5]*
- Kumar96 [6]
METHODS: global 3D affine followed by one local flow iteration,
minize sum of squares of differences, coarse to fine strategy
with 3D pyramid
TESTS: visual inspection of subtracted images and in cine mode
C. S. Zuo, B. L. Buff, T. G. Mahon, T. Z. Wong
(Deaconess Hosptial, Boston, USA),
A. Jiang (Massachusetts General Hosptial and Harvard Medical School,
Boston, USA)
- Zuo96 [7]
METHODS: rigid transformation for rectangular subvolume enclosing the breast,
minimize variance of ratio images of the two image volumes,
TESTS: variance across breast boundary for subtracted images, visually better
subtracted images, SNR within lesion improves, registration of
simulated image pair in correspondance (simulated lesion without
motion) produced rotations angles of
less than 0.05 and translations less than 0.02mm
B. A. Porter, J. P. Smith, (First Hill Diagnostic Imaging, Seattle, USA),
P. Margosian (Picker International Cleveland, USA),
J. V. Hanjal, (Hammersmith Hospital, London, UK)
- Porter96 [8]
METHODS: translation by hand to one voxel accuracy
TESTS: visual inspection
P. Undrill, L. Vieira, T. W. Redpath, F. J. Gilbert
(University of Aberdeen, Aberdeen, UK)
- Undrill96 [9]
METHODS:
a) constrained principle axis determination:
determine translation + rotation + scaling from principle components
of general moments of 3D object;
b) correlation in image domain: translation + rotation + scaling,
minimize global variance between corresponding voxels,
c) polynomial warping determined through corresponding (identified)
control points
TESTS: visual inspection of two cases
- Vieira99 [10]
METHODS:
a) automatic image registration (AIR):
minimise variance of ratio, warping polynomials of 2nd to 5th order,
b) spatial parametric mapping (SPM): sum of squares of differences,
linear combination of basis function
(3D discrete cosine transformations)
TESTS:
simulated deformations like a shear parallel to rib cage, no contrast,
qualitative analysis: only intensity changes assessed not target
registration error
S. Huwer, J. Rahmel, A. v. Wagenheim
(University of Kaiserslautern,
Kaiserslautern, Germany)
- Huwer96 [11]
METHODS: modified Kohonen network model, local non-affine
transformation, intensity difference, gradient, relevant set of pixel
TESTS: visual inspection of one case
M. Davey, I. D. Wilkinson, F. Balen, H. Mumtaz, M. Paley,
D. L. Plummer, M. A. Hall-Craggs,
W. R. Lees, A. Linney (University College London; The Middlesex Hospital,
London, UK)
- Davey97 [12]
METHODS: rigid transformation per breast, root mean square error
mininimization
TESTS: visually, quality of subtracted images
P. M. Hayton, M. Brady, L. Tarassenko, S. M. Smith, N. Moore (
Oxford University, The John
Radcliffe Hospital, Oxford, UK)
- Hayton97 [13],
METHODS: minimize residual error when fitting pharma-cokinetic curve,
optic flow, 2D
TESTS: residual error, registration error from inducing
global translation,
visual inspection of image
- Hayton99 [14]
METHODS: local mutual information,
Bayes probabilities, optic flow field, B-spline
fit, smoothing regularizator, multi-scale, correct for bias field
TESTS: results from 3 patients, visual inspection, consistency of forward and
backward transformation after induced 20deg rotation of a none aligned
image pair
S. Rueckert,
C. Tanner,
J. A. Schnabel,
L. I. Sonoda, A. D. Castellano-Smith, D. L. Hill,
D. J. Hawkes (King's College, London, UK)
E. R. E. Denton, S. C. Rankin (Guy's Hospital, London, UK)
A. Degenhard, C. Hayes, M. O. Leach,
(Institute of Cancer Research and the Royal Marsden NHS
Trust, Sutton, UK)
D. R. Hose (University of Sheffield, Sheffield, UK)
- Rueckert98-99 [15]-[17],
METHODS: rigid or affine registration followed by a free-form
deformation (FFD), FFD is based on a regularly spaced grid of control
points, multi-resolution approach, normalized mutual information as
similarity measure
TESTS: comparision to other similarity measures,
quality of subtracted images
- Denton99 [18]
AIM: evaluation of registration performance
METHODS: ranking of quality of subtracted images before and after
rigid, affine and non-rigid registration by blinded radiologists
- Tanner00 [19]
METHODS: local volume preservation of registration by coupling of
control points
TESTS: analysis of volume change for 15 enhancing lesions
- Schnabel01,03 [20,21]
METHODS: validation method of non-rigid registration by applying
physically plausible breast deformations generated by a finite element
model to image pairs where no motion was visible
TESTS: demonstration of method
- Tanner01 [22]
AIM: comparison of biomechanical models base on finite element methods
to justify plausibility of simulated breast deformations
METHODS: MR images before and after deformation of a volunteers
breast, optimal surface displacement values from 3D non-rigid
registration, obtain solution of finite element model
TESTS: error of predicting displacement of internal breast
