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A STATISITICAL SHAPE MODEL FOR DEFORMABLE
SURFACE REGISTRATION
Wei Quan, Bogdan J. Matuszewski and Lik-Kwan Shark
Applied Digital Signal and Image Processing (ADSIP) Research Centre
University of Central Lancashire, Preston PR1 2HE, U.K.
Keywords: Deformable Registration, Surface Matching, Shape Modelling and Face Articulation.
Abstract: This short paper presents a deformable surface registration scheme which is based on the statistical shape
modelling technique. The method consists of two major processing stages, model building and model
fitting. A statistical shape model is first built using a set of training data. Then the model is deformed and
matched to the new data by a modified iterative closest point (ICP) registration process. The proposed
method is tested on real 3-D facial data from BU-3DFE database. It is shown that proposed method can
achieve a reasonable result on surface registration, and can be used for patient position monitoring in
radiation therapy and potentially can be used for monitoring of the radiation therapy progress for head and
neck patients by analysis of facial articulation.
1 INTRODUCTION
The registration of 3-D surfaces can be considered
as a subset of the general image registration problem
as surveyed by Maintz and Viergever (Maintz and
Viergever, 1998), and has been for many years of
great interest to computer vision community
(Audette et al., 2000). Its applications include
biomedical modelling (Vrtovec et al., 2004),
automated segmentation of medical images
(Lamecker et al., 2003), integrating multiple range
scans into a 3-D model (Hahnel et al., 2003), visual
navigation (Zhang, 1994) and recognition of objects
from database (Lu et al., 2006), etc. In general,
surface registration can be partitioned into three
major components: choice of transformation model,
similarity measure, and optimization method.
The first component concerns the assumptions
made about the relation between the surfaces which
need to be registered. Transformation models can be
roughly classified into two categories, rigid and
deformable. A general rigid transformation can be
expressed as a superposition of rotation and
translation. Deformable transformation could be
similarity, affine, perspective, B-spline, radial basis
function, etc (Audette et al., 2000). The second
component determines what type of characteristic
needs to be extracted from 3-D surfaces. Generally,
the spectrum of surface characteristics includes
landmarks, curves, regions, and dense point sets.
Landmarks are well-localised, sparse loci of
important geometric significance (Thirion, 1994).
Landmarks are frequently determined using
computed surface curvatures or are selected
manually. Curve features typically consist of
differential structures which are usually extracted
from ridges or boundaries between two different
regions in the surfaces (Maintz et al., 1996). Region
features are defined by the areas processing some
homogeneous characteristics, such as consistent
curvature signs (Toriwaki and Yokoi, 1988). Dense
point sets are the feature which constitutes all or
significant subset of all available surface points
(Besl and McKay, 1992). The third component is
about finding parameters of the transformation
which could maximise the similarity measure. This
usually includes the search for the correspondence of
surface characteristics which are used for measuring
the similarity of surfaces. Some frequently used
methods are the random sample consensus
(RANSAC) (Chen et al., 1999), expectation
maximization (EM) (Granger et al., 2001), various
ICP algorithms (Hahnel et al., 2003), etc.
In this paper, a novel method for deformable
surface registration is proposed based on the
authors’ previous work (Quan et al., 2009), which
uses the statistical shape modelling technique to
achieve the deformable registration. The whole
545
Quan W., J. Matuszewski B. and Shark L. (2010).
A STATISITICAL SHAPE MODEL FOR DEFORMABLE SURFACE REGISTRATION.
In Proceedings of the International Conference on Computer Vision Theory and Applications, pages 545-549
DOI: 10.5220/0002895705450549
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