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Proceedings Paper

Automated detection of wall and plaque borders in intravascular ultrasound images
Author(s): Milan Sonka; Xiangmin Zhang; Maria Siebes; Ramakrishna R. Chada; Charles R. McKay; Steve M. Collins
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Paper Abstract

Intravascular ultrasound is a minimally invasive tomographic technique which produces 2-D cross-sectional images depicting vessel wall architecture and plaque morphology. Currently, no reliable automated approaches exist that offer segmentation of blood and vascular wall. We have developed a method for automated segmentation of intravascular ultrasound images to differentiate among plaque, wall, and blood. To achieve reliable border detection in noisy intravascular ultrasound images, a priori knowledge is incorporated in the edge detection process using heuristic graph searching. The method was validated using images from two phantoms that were imaged under several pressure conditions. In the first image set, our automated border detection method correctly identified the wall and plaque borders in 69/91 images. In the second image set, our method successfully identified external and internal wall and plaque borders in all 36 images. Lumen cross-sectional areas correlated very well with distending pressure in both sets of images. By comparison with the micrometer determined average wall thickness, mean absolute error of wall thickness was 0.02 +/- 0.01 mm.

Paper Details

Date Published: 1 May 1994
PDF: 10 pages
Proc. SPIE 2168, Medical Imaging 1994: Physiology and Function from Multidimensional Images, (1 May 1994); doi: 10.1117/12.174406
Show Author Affiliations
Milan Sonka, Univ. of Iowa (United States)
Xiangmin Zhang, Univ. of Iowa (United States)
Maria Siebes, Univ. of Iowa (United States)
Ramakrishna R. Chada, Univ. of Iowa (United States)
Charles R. McKay, Univ. of Iowa (United States)
Steve M. Collins, Univ. of Iowa (United States)

Published in SPIE Proceedings Vol. 2168:
Medical Imaging 1994: Physiology and Function from Multidimensional Images
Eric A. Hoffman; Raj S. Acharya, Editor(s)

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