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Authors: Hazem Wannous 1 ; Yves Lucas 2 and Sylvie Treuillet 2

Affiliations: 1 University of Bordeaux, France ; 2 University of Orleans, France

ISBN: 978-989-674-028-3

Keyword(s): Tissue classification, 3D modeling, Machine learning, Wound assessment.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Feature Extraction ; Features Extraction ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image and Video Analysis ; Image Formation and Preprocessing ; Informatics in Control, Automation and Robotics ; Methodologies and Methods ; Multi-View Geometry ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing, Sensors, Systems Modeling and Control ; Soft Computing ; Surface Geometry and Shape

Abstract: From colour images acquired with a hand held digital camera, an innovative tool for assessing chronic wounds has been developed. It combines both types of assessment, colour analysis and dimensional measurement of injured tissues in a user-friendly system. Colour and texture descriptors have been extracted and selected from a sample database of wound tissues, before the learning stage of a support vector machine classifier with perceptron kernel on four categories of tissues. Relying on a triangulated 3D model captured using uncalibrated vision techniques applied on a stereoscopic image pair, a fusion algorithm elaborates new tissue labels on each model triangle from each view. The results of 2D classification are merged and directly mapped on the mesh surface of the 3D wound model. The result is a significative improvement in the robustness of the classification. Real tissue areas can be computed by retro projection of identified regions on the 3D model.

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Paper citation in several formats:
Wannous H.; Lucas Y.; Treuillet S. and (2010). COMBINED MACHINE LEARNING WITH MULTI-VIEW MODELING FOR ROBUST WOUND TISSUE ASSESSMENT.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-028-3, pages 98-104. DOI: 10.5220/0002833300980104

@conference{visapp10,
author={Hazem Wannous and Yves Lucas and Sylvie Treuillet},
title={COMBINED MACHINE LEARNING WITH MULTI-VIEW MODELING FOR ROBUST WOUND TISSUE ASSESSMENT},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={98-104},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002833300980104},
isbn={978-989-674-028-3},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2010)
TI - COMBINED MACHINE LEARNING WITH MULTI-VIEW MODELING FOR ROBUST WOUND TISSUE ASSESSMENT
SN - 978-989-674-028-3
AU - Wannous, H.
AU - Lucas, Y.
AU - Treuillet, S.
PY - 2010
SP - 98
EP - 104
DO - 10.5220/0002833300980104

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