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Authors: Juan-Carlos Prieto 1 ; Chantal Revol-Muller 1 ; Françoise Peyrin 1 ; Patrizia Camelliti 2 and Christophe Odet 1

Affiliations: 1 Univ. de Lyon1 and INSA-Lyon, France ; 2 Imperial College London, United Kingdom

ISBN: 978-989-8565-04-4

Keyword(s): 3D Texture Synthesis, Image-based Modeling, Organ Modeling, Histology, Patient-specific, Image/Mechanical Simulation, Virtual Human.

Related Ontology Subjects/Areas/Topics: Applications ; Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Geometry and Modeling ; Image-Based Modeling ; Medical Image Applications ; Pattern Recognition ; Software Engineering

Abstract: Virtual anatomy models show in detail characteristics of the human body systems. These models are based in surface representation of the structures and lack information from the interior of the object. Creating models that represent the surface, the interior of the object and are able to provide pathological information is the current challenge of research in life sciences. We present a method to synthesize realistic three-dimensional organic tissues starting from bidimensional textured multi-channel samples. The method relies on an energy function that measures the difference between the reference texture and the synthesized object, through a distance metric that compares perpendicular neighborhoods in the object to neighborhoods in the sample. When this function is minimized by IRLS, the result is a solid object that resembles the sample at every slice. In some cases, the optimization might be aided by adding the feature distance transform, calculated from a given binary mask. This allows to code large textured areas. Multiple textures can also be provided to the optimization in order to create anistropic textures. We apply our method starting from various micrometric images such as histology images or slices of Synchrotron Radiation Computed Micro-Tomography (SRìCT) images. A major advantage of our method is to extend 2D histological information to a 3D representation. We demonstrate the accuracy of the generated texture by comparing statistical and morphological parameters computed from the synthetic object with those obtained from the real object underlying the reference images. (More)

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Paper citation in several formats:
Prieto, J.; Prieto, J.; Revol-Muller, C.; Peyrin, F.; Camelliti, P. and Odet, C. (2012). 3D TEXTURE SYNTHESIS FOR MODELING REALISTIC ORGANIC TISSUES.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-04-4, pages 60-65. DOI: 10.5220/0003863800600065

@conference{visapp12,
author={Juan{-}Carlos Prieto. and Juan{-}Carlos Prieto. and Chantal Revol{-}Muller. and Fran\c{C}oise Peyrin. and Patrizia Camelliti. and Christophe Odet.},
title={3D TEXTURE SYNTHESIS FOR MODELING REALISTIC ORGANIC TISSUES},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={60-65},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003863800600065},
isbn={978-989-8565-04-4},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012)
TI - 3D TEXTURE SYNTHESIS FOR MODELING REALISTIC ORGANIC TISSUES
SN - 978-989-8565-04-4
AU - Prieto, J.
AU - Prieto, J.
AU - Revol-Muller, C.
AU - Peyrin, F.
AU - Camelliti, P.
AU - Odet, C.
PY - 2012
SP - 60
EP - 65
DO - 10.5220/0003863800600065

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