structures on 12 landmarks
- Schnabel02 [23],Tanner02 [24]
AIM: validation of non-rigid registration for MR-mammography
METHODS: based on simulated breast deformations using finite element
models (Schnabel03 [21]) for a multi-level
registration approach (Schnabel02 [25]) and a
volume-preserving multi-resolution registration approach
C. Meyer, J. Boes, B. Kim, P. Bland (University of Michigan,
Ann Arbor, USA)
- Meyer98 [26]
AIM: want to find minimal set of of control points
METHODS: initial estimate from manually selected corresponding control
points, move these around randomly (up to 3mm)
to maximize global mutual information
TESTS: compare curvature of cost function and variance of final
positions of control points, visual inspection of two patients
H. Fischer, M. Otte, C. Ehritt-Braun,
M. Büchert, P. Peschl,
J. Hennig (University of Freiburg)
- Fischer98 [27]
METHODS: subdivided into equally sized subvolumes, connect
local transformations by trilinear Berzier splines, optimized using
linear correlation or linear entropy coefficient
TESTS: visual inspection
- Fischer00 [28]
T. Brückner, M. V. Knopp
(German Cancer Research Center, Heidelberg,
Germany)
R. Lucht, G. Brix (Federal Office
for Radiation Protection, Neuherberg, Germany)
- Lucht98 [29]
METHODS: automatic feature extraction(edges from median filter and
opening), find corresponding features (translations, feature overlap,
bidding system),
bi-linear interpolation
TESTS: feature overlap
- Lucht00 [30]
METHODS: pre-alignment for any data with a shift greater than 1 cm,
feature extraction (edges from median filter and opening),
find corresponding features, smooth transformation by Gaussian
filtering, 2D only
TESTS: assessed on correlation coefficient,
visual inspection of animation,
tracing anatomical landmarks in continous film sequences (20 patients)
- Brückner00 [31]
AIM: evaluation of performance of rigid and elastic matching
algorithms; evaluation of mutual information as matching criterion
METHODS: a) 2D rigid registration,
maximise mutual information;
b) elastic matching as described in Lucht00 [30]
TESTS: assessed within breast region using mutual information,
visual inspection in
cine mode by a blinded reader for 10 patients
S. Krishnan, T. L. Chenevert, M. A. Helvie, F. L. Londy
(University of Michigan Hospitals,
Ann Arbor, USA)
- Krishnan99 [32]
AIM: to correct the phase shifts due to patient motion in k-space data prior to
offline keyhole reconstruction
METHODS: in-vivo, registrations accounting for translations
TESTS: on phantom with induced translations and Gd-DTPA,
64 patients, quantitatively on subtraction edge artifacts
M. A. Wirth
(RMIT University, Melbourne, Australia)
- Wirth99 [33]
METHODS: 2D registration, a) affine transformation, b) radial basis
functions (RBF)
incorporating thin plate splines, c) multiquadric RBF;
control points automatic from breast contour and manually from inner region
TESTS: three 2D slices, 96 control points,
correlation coefficient, sum of squares differences, difference images
J. R. Reichenbach, J. Hopfe,
W. A. Kaiser
(Friedrich-Schiller-University, Jena, Germany)
M. E. Bellemann (University of Applied Sciences, Jena, Germany)
R. Lucht (Federal Office
for Radiation Protection, Neuherberg, Germany)
- Reichenbach00 [34]
METHODS:image split into single breast image, mutual information as
image similarity measure, global translation, individual 2D rotation for
each slice,
TESTS: three volunteers, mutual information plots, visual inspection
- Reichenbach02 [35]
METHODS: image automatically split into single breast image,
mutual information as similarity measure,
rotation parameter for per-selected equidistant slices
TESTS: on translated and rotated phantom, volunteers repositioned,
no contrast agent, performance assessed using mutual information
which was also used as optimization criterion
T. Rohlfing,
C. R. Maurer (Stanford University Medical Centre, STanford, USA)
- Rohlfing01 [36]
METHODS: volume preserving non-rigid registration, global rigid
registration and free-form deformation with regularly spaced
control-points, cost-function consists of two optimazation criteria:
normalized mutual information and average absolute logarithm of the
local volume change, multi-level B-splines for non-uniform local
refinement of control-point spacing
TESTS: volume change, visual inspection of one case
B. Fischer,
J. Modersitzki
(University of Lübeck, Lübeck, Germany)
- Fischer02 [37,38]
METHODS: curvature type regularization term, sum of squares
differences
TESTS: illustrated on 2D MR-mammograms, reduction of sum of squares
differences, visual inspection
- 1
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M. H. Davis, A. Khotanzad, D. P. Flamig, and S. E. Harms, ``Coordinate
Transformation in 3D Image Matching by a Physical Based Method-Elastic Body
Spline,'' in Int. Symposium on Computer Vision, Coral Gables,
Florida, p. 218, 1995.
