Automated labeling of coronary arterial tree segments in angiographic projection data

Adrie C. M. Dumay; Jan J. Gerbrands; Rob J. Geest; Patricia E. Verbruggen; Johan H. C. Reiber

doi:10.1117/12.45200

1 June 1991 Automated labeling of coronary arterial tree segments in angiographic projection data

Adrie C. M. Dumay, Jan J. Gerbrands, Rob J. Geest, Patricia E. Verbruggen, Johan H. C. Reiber

Proceedings Volume 1445, Medical Imaging V: Image Processing; (1991) https://doi.org/10.1117/12.45200
Event: Medical Imaging '91, 1991, San Jose, CA, United States

Abstract

The fully automated reporting of the extent of disease from coronary arteriograms is likely to be a four-step procedure: (1) segmentation of the center lines of the coronary tree from one or more angiographic projection(s); (2) detection of the arterial boundaries based on the center lines; (3) automated detection of possible narrowings in the coronary segments, and (4) identification of the coronary arteries. In this paper we will concentrate on the developments of techniques for the last step, the automated identification of coronary arterial segments. Our approach is based on the representation of the projection of the coronary tree by a graph so that graph matching techniques can be used for labeling of the coronary arterial segments. The coronary tree, which is a branching structure with possible crossings and overlaps as visualized in an angiographic projection, is assumed to be segmented more or less successfully from an angiogram with known projection geometry. A model graph is composed from the projection of a three-dimensional representation of the normal coronary tree, and matched with the data graph by inexact graph matching. Two types of graph representations will be discussed. In the first type arterial branching points are represented by nodes and arterial segments by arcs between nodes. In the second type the arterial segments are represented by nodes and relationships between the arteries by arcs. Nodes and arcs in both types of representation are attributed with a semantic vector of object (node) features or relational (arc) features, which is a mixture in the first type of representation. Both types have been implemented and we are currently in the process of determining optimal parameter values for the associated cost functions.

Citation Download Citation

Adrie C. M. Dumay, Jan J. Gerbrands, Rob J. Geest, Patricia E. Verbruggen, and Johan H. C. Reiber "Automated labeling of coronary arterial tree segments in angiographic projection data", Proc. SPIE 1445, Medical Imaging V: Image Processing, (1 June 1991); https://doi.org/10.1117/12.45200

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