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Authors: Karla L Caballero 1 ; Joel Barajas 1 ; Oriol Pujol 2 ; Josefina Mauri 3 and Petia Radeva 1

Affiliations: 1 Computer Vision Center, Autonomous University of Barcelona, Spain ; 2 University of Barcelona, Computer Vision Center, Spain ; 3 Hospital Universitari German Trias i Pujol, Spain

ISBN: 978-972-8865-75-7

ISSN: 2184-4321

Keyword(s): Intravascular Ultrasound, RF signals, Image Reconstruction, Tissue Classification, Adaboost, ECOC.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Feature Extraction ; Features Extraction ; Image and Video Analysis ; Image Formation and Preprocessing ; Image Formation, Acquisition Devices and Sensors ; Informatics in Control, Automation and Robotics ; Medical Image Analysis ; Signal Processing, Sensors, Systems Modeling and Control

Abstract: Plaque rupture in coronary vessels is one of the principal causes of sudden death in western societies. Reliable diagnostic tools are of great interest for physicians in order to detect and quantify vulnerable plaque in order to develop an effective treatment. To achieve this, a tissue classification must be performed. Intravascular Ultrasound (IVUS) represents a powerful technique to explore the vessel walls and to observe its morphology and histological properties. In this paper, we propose a method to reconstruct IVUS images from the raw Radio Frequency (RF) data coming from the ultrasound catheter. This framework offers a normalization scheme to compare accurately different patient studies. Then, an automatic tissue classification based on the texture analysis of these images and the use of Adapting Boosting (AdaBoost) learning technique combined with Error Correcting Output Codes (ECOC) is presented. In this study, 9 in-vivo cases are reconstructed with 7 different parameter set. This method improves the classification rate based on images, yielding a 91% of well-detected tissue using the best parameter set. It is also reduced the inter-patient variability compared with the analysis of DICOM images, which are obtained from the commercial equipment. (More)

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Paper citation in several formats:
L Caballero K.; Barajas J.; Pujol O.; Mauri J.; Radeva P. and (2007). RECONSTRUCTING IVUS IMAGES FOR AN ACCURATE TISSUE CLASSIFICATION.In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 3: Computer Vision Methods in Medicine, (VISAPP 2007) ISBN 978-972-8865-75-7, pages 113-119. DOI: 10.5220/0002061001130119

@conference{computer vision methods in medicine07,
author={Karla {L Caballero} and Joel Barajas and Oriol Pujol and Josefina Mauri and Petia Radeva},
title={RECONSTRUCTING IVUS IMAGES FOR AN ACCURATE TISSUE CLASSIFICATION},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 3: Computer Vision Methods in Medicine, (VISAPP 2007)},
year={2007},
pages={113-119},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002061001130119},
isbn={978-972-8865-75-7},
}

TY - CONF

JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 3: Computer Vision Methods in Medicine, (VISAPP 2007)
TI - RECONSTRUCTING IVUS IMAGES FOR AN ACCURATE TISSUE CLASSIFICATION
SN - 978-972-8865-75-7
AU - L Caballero, K.
AU - Barajas, J.
AU - Pujol, O.
AU - Mauri, J.
AU - Radeva, P.
PY - 2007
SP - 113
EP - 119
DO - 10.5220/0002061001130119

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