- 2
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M. H. Davis, A. Khotanzad, D. P. Flemig, and S. E. Harms, ``A Physics-Based
Coordinate Transformation for 3-D Image Matching,'' IEEE Transactions
on Medical Imaging, vol. 16, no. 3, pp. 317-328, 1997.
- 3
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M. S. NessAiver, B. S. Fernandes, T. Krebs, J. Wong, and M. Steverson, ``Three
Dimensional Subtraction of Pre- and Post-contrast Breast Image with
Translational and Rotational Registration,'' in Proceedings of the
Third Scientific Meeting of the International Society for MR in Medicine,
Nice, France, p. 438, 1995.
- 4
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M. S. NessAiver, T. Krebs, and J. Wong, ``Improved Registration for Subtration
of Pre- and Post-Contrast 3D Breast Images,'' in Proceedings of the
Fourth Scientific Meeting of the International Society for MR in Medicine,
New York, USA, p. 38, 1996.
- 5
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R. Kumar, K. Hanna, J. C. Asmuth, J. Bergen, C. Hulka, D. B. Kopans,
R. Weisskoff, and R. Moore, ``Detecting Lesions in Magnetic Resonance Breast
Scans,'' in AIPR Workshop on Tools and Techniques for Modeling and
Simulation, Washington DC, USA, Vol. 2645, pp. 181-190, 1995.
- 6
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R. Kumar, J. C. Asmuth, K. Hanna, J. Bergen, C. Hulka, D. B. Kopans,
R. Weisskoff, and R. Moore, ``Application of 3D Registration for Detecting
Lesions in Magnetic Resonance Breast Scans,'' in Proceedings SPIE
Medical Imaging 1996, Image Processing, San Diego, CA, pp. 646-656, 1996.
- 7
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C. S. Zuo, A. Jiang, B. L. Buff, T. G. Mahon, and T. Z. Wong, ``Automatic
Motion Correction for Breast MR Imaging,'' Radiology, vol. 198, no. 3,
pp. 903-906, 1996.
- 8
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B. A. Porter, J. P. Smith, P. Margosian, and J. V. Hanjal, ``Image
Re-registration for MR Subtraction Techniques,'' in Proceedings of the
Fourth Scientific Meeting of the International Society for MR in Medicine,
New York, USA, p. 760, 1996.
- 9
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P. E. Undrill, T. W. Redpath, and F. J. Gilbert, ``Reduction of Movement
Artefacts in Comparative 3D Magnetic Resonance (MR) Breast Imagaging,'' in
Proceedings SPIE Medical Imaging 1996, Image Processing, San Diego, CA,
pp. 922-930, 1996.
- 10
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L. Vieira and P. Undrill, ``Use of a Breast Model to Test the Performance of
Image Registration Methods,'' in Medical Image Understanding and
Analysis, Oxford, UK, pp. 181-184, 1999.
- 11
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S. Huwer, J. Rahmel, and A. v. Wagenheim, ``Data-Driven Registration for Local
Deformations,'' Pattern Recognition Letters, vol. 17, pp. 951-957,
1996.
- 12
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M. Davey, I. D. Wilkinson, F. Balen, H. Mumtaz, M. Paley, D. L. Plummer, M. A.
Hall-Craggs, W. R. Lees, and A. Linney, ``Detecting Breast Cancer:
Registration of Pre- and Post-contrast T1-weighted 3D Data Sets,'' in
Proceedings of the Fifth Scientific Meeting of the International Society for
MR in Medicine, Vancouver, Canada, p. 1048, 1997.
