Image registration is an important and active area of medical image processing. Given two images, the idea is
to compute a reasonable displacement field which deforms one image such that it becomes similar to the other
image. The design of an automatic registration scheme is a tricky task and often the computed displacement
field has to be discarded, when the outcome is not satisfactory. On the other hand, however, any displacement
field does contain useful information on the underlying images.
It is the idea of this note, to utilize this information and to benefit from an even unsuccessful attempt for the
subsequent treatment of the images. Here, we make use of typical vector analysis operators like the divergence
and curl operator to identify meaningful portions of the displacement field to be used in a follow-up run. The
idea is illustrated with the help of academic as well as a real life medical example. It is demonstrated on how the
novel methodology may be used to substantially improve a registration result and to solve a difficult segmentation
problem.
Image registration and segmentation are two important tasks in medical image analysis. However, the validation
of algorithms for non-linear registration in particular often poses significant challenges:1, 2
Anatomical labeling based on scans for the validation of segmentation algorithms is often not available, and
is tedious to obtain. One possibility to obtain suitable ground truth is to use anatomically labelled atlas images.
Such atlas images are, however, generally limited to single subjects, and the displacement field of the registration
between the template and an arbitrary data set is unknown. Therefore, the precise registration error cannot be
determined, and approximations of a performance measure like the consistency error must be adapted. Thus,
validation requires that some form of ground truth is available.
In this work, an approach to generate a synthetic ground truth database for the validation of image registration
and segmentation is proposed. Its application is illustrated using the example of the validation of a registration
procedure, using 50 magnetic resonance images from different patients and two atlases. Three different non-linear
image registration methods were tested to obtain a synthetic validation database consisting of 50 anatomically
labelled brain scans.
Accurate image registration is a necessary prerequisite for many diagnostic and therapy planning procedures
where complementary information from different images has to be combined. The design of robust and reliable
non-parametric registration schemes is currently a very active research area. Modern approaches combine
the pure registration scheme with other image processing routines such that both ingredients may benefit from
each other. One of the new approaches is the combination of segmentation and registration ("segistration").
Here, the segmentation part guides the registration to its desired configuration, whereas on the other hand
the registration leads to an automatic segmentation. By joining these image processing methods it is possible
to overcome some of the pitfalls of the individual methods. Here, we focus on the benefits for the registration task.
In the current work, we present a novel unified framework for non-parametric registration combined with energy-based
segmentation through active contours. In the literature, one may find various ways to combine these image
processing routines. Here, we present the most promising approaches within the general framework. It is based
on a single variational formulation of both the registration and the segmentation part. The performance tests
are carried out for magnetic resonance (MR) images of the brain, and they demonstrate the potential of the
proposed methods.
We have compared and validated image registration methods with respect to the clinically relevant use-case
of lung CT max-inhale to max-exhale registration. Four fundamentally different algorithms representing main
approaches for image registration were compared using clinical images. Each algorithm was assigned to a different
person with extensive working knowledge of its usage. Quantitative and qualitative evaluation is performed.
Whereas the methods achieve similar results in target registration error, characteristic differences come to show
by closer analysis of the displacement fields.
One of the future-oriented areas of medical image processing is to develop fast and exact algorithms for image
registration. By joining multi-modal images we are able to compensate the disadvantages of one imaging modality
with the advantages of another modality. For instance, a Computed Tomography (CT) image containing the
anatomy can be combined with metabolic information of a Positron Emission Tomography (PET) image. It is
quite conceivable that a patient will not have the same position in both imaging systems. Furthermore some
regions for instance in the abdomen can vary in shape and position due to different filling of the rectum. So a
multi-modal image registration is needed to calculate a deformation field for one image in order to maximize the
similarity between the two images, described by a so-called distance measure.
In this work, we present a method to adapt a multi-modal distance measure, here mutual information (MI),
with weighting masks. These masks are used to enhance relevant image structures and suppress image regions
which otherwise would disturb the registration process. The performance of our method is tested on phantom
data and real medical images.
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