Segmentation of intravascular ultrasound images: a knowledge-based approach

Milan Sonka; Xiangmin Zhang; Maria Siebes; Mark S Bissing; Steven C DeJong; Steve M Collins; Charles R McKay

doi:10.1109/42.476113

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Segmentation of intravascular ultrasound images: a knowledge-based approach

Journal article

Peer reviewed

Segmentation of intravascular ultrasound images: a knowledge-based approach

Milan Sonka, Xiangmin Zhang, Maria Siebes, Mark S Bissing, Steven C DeJong, Steve M Collins and Charles R McKay

IEEE transactions on medical imaging, Vol.14(4), pp.719-732

12/1995

DOI: 10.1109/42.476113

PMID: 18215876

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Abstract

Intravascular ultrasound imaging of coronary arteries provides important information about coronary lumen, wall, and plaque characteristics. Quantitative studies of coronary atherosclerosis using intravascular ultrasound and manual identification of wall and plaque borders are limited by the need for observers with substantial experience and the tedious nature of manual border detection. We have developed a method for segmentation of intravascular ultrasound images that identifies the internal and external elastic laminae and the plaque-lumen interface. The border detection algorithm was evaluated in a set of 38 intravascular ultrasound images acquired from fresh cadaveric hearts using a 30 MHz imaging catheter. To assess the performance of our border detection method we compared five quantitative measures of arterial anatomy derived from computer-detected borders with measures derived from borders manually defined by expert observers. Computer-detected and observer-defined lumen areas correlated very well (r=0.96, y=1.02x+0.52), as did plaque areas (r=0.95, y=1.07x-0.48), and percent area stenosis (r=0.93, y=0.99x-1.34.) Computer-derived segmental plaque thickness measurements were highly accurate. Our knowledge-based intravascular ultrasound segmentation method shows substantial promise for the quantitative analysis of in vivo intravascular ultrasound image data.

Anatomy

Heart

Image segmentation

Ultrasonic imaging

Ultrasonic variables measurement

Atherosclerosis

Detection algorithms

Arteries

Catheters

Thickness measurement

Details

Title: Subtitle: Segmentation of intravascular ultrasound images: a knowledge-based approach
Creators: Milan Sonka - Dept. of Electr. & Comput. Eng., Iowa Univ., Iowa City, IA, USA
Xiangmin Zhang - Dept. of Electr. & Comput. Eng., Iowa Univ., Iowa City, IA, USA
Maria Siebes - Dept. of Electr. & Comput. Eng., Iowa Univ., Iowa City, IA, USA
Mark S Bissing - Dept. of Electr. & Comput. Eng., Iowa Univ., Iowa City, IA, USA
Steven C DeJong - Dept. of Electr. & Comput. Eng., Iowa Univ., Iowa City, IA, USA
Steve M Collins - Dept. of Electr. & Comput. Eng., Iowa Univ., Iowa City, IA, USA
Charles R McKay - Dept. of Electr. & Comput. Eng., Iowa Univ., Iowa City, IA, USA
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.14(4), pp.719-732
DOI: 10.1109/42.476113
PMID: 18215876
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers
Language: English
Date published: 12/1995
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
Record Identifier: 9984047853202771

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