Authors:
Paolo Piro
1
;
Wafa Bel Haj Ali
2
;
Lydie Crescence
3
;
Omelkheir Ferhat
3
;
Jacques Darcourt
3
;
Thierry Pourcher
3
and
Michel Barlaud
2
Affiliations:
1
Italian Institute of Technology (IIT), Italy
;
2
University of Nice-Sophia Antipolis, France
;
3
University of Nice-Sophia Antipolis-CAL, France
Keyword(s):
Cell classification, NIS protein, k-NN, boosting.
Related
Ontology
Subjects/Areas/Topics:
Bioinformatics
;
Biomedical Engineering
;
Image Analysis
;
Pattern Recognition, Clustering and Classification
Abstract:
Cellular imaging is an emerging technology for studying many biological phenomena. Cellular image analysis
generally requires to identify and classify cells according to their morphological aspect, staining intensity,
subcellular localization and other parameters. Hence, this task may be very time-consuming and poorly reproducible
when carried out by experimenters. In order to overcome such limitations, we propose an automatic
segmentation and classification software tool that was tested on cellular images acquired for the analysis of
NIS phosphorylation and the identification of NIS-interacting proteins. On the algorithmic side, our method
is based on a novel texture-based descriptor that is highly discriminative in representing the main visual features
at the subcellular level. These descriptors are then used in a supervised learning framework where the
most relevant prototypical samples are used to predict the class of unlabeled cells, using a new methodology
we have recently prop
osed, called UNN, which grounds on the boosting framework. In order to evaluate the
automatic classification performances, we tested our algorithm on a significantly large database of cellular
images annotated by an expert of our group. Results are very promising, providing precision of about 84% on
average, thus suggesting our method as a valuable decision-support tool in such cellular imaging applications.
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