Biomedical Engineering Reference
In-Depth Information
Chapter 12
Deformable Image Registration with
Hyperelastic Warping
Alexander I. Veress, Nikhil Phatak, and Jeffrey A. Weiss
12.1
Introduction
The extraction of quantitative information regarding growth and deformation
from series of image data is of significant importance in many fields of science
and medicine. Imaging techniques such as MRI, CT and ultrasound provide a
means to examine the morphology and in some cases metabolism of tissues.
The registration of this image data between different time points after external
loading, treatment, disease or other pathologies is performed using methods
known as deformable image registration.
The goal of deformable image registration is to find a transformation that
best aligns the features of a “template” and “target” image (Fig. 12.1). In the
ideal case, the quantity and quality of the image texture present in the template
and target images, as well as the similarity in underlying anatomical structure,
would yield a unique “best” transformation. In real problems, however, this is
not the case. Deformable image registration is most often ill-posed in the sense
of Hadamard [2-3]. No perfect transformation exists, and the solution depends
on the choice of the cost function and associated solution methods.
Deformable image registration grew primarily out of the pattern recognition
field where significant effort has been devoted to the representation of image
ensembles (e.g., [4-13]). The approaches that are used are usually classified as
either model-based or pixel-based. Model-based approaches typically require
