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Model Guided Automatic Frame-To-Frame Segmentation In Digital Subtraction Angiography

Image sequences of moving organs provide pattern motion informations and their appropriate management leads to new looks at image segmentation. We propose in this paper an illustration of the benefits that can be expected for coronary vessels analysis. A new method is described which combines the motion estimation with the frame-to-frame structure detection in a natural way such that they act interactively. The first step consists of the extraction of the vessel centerlines in one image. These data are further organized in meaningful constituents or branches of the coronary arterial tree. Then, the motion is estimated at each point of the centerlines through a gradient-based method. These motion estimates supply an initial positioning of an active contour model (or "snake") in the next image. This model adapts itself by changing its shape and locks accurately onto the new distorted centerlines. This whole process is then reiterated on the subsequent images to depict the dynamic behaviour of all the relevant branches. The main interests of this scheme are : (1) the snakes operate locally, so a fast detection can be performed; (2) the skeleton extraction is fully guided by the confluence of the motion estimation and the active contour modelling; (3) it can be easily extended to derive the boundaries of the coronary arteries; (4) both morphological and kinetic properties are achieved on a quantitative basis.