We propose a novel solution to the problem of motion compensation of coronary angiographs. As the heart is beating, it is difficult for the physician to observe closely a particular point (e.g. stenosis) on the artery tree. We propose, to rigidly compensate the sequence so that the area around the point of interest appears stable. This is a difficult problem because the arteries deform in a non-rigid manner and only their 2D X-ray projection is observed. Also, the lack of features around the selected point makes the matching subject to the aperture problem. The algorithm automatically extracts a section of the artery of interest, models it as a polyline, and tracks it. The problem is formulated as an energy minimization problem which is solved using a shortest path in a graph algorithm. The motion compensated sequence can be obtained by translating every pixel so that the point of interest remains stable. We have applied this algorithm to many examples in two sets of angiography data and have obtained excellent results.

Modeling nonrigid moving 3-D objects entails recovery of global motion, global shape, and local shape. The already difficult problem of tracking nonrigid local shape is made worse by the need for a model-data correspondence scheme to make tracking possible. Both problems are addressed. Various methods for establishing 3-D model-data correspondence are discussed, and a correspondence scheme for tessellated data is presented. A force-based method for nonrigid motion tracking is presented. The authors' method does not use complex finite element techniques but follows a residual force-based dynamic model for local deformation. Another novel feature is that the net forces are obtained by an adaptive force distribution scheme which ensures smooth and stable deformation. Results are presented of tracking simulated as well as real 3-D data of the inner wall of the heart ventricle from segmented magnetic resonance images.>

A linking algorithm to fill gaps between edge segments is presented. The gap filling operation is performed in an iterative manner rather than a single step. During each iteration, the noises are also removed gradually. The proposed algorithm starts with connecting the tip ends of two (line or curve) segments with a line segment and then tries to modify the resulting segment by straight-line fitting. The two major phases of the algorithm are detailed step by step. The advantages of using the algorithm are also discussed.>