INFERENTIAL MINING FOR RECONSTRUCTION OF 3D CELL STRUCTURES IN ATOMIC FORCE MICROSCOPY IMAGING

Mario D; Stefano Berrettini; Serena Danti; Michele Lisanti; Mario Petrini; Andrea Pietrabissa; Ovidio Salvetti

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Paper

INFERENTIAL MINING FOR RECONSTRUCTION OF 3D CELL STRUCTURES IN ATOMIC FORCE MICROSCOPY IMAGING

Topics: Concept Mining; Pre-Processing and Post-Processing for Data Mining; Visual Data Mining and Data Visualization

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 0IC3K, 340-345, 2011 , Paris, France

Authors: Mario D'Acunto ¹ ; Stefano Berrettini ² ; Serena Danti ² ; Michele Lisanti ² ; Mario Petrini ² ; Andrea Pietrabissa ³ and Ovidio Salvetti ¹

Affiliations: ¹ ISTI-CNR, Italy ; ² University of Pisa, Italy ; ³ University of Pavia, Italy

Keyword(s): Atomic Force Microscopy Imaging, 3D Cell Reconstruction, Bayesian single frame high-resolution.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Concept Mining ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Pre-Processing and Post-Processing for Data Mining ; Symbolic Systems ; Visual Data Mining and Data Visualization

Abstract: Atomic Force Microscopy (AFM) is a fundamental tool for the investigation of a wide range of mechanical properties on nanoscale due to the contact interaction between the AFM tip and the sample surface. The focus of this paper is on an algorithm for the reconstruction of 3D stem-differentiated cell structures extracted by typical 2D surface AFM images. The AFM images resolution is limited by the tip-sample convolution due to the combined geometry of the probe tip and the pattern configuration of the sample. This limited resolution limits the accuracy of the correspondent 3D image. To drop unwanted effects, we adopt an inferential method for pre-processing single frame AFM image (low resolution image) building its super-resolution version. Therefore the 3D reconstruction is made on animal cells using a Markov Random Field approach for augmented voxels. The 3D reconstruction should improve unambiguous identification of cells structures. The computation method is fast and can be applied both to multi- and to single-frame images. (More)

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Paper citation in several formats:

D'Acunto, M.; Berrettini, S.; Danti, S.; Lisanti, M.; Petrini, M.; Pietrabissa, A. and Salvetti, O. (2011). INFERENTIAL MINING FOR RECONSTRUCTION OF 3D CELL STRUCTURES IN ATOMIC FORCE MICROSCOPY IMAGING. In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2011) - KDIR; ISBN 978-989-8425-79-9; ISSN 2184-3228, SciTePress, pages 340-345. DOI: 10.5220/0003685503480353

@conference{kdir11,
author={Mario D'Acunto. and Stefano Berrettini. and Serena Danti. and Michele Lisanti. and Mario Petrini. and Andrea Pietrabissa. and Ovidio Salvetti.},
title={INFERENTIAL MINING FOR RECONSTRUCTION OF 3D CELL STRUCTURES IN ATOMIC FORCE MICROSCOPY IMAGING},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2011) - KDIR},
year={2011},
pages={340-345},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003685503480353},
isbn={978-989-8425-79-9},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2011) - KDIR
TI - INFERENTIAL MINING FOR RECONSTRUCTION OF 3D CELL STRUCTURES IN ATOMIC FORCE MICROSCOPY IMAGING
SN - 978-989-8425-79-9
IS - 2184-3228
AU - D'Acunto, M.
AU - Berrettini, S.
AU - Danti, S.
AU - Lisanti, M.
AU - Petrini, M.
AU - Pietrabissa, A.
AU - Salvetti, O.
PY - 2011
SP - 340
EP - 345
DO - 10.5220/0003685503480353
PB - SciTePress