Kluwer - Handbook of Biomedical Image Analysis Vol.3
Preface
Our goal is to develop automated methods for the segmentation of threedimensional
biomedical images. Here, we describe the segmentation of confocal
microscopy images of bee brains (20 individuals) by registration to one
or several atlas images. Registration is performed by a highly parallel implementation
of an entropy-based nonrigid registration algorithm using B-spline
transformations. We present and evaluate different methods to solve the correspondence
problem in atlas based registration. An image can be segmented by
registering it to an individual atlas, an average atlas, or multiple atlases. When
registering to multiple atlases, combining the individual segmentations into a
final segmentation can be achieved by atlas selection, or multiclassifier decision
fusion. We describe all these methods and evaluate the segmentation accuracies
that they achieve by performing experiments with electronic phantoms as well
as by comparing their outputs to a manual gold standard.
The present work is focused on the mathematical and computational theory
behind a technique for deformable image registration termed Hyperelastic
Warping, and demonstration of the technique via applications in image registration
and strain measurement. The approach combines well-established principles
of nonlinear continuum mechanics with forces derived directly from threedimensional
image data to achieve registration. The general approach does not
require the definition of landmarks, fiducials, or surfaces, although it can accommodate
these if available. Representative problems demonstrate the robust
and flexible nature of the approach.
Three-dimensional registration methods are introduced for registering MRI
volumes of the pelvis and prostate. The chapter first reviews the applications,
challenges, and previous methods of image registration in the prostate. Then
the chapter describes a three-dimensional mutual information rigid body registration
algorithm with special features. The chapter also discusses the threedimensional
nonrigid registration algorithm. Many interactively placed control
points are independently optimized using mutual information and a thin plate
spline transformation is established for the warping of image volumes. Nonrigid
method works better than rigid body registration whenever the subject position
or condition is greatly changed between acquisitions.
This chapter will cover 1D, 2D, and 3D registration approaches both rigid
and elastic. Mathematical foundation for surface and volume registration approaches
will be presented. Applications will include plastic surgery, lung cancer,
and multiple sclerosis.
Flow-mediated dilation (FMD) offers a mechanism to characterize endothelial
function and therefore may play a role in the diagnosis of cardiovascular
diseases. Computerized analysis techniques are very desirable to give accuracy
and objectivity to the measurements. Virtually all methods proposed up to now
to measure FMD rely on accurate edge detection of the arterial wall, and they
are not always robust in the presence of poor image quality or image artifacts.
A novel method for automatic dilation assessment based on a global image
analysis strategy is presented. We model interframe arterial dilation as a superposition
of a rigid motion model and a scaling factor perpendicular to the artery.
Rigid motion can be interpreted as a global compensation for patient and probe
movements, an aspect that has not been sufficiently studied before. The scaling
factor explains arterial dilation. The ultrasound (US) sequence is analyzed
in two phases using image registration to recover both transformation models.
Temporal continuity in the registration parameters along the sequence is enforced
with a Kalman filter since the dilation process is known to be a gradual
physiological phenomenon. Comparing automated and gold standard measurements
we found a negligible bias (0.04%) and a small standard deviation of the
differences (1.14%). These values are better than those obtained from manual
measurements (bias = 0.47%, SD = 1.28%). The proposed method offers also
a better reproducibility (CV = 0.46%) than the manual measurements (CV =
1.40%).
Download
http://s18.alxa.net/s18/srvs2/02/003...ysis.Vol.3.rar
Preface
Our goal is to develop automated methods for the segmentation of threedimensional
biomedical images. Here, we describe the segmentation of confocal
microscopy images of bee brains (20 individuals) by registration to one
or several atlas images. Registration is performed by a highly parallel implementation
of an entropy-based nonrigid registration algorithm using B-spline
transformations. We present and evaluate different methods to solve the correspondence
problem in atlas based registration. An image can be segmented by
registering it to an individual atlas, an average atlas, or multiple atlases. When
registering to multiple atlases, combining the individual segmentations into a
final segmentation can be achieved by atlas selection, or multiclassifier decision
fusion. We describe all these methods and evaluate the segmentation accuracies
that they achieve by performing experiments with electronic phantoms as well
as by comparing their outputs to a manual gold standard.
The present work is focused on the mathematical and computational theory
behind a technique for deformable image registration termed Hyperelastic
Warping, and demonstration of the technique via applications in image registration
and strain measurement. The approach combines well-established principles
of nonlinear continuum mechanics with forces derived directly from threedimensional
image data to achieve registration. The general approach does not
require the definition of landmarks, fiducials, or surfaces, although it can accommodate
these if available. Representative problems demonstrate the robust
and flexible nature of the approach.
Three-dimensional registration methods are introduced for registering MRI
volumes of the pelvis and prostate. The chapter first reviews the applications,
challenges, and previous methods of image registration in the prostate. Then
the chapter describes a three-dimensional mutual information rigid body registration
algorithm with special features. The chapter also discusses the threedimensional
nonrigid registration algorithm. Many interactively placed control
points are independently optimized using mutual information and a thin plate
spline transformation is established for the warping of image volumes. Nonrigid
method works better than rigid body registration whenever the subject position
or condition is greatly changed between acquisitions.
This chapter will cover 1D, 2D, and 3D registration approaches both rigid
and elastic. Mathematical foundation for surface and volume registration approaches
will be presented. Applications will include plastic surgery, lung cancer,
and multiple sclerosis.
Flow-mediated dilation (FMD) offers a mechanism to characterize endothelial
function and therefore may play a role in the diagnosis of cardiovascular
diseases. Computerized analysis techniques are very desirable to give accuracy
and objectivity to the measurements. Virtually all methods proposed up to now
to measure FMD rely on accurate edge detection of the arterial wall, and they
are not always robust in the presence of poor image quality or image artifacts.
A novel method for automatic dilation assessment based on a global image
analysis strategy is presented. We model interframe arterial dilation as a superposition
of a rigid motion model and a scaling factor perpendicular to the artery.
Rigid motion can be interpreted as a global compensation for patient and probe
movements, an aspect that has not been sufficiently studied before. The scaling
factor explains arterial dilation. The ultrasound (US) sequence is analyzed
in two phases using image registration to recover both transformation models.
Temporal continuity in the registration parameters along the sequence is enforced
with a Kalman filter since the dilation process is known to be a gradual
physiological phenomenon. Comparing automated and gold standard measurements
we found a negligible bias (0.04%) and a small standard deviation of the
differences (1.14%). These values are better than those obtained from manual
measurements (bias = 0.47%, SD = 1.28%). The proposed method offers also
a better reproducibility (CV = 0.46%) than the manual measurements (CV =
1.40%).
Download
http://s18.alxa.net/s18/srvs2/02/003...ysis.Vol.3.rar