Anzeige
Erschienen in:
2007 | OriginalPaper | Buchkapitel
One-Class Acoustic Characterization Applied to Blood Detection in IVUS
verfasst von : Sean M. O’Malley, Morteza Naghavi, Ioannis A. Kakadiaris
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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
μ
m.