Region-based active contours are a variational framework for image segmentation. It involves estimating the
probability distributions of observed features within each image region. Subsequently, these so-called region
descriptors are used to generate forces to move the contour toward real image boundaries. In this paper region
descriptors are computed from samples within windows centered on contour pixels and they are named local
region descriptors (LRDs). With these descriptors we introduce an equation for contour motion with two terms:
growing and competing. This equation yields a novel type of AC that can adjust the behavior of contour pieces to
image patches and to the presence of other contours. The quality of the proposed motion model is demonstrated
on complex images.
In this paper we present a novel fast method for the non-rigid registration of a few X-ray projections with
CT data. The method involves non-parametric non-rigid registration techniques for the difficult 2D-3D case,
combined with knowledge of probable deformations modeled as active shape models (ASMs). ASMs allow us
to cope with as few as two projections by regularizing the registration process. The model is learned from
deformations observed during respiration in a 4D-CT. This method can be applied in motion compensated
radiation therapy to eliminate the need for fiducial implantation. We designed a fast C++ implementation for
our method in order to make it practicable. Our tests on real 4D-CT data achieved registration times of 2-4
minutes using a desktop PC.