- 13
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P. M. Hayton, M. Brady, L. Tarassenko, and N. Moore, ``Analysis of Dynamic
MR Breast Images using a Model of Contrast Enhancement,''
Medical Image Analysis, vol. 1, pp. 207-224, September 1997.
- 14
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P. M. Hayton, M. Brady, S. M. Smith, and N. Moore, ``A non-rigid Registration
Algorithm for Dynamic Breast MRI Images,'' Artificial Intelligence,
vol. 114, pp. 125-156, 1999.
- 15
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D. Rueckert, C. Hayes, C. Studholme, P. Summers, M. Leach, and D. J. Hawkes,
``Non-rigid Registration of Breast MR Images using Mutual
Information,'' in Medical Image Computing and Computer-Assisted
Intervention, Cambridge, USA, pp. 1144-1152, 1998.
- 16
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D. Rueckert, L. I. Sonoda, E. Denton, S. Rankin, C. Hayes, M. O. Leach, D. L.
Hill, and D. J. Hawkes, ``Comparison and Evalution of Rigid and
Non-rigid Registration of Breast MR Images,'' in Proceedings
SPIE Medical Imaging 1999, Image Processing, San Diego, CA, 1999.
- 17
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D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach, and D. J. Hawkes,
``Non-rigid Registration using Free-Form Deformations: Application to Breast
MR Images,'' IEEE Transactions on Medical Imaging, vol. 18(8),
pp. 712-721, 1999.
- 18
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E. R. E. Denton, L. I. Sonoda, D. Rueckert, S. C. Rankin, C. Hayes, M. O.
Leach, and D. J. Hawkes, ``Comparison and Evaluation of Rigid, Affine,
and Nonrigid Registration of Breast MR Images,'' Journal of
Computer Assisted Tomography, vol. 23(5), pp. 800-805, 1999.
- 19
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C. Tanner, J. A. Schnabel, D. Chung, M. J. Clarkson, D. Rueckert, D. L. G.
Hill, and D. J. Hawkes, ``Volume and Shape Preservation of Enhancing Lesions
when Applying Non-rigid Registration to a Time Series of Contrast Enhanced MR
Breast Images,'' in Medical Image Computing and Computer-Assisted
Intervention, Pittsburgh, USA, Vol. 1935 of Lecture Notes in Computer
Science, Springer Verlag, pp. 327-337, 2000.
- 20
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J. A. Schnabel, D. Rueckert, M. Quist, J. M. Blackall, A. Castellano-Smith,
T. Hartkens, G. P. Penney, W. A. Hall, C. L. Truwit, F. A. Gerritsen,
D. L. G. Hill, and D. J. Hawkes, ``A Generic Framework for Non-Rigid
Registration based on Non-Uniform Multi-Level Free-Form Deformations,'' in
Medical Image Computing and Computer-Assisted Intervention, Utrecht,
Netherlands, Vol. 2208 of Lecture Notes in Computer Science, Springer
Verlag, pp. 573-581, 2001.
- 21
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J. A. Schnabel, C. Tanner, A. D. Castellano-Smith, A. Degenhard, M. O. Leach,
D. R. Hose, D. L. G. Hill, and D. J. Hawkes, ``Validation of Non-Rigid Image
Registration using Finite Element Methods: Application to Breast MR
Images,'' IEEE Transactions on Medical Imaging, vol. 22(1), 2003. In
press.
- 22
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C. Tanner, A. Degenhard, J. A. Schnabel, A. Castellano-Smith, C. Hayes, L. I.
Sonoda, M. O. Leach, D. R. Hose, D. L. G. Hill, and D. J. Hawkes, ``A Method
for the Comparison of Biomechanical Breast Models,'' in IEEE Workshop
on Mathematical Methods in Biomedical Image Analysis, Kauai, USA,
pp. 11-18, 2001.
- 23
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J. A. Schnabel, C. Tanner, A. D. Castellano-Smith, A. Degenhard, C. Hayes,
M. O. Leach, D. R. Hose, D. L. G. Hill, and D. J. Hawkes, ``Finite Element
Based Validation of Non-Rigid Registration using Single- and Multi-Level
Free-Form Deformations: Application to Contrast-Enhanced MR Mammography,''
in Proceedings SPIE Medical Imaging 2002, Image Processing, San Diego,
CA, pp. 550-581, 2002.
- 24
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C. Tanner, J. A. Schnabel, A. Degenhard, A. Castellano-Smith, C. Hayes, M. O.
