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Identification and classification of biological micro-organisms by holographic learning
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Paper Abstract

The identification and classification of biological samples is high-demanded in biomedical imaging for diagnostic purposes. Among all imaging modalities, digital holography has gained credits as a powerful solutions, thanks to its ability to perform full-field and label –free quantitative phase imaging. On the other hand, machine learning is nowadays the most used approach for classification purposes. The robustness and the accuracy of the classification depend of the features used for the training step. Therefore, the identification of micro-organism becomes strictly related to the features that can be extracted from their images. In other word, the more the image contains information, the higher the possibility of extracting highly distinctive descriptors to differentiate biological phenotypes. Digital holography can be considered one of the richest in terms of information content due to the fact that a single digital hologram encode both amplitude and phase information about the imaged cells. This opens the way to improve the features extraction, thus making more accurate the classification step. In this paper we analyze a test case by using a holographic image dataset for classification, by extracting unique features that can be solely obtained by holographic images.

Paper Details

Date Published: 21 June 2019
PDF: 6 pages
Proc. SPIE 11060, Optical Methods for Inspection, Characterization, and Imaging of Biomaterials IV, 110600H (21 June 2019); doi: 10.1117/12.2527484
Show Author Affiliations
Pasquale Memmolo, Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (Italy)
Vittorio Bianco, Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (Italy)
Pierluigi Carcagnì, Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (Italy)
Andouglas Goncalves da Silva Jr., Univ. Federal do Rio Grande do Norte (Brazil)
Luiz Marcos Garcia Goncalves, Univ. Federal do Rio Grande do Norte (Brazil)
Francesco Merola, Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (Italy)
Melania Paturzo, Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (Italy)
Cosimo Distante, Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (Italy)
Pietro Ferraro, Istituto di Scienze Applicate e Sistemi Intelligenti "Eduardo Caianiello" (Italy)


Published in SPIE Proceedings Vol. 11060:
Optical Methods for Inspection, Characterization, and Imaging of Biomaterials IV
Pietro Ferraro; Simonetta Grilli; Monika Ritsch-Marte; Christoph K. Hitzenberger, Editor(s)

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