One-class acoustic characterization applied to blood detection in IVUS

Sean M. O'Malley, Morteza Naghavi, Ioannis A. Kakadiaris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

Intravascular ultrasound (IVUS) is an invasive imaging modality capable of providing cross-sectional images of the interior of a blood vessel in real time and at normal video framerates (10-30 frames/s). Low contrast between the features of interest in the IVUS imagery remains a confounding factor in IVUS analysis; it would be beneficial therefore to have a method capable of detecting certain physical features imaged under IVUS in an automated manner. We present such a method and apply it to the detection of blood. While blood detection algorithms are not new in this field, we deviate from traditional approaches to IVUS signal characterization in our use of 1-class learning. This eliminates certain problems surrounding the need to provide "foreground" and "background" (or, more generally, n-class) samples to a learner. Applied to the blood-detection problem on 40 MHz recordings made in vivo in swine, we are able to achieve ∼95% sensitivity with ∼90% specificity at a radial resolution of ∼600 μm.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - 10th International Conference, Proceedings
PublisherSpringer-Verlag
Pages202-209
Number of pages8
EditionPART 1
ISBN (Print)9783540757566
DOIs
StatePublished - 2007
Event10th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2007 - Brisbane, Australia
Duration: Oct 29 2007Nov 2 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume4791 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2007
Country/TerritoryAustralia
CityBrisbane
Period10/29/0711/2/07

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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