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ARISTOTLE UNIVERSITY OF THESSALONIKI. DEPARTMENT OF INFORMATICS 2D/3D Image Registration and Alignment: A Review Stelios Krinidis Presentation outline Definitions General aspects ICP algorithm Shape-based algorithm References Definitions Registration: a fundamental task in image

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Aristotle university of thessaloniki department of informatics l.jpg
ARISTOTLE UNIVERSITY OF THESSALONIKI.DEPARTMENT OF INFORMATICS

2D/3D Image Registration and Alignment:

A Review

Stelios Krinidis

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Presentation outline

  • Definitions

  • General aspects

  • ICP algorithm

  • Shape-based algorithm

  • References

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


Definitions l.jpg
Definitions

  • Registration:a fundamental task in image

  • processing used to match two or more pictures taken,

  • for example, at different times, from different sensors,

  • or from different viewpoints.

  • Alignment:a fundamental task in image processing

  • used to match two or more pictures that are similar

  • but not alike, for example different sections from a 3D

  • object.

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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General aspects (1)

  • Registration/Alignment can be used to:

  • 3D object reconstruction from its 2D sections.

  • 3D object visualization and morphological analysis.

  • Compare medical tissues (taken at different times)

  • showing tumor growth, internal abnormalities, etc.

  • Medical and surgical analysis, tests and simulations.

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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General aspects (2)

  • Registration/Alignment (2D and 3D) compensation:

  • rotation and translation (MRI, CT, etc)

  • non-rigid transforms (physical sectioning of

  • biological tissues, anatomical atlases, etc)

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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General aspects (3)

  • Proposed Registration/Alignment methods:

  • fiducial marker-based

  • feature-based using contours

  • crest lines or characteristics points

  • gray level-based

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Iterative Closest Point (ICP)

  • It can be used with the following representations of

  • geometrical data:

  • points sets

  • line segments (polylines)

  • implicit curves: g(x,y,z) = 0

  • parametric curves: (x(u),y(u),z(u))

  • triangle sets (faceted surfaces)

  • implicit surfaces: g(x,y,z) = 0

  • parametric surfaces: (x(u,υ),y(u,υ),z(u,υ))

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Iterative Closest Point (ICP)

  • Characteristics:

  • monotonic convergence to the nearest local minimum

  • rapid convergence during the first few iterations

  • global convergence depends on the initial parameters

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Iterative Closest Point (ICP)

Model point set:

Data point set:

Closest point set:

Distance metric:

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Iterative Closest Point (ICP)

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Iterative Closest Point (ICP)

Quaternion is the eigenvector related to the largest eigenvalue:

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Iterative Closest Point (ICP)

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Iterative Closest Point (ICP)

Point Set Matching

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Iterative Closest Point (ICP)

Curve Set Matching

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Iterative Closest Point (ICP)

Surface Set Matching

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Shape-Based Alignment

  • Alignment of 2D serially acquired sections forming a

  • 3D object

  • Characteristics:

  • shape-based algorithm (contours)

  • global energy function (expressing similarity between

  • neighboring slices).

  • no direction is privileged

  • no global offset

  • no error propagation

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Shape-Based Alignment

N : frame number

Nx: horizontal image dimension

Ny: vertical image dimension

R : neighborhood’s length

f : pixel similarity metric

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Shape-Based Alignment

Di : Distance Transform of image i

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Shape-Based Alignment

Distance Transform: each pixel has value equal to the pixel’s distance from the nearest non-zero pixel.

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Shape-Based Alignment

Alignment Errors Statistics

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Shape-Based Alignment

Alignment Errors Statistics

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Shape-Based Alignment

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Shape-Based Alignment

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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Shape-Based Alignment

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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References

  • P. Van den Elsen, E.J.D. Paul, and M.A.Viergever. Medical Image Matching – A review with classification. IEEE engineering in Medicine and Biology, 12(1):26-39, 1993.

  • M.J.Besl and N.McKay. A Method for the Registration of 3D Shapes. IEEE transactions of Pattern Analysis and Machine Intelligence(PAMI), 14(2):239-256, 1992

  • G.Borgefors. Hierarchical Chamfer Matching: A parametric edge matching algorithm. IEEE transactions of Pattern Analysis and Machine Intelligence(PAMI), 679-698, 1986.

  • W.Wells III, P.Viola, H.Atsumi, S.Nakajima, and R.Kikinis. Multimodal volume registration by maximization of mutual information. Medical Image Analysis, 1(1):33-51, 1996.

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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References

  • C.Nikou, J.P.Armspach, F.Heitz, I.J.Namer, and D.Grucker. MR/MR and MR/SPECT registration of brain by fast stochastic optimization of robust voxel similarity measures NeuroImage, 8(1):30-43, 1998.

  • S.Krinidis, N.Nikolaidis, I.Pitas. Shape Based Alignment of 3-D Volume Slices. International Conference on Electronics, Circuits and Systems (ICECS'00) Kaslik, Lebanon, 17-20 September 2000.

Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001


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