NDLI: Near real-time plaque segmentation of IVUS

Content Provider	IEEE Xplore Digital Library
Author	Pujol, O. Rotger, D. Radeva, P. Rodriguez, O. Mauri, J.
Copyright Year	2003
Description	Author affiliation: Comput. Vision Center, Univ. Autonoma de Barcelona, Spain (Pujol, O.; Rotger, D.; Radeva, P.)
Abstract	This article is devoted to the creation of a near-real time framework to discriminate between tissue and blood. We perform a fast supervised learning of local texture patterns of the plaque using local binary patterns. A classifier is built by assembling weak classifiers using boosting schemes that allow quick performance and reliability. After that, a deformable model is used to ensure continuity in the segmentation and to fill in the gaps in the classification scheme. Our supervised learning framework has been validated using 450 test images from 15 different patients. The resulting segmentation differs from the physicians segmentation in a mean rate of 0.15 mm. and maximum rate of 0.33 mm. The method benefits from the low time consuming feature extraction, as well as a faster classification scheme reducing 10 times the whole processing time compared to most of the texture based approaches.
Sponsorship	European Soc. of Cardiology Nat. Inst. of Health, USA
Starting Page	69
Ending Page	72
File Size	251908
Page Count	4
File Format	PDF
ISBN	078038170X
ISSN	02766547
DOI	10.1109/CIC.2003.1291092
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-21
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Feature extraction Computer vision Boosting Ultrasonic imaging Image texture analysis Supervised learning Assembly Deformable models Image analysis
Content Type	Text
Resource Type	Article

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