Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001 ARISTOTLE UNIVERSITY OF THESSALONIKI. DEPARTMENT OF INFORMATICS Stelios Krinidis

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

ARISTOTLE UNIVERSITY OF THESSALONIKI.ARISTOTLE UNIVERSITY OF THESSALONIKI.DEPARTMENT OF INFORMATICSDEPARTMENT OF INFORMATICS

Stelios KrinidisStelios Krinidis

2D/3D Image Registration and 2D/3D Image Registration and Alignment: Alignment: A ReviewA Review


Presentation outlinePresentation outline

Definitions

General aspects

ICP algorithm

Shape-based algorithm

References


DefinitionsDefinitions 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.


General aspects (1)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.


General aspects (2)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)


General aspects (3)General aspects (3)

Proposed Registration/Alignment methods:

fiducial marker-based

feature-based using contours

crest lines or characteristics points

gray level-based


Iterative Closest Point Iterative Closest Point (ICP)(ICP)

It can be used with the following representations of geometrical data:points sets

line segments (polylines)implicit curves: g(x,y,z) = 0parametric curves: (x(u),y(u),z(u))triangle sets (faceted surfaces)implicit surfaces: g(x,y,z) = 0parametric surfaces: (x(u,υ),y(u,υ),z(u,υ))



Characteristics:

• monotonic convergence to the nearest local minimum

• rapid convergence during the first few iterations

• global convergence depends on the initial parameters



Model point set:

Data point set:

Closest point set:

Distance metric:





Quaternion is the eigenvector related to the largest eigenvalue:





Point Set Matching



Curve Set Matching



Surface Set Matching


Shape-Based AlignmentShape-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



N : frame numberNx: horizontal image dimensionNy: vertical image dimensionR : neighborhood’s lengthf : pixel similarity metric



Di : Distance Transform of image i



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



Alignment Errors Statistics



Alignment Errors Statistics








ReferencesReferences

1) 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.

2) 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

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

4) 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.


ReferencesReferences

5) 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.

6) 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.

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Department of Informatics, Aristotle University of Thessaloniki, May 4, 2001 ARISTOTLE UNIVERSITY OF THESSALONIKI. DEPARTMENT OF INFORMATICS Stelios Krinidis