Biological cells classification using bio-inspired descriptor in a boosting k-NN framework

Wafa Bel Haj Ali 1 Paolo Piro 2 Dario Giampaglia 1 Thierry Pourcher 3 Michel Barlaud 1
1 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Projet MEDIACODING
SIS - Signal, Images et Systèmes
2 IIT - Istituto Italiano di Tecnologia
3 TIRO - UMR E93 - Transporteurs en Imagerie et Radiothérapie en Oncologie
Abstract : High-content imaging is an emerging technology for the analysis and quantification of biological phenomena. Thus, classifying a huge number of cells or quantifying markers from large sets of images by experts is a very time-consuming and poorly reproducible task. In order to overcome such limitations, we propose a supervised method for automatic cell classification. Our approach consists of two steps: the first one is an indexing stage based on specific bio-inspired features relying on the distribution of contrast information on segmented cells. The second one is a supervised learning stage that selects the prototypical samples best representing the cell categories. These prototypes 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 pathologies. In order to evaluate the automatic classification performances, we tested our algorithm on the HEp2 Cells dataset of (Foggia et al, CBMS 2010). Results are very promising, showing classification precision larger than 96% on average, thus suggesting our method as a valuable decision-support tool in such cellular imaging applications.
Keywords : cellular biophysics feature extraction image classification image segmentation indexing learning (artificial intelligence) medical image processing
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Communication dans un congrès
CBMS - 25th International Symposium on Computer-Based Medical Systems, Jun 2012, Rome, Italy. IEEE, pp.1-6, 2012, <10.1109/CBMS.2012.6266359>
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Wafa Bel Haj Ali, Paolo Piro, Dario Giampaglia, Thierry Pourcher, Michel Barlaud. Biological cells classification using bio-inspired descriptor in a boosting k-NN framework. CBMS - 25th International Symposium on Computer-Based Medical Systems, Jun 2012, Rome, Italy. IEEE, pp.1-6, 2012, <10.1109/CBMS.2012.6266359>. <hal-00958860>

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