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FEATURE-BASED RIGID MOTION EXTRACTION
IN KNEE FLUOROSCOPY IMAGES
D. Smeets1,2, J. Keustermans1,2, D. Vandermeulen1,2, P. Suetens1,2
1K.U.Leuven,
2
ESAT/PSI, Medical Image Computing, Belgium
IBBT-K.U.Leuven Future Health Department, Belgium
Abstract
This paper presents a method to extract multiple
rigid motions from 2D medical images in the
presence of outliers. Based on a clustering on the
manifold of rigid transformations the most important
rigid motions between two images are extracted.
We give a proof-of-concept for fluoroscopy images
of knees.
Keyword(s): medical imaging
1
Introduction
Piecewise rigid registration is a fundamental
problem in medical imaging and precedes the
compensation of patient pose differences in multiple
medical images, mostly due to articulated motion.
During motion, the main challenge is the large
deformation, for which the classical local registration
methods fail [1]. Therefore, multiple rigid
transformations need to be estimated for a better
initialization of dense (non-rigid) registration.
In this paper, rigid motions between knee
fluoroscopy images after total knee replacement will
be estimated. With this information, a clinician can
assess the kinematics of the knee joint and
compare the mobility before and after surgery.
2
Figure 1: Schematic representation of Lie Group (sphere)
and associated Lie algebra (plane).
In order to reduce the computational cost, a sample
is used further if the sampled transformation maps
at least one of the other features. Given the rigid
transformation matrices as samples of a distribution
defined on a Lie group, the modes of the
distribution, corresponding to the main rigid motion
clusters, are found with the mean shift algorithm, a
robust clustering technique without need for prior
knowledge of the number of clusters. Finally, each
correspondence is assigned to the largest cluster.
3 Experimental results
Figure 2 shows the result of motion estimation in a
pair of fluoroscopy images.
Method
2.1 Feature matching
In a first step, salient points, so called keypoints, are
localized and described in both images. Therefore,
the scale-invariant feature transform (SIFT) [2] is
used. The detected SIFT features are matched by
finding the closest feature descriptor in the other
image for each feature descriptor, after which the
matches are filtered for robustness.
2.2 Rigid motion clustering
Next, the rigid motion clustering extracts the
relevant rigid motions present in between both
images, by sampling rigid transformation matrices in
the special Euclidean group SE(2) and mean shift
clustering in the associated Lie algebra (2) [3].
A sample is taken by randomly selecting n
corresponding feature pairs and computing the
translation vector tj and rotation matrix Rj .
Figure 2: Rigid motion clusters (red and green) found
between knee fluoroscopy images of a cadaver.
References
[1] Li, X., et al., Automatic registration of whole
body serial micro CT images with a combination
of point-based and intensity-based registration
techniques, ISBI 2006. p454-457.
[2] Lowe, D.: Object recognition from local scaleinvariant feature, ICCV 1999, p1150-1157.
[3] Tuzel, O., et al, Simultaneous multiple 3D
motion estimation via mode finding on lie
groups, ICCV 2005 (1), p18–25.
10th Belgian Day on Biomedical Engineering – joint meeting with IEEE EMBS Benelux Chapter
December 2, 2011