When designing a complex system, adhering to a design methodology is essential to ensure design quality and to shorten the design phase. Until recently, enforcing this could be done only partially or manually. This paper demonstrates how constraint programming technology can enable automation of the design methodology support when the design artifacts reside in a central repository. At any phase of the design, the proposed constraint programming application can indicate whether the design process data complies with the methodology and point out any violations that may exist. Moreover, the application can provide recommendations regarding the design process. The application was successfully used to check the methodology conformance of an industrial example and produced the desired outputs within reasonable times.

The problem is discussed of building a 3-D temporal geometric model of the coronary tree from a succession of biplane (dual views) cardioangiogram images. The model represents geometric topologic information, which can serve as a basis for further research of the organ, and enhance medical diagnosis of angiograms in the optimal planning of bypass surgery. The cardioangiogram image data is a sequence of about 70 image pairs taken during about four cardiac cycles. The major problems dealt with are the recognition of arterial nodes and vessels in successive images, and the registration of matching objects in the two views, to convert them to 3-D coordinates, through all 70 pairs of images in a single case. In reaching towards a fully automatic system, the following points are emphasized: automatic recognition of arterial nodes and vessels in a series of 2-D images along the time axis; and semiautomatic solution of the two-views registration problem, needed to construct a 3-D model.>

Filling regions in raster images is the term given to the problem of extracting a connected region (that contains some preselected seed pixel) and filling it with some color. A connected region is formally defined as the collection of all pixels that are in the transitive closure of a pixel-connectivity operator that is applied to the seed. For example, the 4-pixel-connectivity operator selects all pixels that are (spatially) 4-connected to the pixel operand, and have the same color. This problem can be solved relatively easily if the fill color is distinguishable so that every pixel, once colored (i.e. flagged OLD) will not be considered again. In binary images, the fill “color” is usually a binary pattern and therefore, a “colored” pixel may still have its previous (e.g., black or white) color. This fact makes the problem non-trivial.

We are working towards a general model of the anatomy seen in medical images that can be used to find the boundaries of organs. We argue that such a model must have four components: - multiple resolution copies of the image (pyrarn ids) ; - geometric shape models for organs; - network-like models of inter-organ relationships; and - a top-down control structure that plans ahead.