A novel 3D segmentation method of the lumen from intravascular ultrasound images

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DOIResolve DOI: http://doi.org/10.1007/978-3-540-74260-9_84
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TypeBook Chapter
Proceedings titleImage Analysis and Recognition : 4th International Conference, ICIAR 2007, Montreal, Canada, August 22-24, 2007. Proceedings
Series titleLecture Notes In Computer Science; Volume 4633
Conference4th International Conference on Image Analysis and Recognition (ICIAR 2007), August 22-24, 2007, Montreal, Quebec
SubjectIVUS segmentation; 3D co-occurrence matrix; k-means classification; swap heuristics; texture analysis
AbstractIn this paper a novel method that automatically detects the lumen-intima border on an intravascular ultrasound sequence (IVUS) is presented. First, a 3D co-occurrence matrix was used to efficiently extract the texture information of the IVUS images through the temporal sequence. By extracting several co-occurrence matrices a complete characterization feature space was determined. Secondly, using a k-means algorithm, all the pixels in the IVUS images were classified by determining if they belong to either the lumen or the other vessel tissues. This enables automatic clustering and therefore no learning step was required. The classification of the pixels within the feature space was obtained using 3 clusters: two clusters for the vessel tissues, one cluster for the lumen, while the remaining pixels are labeled as unclassified. Experimental results show that the proposed method is robust to noisy images and yields segmented lumen-intima contours validated by an expert in more than 80% of a total of 300 IVUS images.
Publication date
PublisherSpringer Berlin Heidelberg
AffiliationNational Research Council Canada (NRC-CNRC); NRC Industrial Materials Institute
Peer reviewedYes
NRC number53675
NPARC number15854986
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Record identifier4e97d7d4-f1db-47bd-88d4-d599acaaf55a
Record created2010-07-23
Record modified2016-06-17
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