Leach, D. R. Hose, D. L. G. Hill, and D. J. Hawkes, ``Validation of
Volume-Preserving Non-Rigid Registration: Application to Contrast-Enhanced
MR-Mammography.,'' in Medical Image Computing and Computer-Assisted
Intervention, Tokyo, Japan, Vol. 2489 of Lecture Notes in Computer Science,
Springer Verlag, pp. 307-314, 2002.
- 25
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J. A. Schnabel, C. Tanner, A. D. Castellano-Smith, A. Degenhard, C. Hayes,
M. O. Leach, D. R. Hose, D. L. G. Hill, and D. J. Hawkes, ``Validation of
Non-Rigid Registration of Contrast-Enhanced MR Mammography using Finite
Element Methods.,'' in In Proc. Workshop on Bildverarbeitung fuer die
Medizin, Informatik Aktuell, pp. 143-146, 2002.
- 26
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C. Meyer, J. Boes, B. Kim, and P. Bland, ``Evaluation of Control Point
Selection in Automatic, Mutual Information Driven, 3D Warping,'' in
Medical Image Computing and Computer-Assisted Intervention, Cambridge, USA,
pp. 944-951, 1998.
- 27
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H. Fischer, M. Otte, C. Ehritt-Braun, M. Buechert, P. Peschl, and J. Hennig,
``Local Elastic Motion Correction in MR-Mammography,'' in Proceedings
of the Sixth Scientific Meeting of the International Society for MR in
Medicine, Sydney, Australia, p. 725, 1998.
- 28
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H. Fischer, ``Automatic Elastic Motion Correction in MR-Mammography,'' in
2nd International Congress on MR-Mammography, Jena, Germany, published
in European Radiology 10:9, pp. F48-F49, 2000.
- 29
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R. Lucht, M. V. Knopp, and G. Brix, ``Elastic Matching of Dynamic MR Image
Data Sets of the Female Breast,'' in Proceedings of the Sixth
Scientific Meeting of the International Society for MR in Medicine, Sydney,
Australia, p. 940, 1998.
- 30
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R. Lucht, M. V. Knopp, and G. Brix, ``Elastic Matching of Dynamic MR
Mammographic Images,'' Magnetic Resonance in Medicine, vol. 43,
pp. 9-16, January 2000.
- 31
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T. Brückner, R. Lucht, and G. Brix, ``Comparison of Rigid and Elastic
Matching of Dynamic Magnetic Resonance Mammographic Images by Mutual
Information,'' Medical Physics, vol. 27, pp. 2456-2461, October 2000.
- 32
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S. Krishnan, T. L. Chenevert, M. A. Helvie, and F. L. Londy, ``Linear Motion
Correction in Three Dimensions Applied to Dynamic Gadolinium Enhanced Breast
Imaging,'' Medical Physics, vol. 26, pp. 707-714, 1999.
- 33
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M. A. Wirth, A Nonrigid Approach to Medical Image Registration: Matching
Images of the Breast.
PhD thesis, RMIT University Melbourne, 1999.
- 34
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J. R. Reichenbach, J. Hopfe, R. Lucht, W. A. Kaiser, and M. E. Bellemann,
``Image Registration of Serial 3D MR Breast Data Using Local Parameters,''
in Proceedings of the Eighth Scientific Meeting of the International
Society for MR in Medicine, Denver, USA, p. 2173, 2000.
- 35
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J. R. Reichenbach, J. Hopfe, M. E. Bellemann, and W. A. Kaiser, ``Development
and Validation of an Algorithm for Registration of Serial 3D MR Breast Data
Sets,'' Magnetic Resonance Materials in Physics, Biologie and
Medicine, vol. 14, pp. 249-258, 2002.
- 36
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T. Rohlfing and C. R. Maurer, ``Intensity-Based Non-rigid Registration Using
Adaptive Multilevel Free-Form Deformation with an Incompressibility
Constraint,'' in Medical Image Computing and Computer-Assisted
Intervention, Utrecht, Netherlands, Vol. 2208 of Lecture Notes in Computer
Science, Springer Verlag, pp. 111-119, 2001.
- 37
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B. Fischer and J. Modersitzki, ``Fast Curvature Based Registration of
MR-Mammography Images,'' in In Proc. Workshop on Bildverarbeitung fuer
die Medizin, Informatik Aktuell, pp. 139-143, 2002.
- 38
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B. Fischer and J. Modersitzki, ``A Unified Approach to Fast Image Registration
and a New Curvature Based Registration Technique,'' Technical Report,
Preprint A-02-07, Institute of Mathematics, Medical University of Luebeck,
pp. 1-17, 2002.
Christine.Tanner
2003-02-25