automatic registration-fusion

In medical imaging a frequent task has become

the registration of images from a subject taken with different imaging modalities,

where the term modalities here refers to imaging techniques such as Computed Tomography (CT), Magnetic Resonance Tomography (MRT) and Positron Emission Tomography (PET).

The challenge in inter-modality registration lies in the fact that e.g in CT images’bright’ regions are not necessarily bright regions in MRT images of the same subject.
exemple:
an affine registration, i.e. it determines an optimal transformation with respect to translation, rotation, anisotrope scaling, and shearing.
Closely related to registration is the task of image fusion, i.e. the simultaneous visualization of two
registered image datasets.

————Basic Manual Registration

play with software (i.e. amira) for a better alignment of the CT and MRT data, but it’s still not perfect…

———–Automatic Registration

an automatic registration is via optimization of a quality function.

For registration of datasets from different imaging modalities, in amira, the Normalized Mutual Information is the best suited quality function. In short, it favors an alignment which ’maps similar gray values to similar
gray values’. A hierarchical strategy is applied, starting at a coarse resampling of the datasets, and
proceeding to finer resolutions later on.

————Registration Using Landmarks

You should be able to load files, interact with the 3D viewer, and be
familiar with the 2-viewer layout and the viewer toggles.
We will transform two 3D objects into each other by first setting landmarks on their surfaces and then
defining a mapping between the landmark sets. As a result we shall see a rigid transformation and a
warping which deforms one of the objects to match it with the other. The steps are:
1. Displaying data sets in two viewers.
2. Creating a landmark set.
3. Alignment via a rigid transformation.
4. Warping two image volumes.