In this paper, we estimate the deformations induced on soft tissues by the rigid independent movements of
hard objects and create an admixture of rigid and elastic adaptive image registration transformations. By
automatically segmenting and independently estimating the movement of rigid objects in 3D images, we can
maintain rigidity in bones and hard tissues while appropriately deforming soft tissues. We tested our algorithms
on 20 pairs of 3D MRI datasets pertaining to a kinematic study of the flexibility of the ankle complex of normal
feet as well as ankles affected by abnormalities in foot architecture and ligament injuries. The results show
that elastic image registration via rigid object-induced deformation outperforms purely rigid and purely nonrigid
approaches.
The computerized assistive process of recognizing, delineating and quantifying organs and tissue regions in medical
images, occurring automatically during clinical image interpretation, is called automatic anatomic recognition (AAR).
This paper studies the feasibility of developing an AAR system in clinical radiology. The anatomy recognition method
described here consists of three components: (a) oriented active shape modeling (OASM); (b) multi object generalization
of OASM; (c) object recognition strategies. (b) and (c) are novel and depend heavily on the idea of OASM, presented
previously in this conference. The delineation of an object boundary is done in OASM via a two level dynamic
programming algorithm wherein the first level finds optimal location for the landmarks and the second level finds
optimal oriented boundary segments between successive landmarks. This algorithm is generalized to multiple objects by
including a model for each object and assigning a cost structure specific to each object in the spirit of live wire. The
object recognition strategy attempts to find that pose vector (consisting of translation, rotation, and a scale component)
for the multi object model that yields the smallest total boundary cost for all objects. The evaluation results on a routine
clinical abdominal CT data set indicate the following: (1) High recognition accuracy can be achieved without fail by
including a large number of objects which are spread out in the body region; (2) An overall delineation accuracy of
TPVF>97%, FPVF<0.2% was achieved, suggesting the feasibility of AAR.
Nonrigid image registration plays an important role in medical application fields. Owing to its complex computations,
it incurs high computational cost. In this paper, a parallel algorithm schema for nonrigid image
registration methods that use B-splines for deformation and mutual information as a similarity measure is proposed.
It involves a complex interplay of various steps which are analyzed in considerable detail from the view
point of parallelizing registration. The algorithms are implemented on a cluster of workstations. We present
some results on a 10 processor cluster of PCs and compare them with a sequential implementation. The results
show that a speed up of n/2 for n processors in registering large images. The method is fully portable and
seamlessly expandable.
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