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Authors: Wafa Bel Haj Ali 1 ; Paolo Piro 2 ; Lydie Crescence 3 ; Dario Giampaglia 1 ; Oumelkheir Ferhat 3 ; Jacques Darcourt 3 ; Thierry Pourcher 3 and Michel Barlaud 1

Affiliations: 1 University of Nice-Sophia Antipolis, France ; 2 Italian Institute of Technology (IIT), Italy ; 3 University of Nice-Sophia Antipolis/CAL, France

ISBN: 978-989-8565-03-7

Keyword(s): Cell Classification, Sodium Iodide Symporter, Bio-inspired, k-NN, Boosting, Machine Learning.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis ; Segmentation and Grouping

Abstract: High-content cellular imaging is an emerging technology for studying many biological phenomena. statistical analyses on large populations (more than thousands) of cells are required. Hence classifying cells by experts is a very time-consuming task and poorly reproducible. In order to overcome such limitations, we propose an automatic supervised classification method. Our new cell classification method consists of two steps: The first one is an indexing process based on specific bio-inspired features using contrast information distributions on cell sub-regions. The second is a supervised learning process to select prototypical samples (that best represent the cells categories) which are used in a leveraged k-NN framework to predict the class of unlabeled cells. In this paper we have tested our new learning algorithm on cellular images acquired for the analysis of changes in the subcellular localization of a membrane protein (the sodium iodide symporter). In order to evaluate the automa tic classification performances, we tested our algorithm on a significantly large database of cellular images annotated by experts of our group. Results in term of Mean Avarage Precision (MAP) are very promising, providing precision upper than 87% on average, thus suggesting our method as a valuable decision-support tool in such cellular imaging applications. Such supervised classification method has many other applications in cell imaging in the areas of research in basic biology and medicine but also in clinical histology. (More)

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Paper citation in several formats:
Bel Haj Ali, W.; Piro, P.; Crescence, L.; Giampaglia, D.; Ferhat, O.; Darcourt, J.; Pourcher, T. and Barlaud, M. (2012). A BIO-INSPIRED LEARNING AND CLASSIFICATION METHOD FOR SUBCELLULAR LOCALIZATION OF A PLASMA MEMBRANE PROTEIN.In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012) ISBN 978-989-8565-03-7, pages 579-584. DOI: 10.5220/0003821905790584

@conference{visapp12,
author={Wafa Bel Haj Ali. and Paolo Piro. and Lydie Crescence. and Dario Giampaglia. and Oumelkheir Ferhat. and Jacques Darcourt. and Thierry Pourcher. and Michel Barlaud.},
title={A BIO-INSPIRED LEARNING AND CLASSIFICATION METHOD FOR SUBCELLULAR LOCALIZATION OF A PLASMA MEMBRANE PROTEIN},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012)},
year={2012},
pages={579-584},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003821905790584},
isbn={978-989-8565-03-7},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2012)
TI - A BIO-INSPIRED LEARNING AND CLASSIFICATION METHOD FOR SUBCELLULAR LOCALIZATION OF A PLASMA MEMBRANE PROTEIN
SN - 978-989-8565-03-7
AU - Bel Haj Ali, W.
AU - Piro, P.
AU - Crescence, L.
AU - Giampaglia, D.
AU - Ferhat, O.
AU - Darcourt, J.
AU - Pourcher, T.
AU - Barlaud, M.
PY - 2012
SP - 579
EP - 584
DO - 10.5220/0003821905790584

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