The re-alignment of series of medical images in which there are multiple contrast variations is difficult.
The reason for this is that the popularmeasures of image similarity used to drive the alignment procedure
do not separate the influence of intensity variation due to image feature motion and intensity variation
due to feature enhancement. In particular, the appearance of new structure poses problems when it
has no representation in the original image. The acquisition of many images over time, such as in
dynamic contrast enhanced MRI, requires that many images with different contrast be registered to the
same coordinate system, compounding the problem. This thesis addresses these issues, beginning by
presenting conditions under which conventional registration fails and proposing a solution in the form of
a ’progressive principal component registration’. The algorithm uses a statistical analysis of a series of
contrast varying images in order to reduce the influence of contrast-enhancement that would otherwise
distort the calculation of the image similarity measures used in image registration. The algorithm is
shown to be versatile in that it may be applied to series of images in which contrast variation is due to
either temporal contrast enhancement changes, as in dynamic contrast-enhanced MRI or intrinsically in
the image selection procedure as in diffusion weighted MRI
