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Proceedings Paper

Robust detection of lumen centerlines in complex coronary angiograms
Author(s): Milan Sonka; Steve M. Collins
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Paper Abstract

We have developed a method for lumen centerline detection based on simultaneous detection of approximate left and right coronary borders. This approach is motivated by the observation that a clinician visually identifies the lumen centerline as midway between the simultaneously determined left and right borders of the vessel segment of interest. Our lumen centerline detection algorithm and an algorithm based on a conventional method for individually identifying left and right coronary borders were tested using 89 complex coronary images. Selected manually-traced centerlines defined in a previous angioplasty study were used as an independent standard. Computer-detected and observer-defined centerlines were compared using five parameters (maximum and rms distances, maximum and average orientation differences, and orientation similarity index). The quality of centerlines determined using the new simultaneous centerline detection method, a modification incorporating an initial maximum brightness search, and a conventional centerline detection method was also assessed. Our new centerline detection method yielded accurate centerlines in the 89 complex images. Moreover, our method outperformed the conventional method as judged by all five calculated parameters (p < 0.001 for each parameter). Automated detection of lumen centerlines based on simultaneous detection of both coronary borders provides improved accuracy in coronary arteriograms with poor contrast, nearby or overlapping structures, or branching vessels.

Paper Details

Date Published: 29 July 1993
PDF: 11 pages
Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); doi: 10.1117/12.148622
Show Author Affiliations
Milan Sonka, Univ. of Iowa (United States)
Steve M. Collins, Univ. of Iowa (United States)

Published in SPIE Proceedings Vol. 1905:
Biomedical Image Processing and Biomedical Visualization
Raj S. Acharya; Dmitry B. Goldgof, Editor(s)

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