Biomedical Engineering Reference
In-Depth Information
the registration of the acquired images is comparatively easy, fast, can usually
be automated, and, since the registration parameters can often be computed
explicitly, has no need for complex optimization algorithms. The main draw-
back of extrinsic registration is that, for good accuracy,
invasive
maker (e.g.,
stereotactic frame or screw markers) objects are used.
Non-invasive
markers
(e.g., skin markers individualized foam moulds, head holder frames, or dental
adapters) can be used, but as a rule are less accurate.
Intrinsic registration can rely on
landmarks
in the images or volumes to be
aligned. These landmarks can be
anatomical
based on morphological points on
some visible anatomical organ(s), or pure
geometrical
based. Intrinsic registra-
tion can also be based on segmentation results. Segmentation in this case can be
rigid
where anatomically the same structures (mostly surfaces) are extracted
from both images to be registered, and used as sole input for the alignment pro-
cedure. They can also be
deformable model based
where an extracted structure
(also mostly surfaces, and curves) from one image is elastically deformed to fit
the second image. The rigid model based approaches are probably the most pop-
ular methods due to its easy implementation and fast results. A drawback of rigid
segmentation based methods is that the registration accuracy is limited to the ac-
curacy of the segmentation step. In theory, rigid segmentation based registration
is applicable to images of many areas of the body, yet, in practice, the application
areas have largely been limited to neuroimaging and orthopedic imaging.
Another example of intrinsic registration are the
voxel based
registration
methods that operate directly on the image gray values, without prior data re-
duction by the user or segmentation. There are two distinct approaches: the first
is to immediately reduce the image gray value content to a representative set
of scalars and orientations, the second is to use the full image content through-
out the registration process. Principal axes and moments based methods are
the prime examples of reductive registration methods. Within these methods
the image center of gravity and its principal orientations (principal axes) are
computed from the image zeroth and first order moments. Registration is then
performed by aligning the center of gravity and the principal orientations. The
result is usually not very accurate, and the method is not equipped to handle dif-
ferences in scanned volume well. Despite its drawbacks, principal axes methods
are widely used in registration problems that require no high accuracy, because
of the automatic and very fast nature of its use, and the easy implementation.
On the other hand, voxel based registration using full image content is more
