Automated disease identification using computational 3D optical sensing and imaging systems

Arun Anand; Inkyu Moon; Eriko Watanabe; Adrian Stern; Bahram Javidi

doi:10.1364/COSI.2016.CM4D.1

Automated disease identification using computational 3D optical sensing and imaging systems

Arun Anand, Inkyu Moon, Eriko Watanabe, Adrian Stern, Bahram Javidi

Department of Robotics and Mechatronics Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this invited paper, we present an overview of our reported work on 3D sensing and imaging applied to automated cell identification. The sensing and imaging methods include digital holographic imaging, interferometric systems, and integral imaging. We show that 3D sensing and imaging approaches combined with appropriate pattern recognition algorithms provide an impressive approach for automated cell identification with compact field portable systems.

Original language	English
Title of host publication	Computational Optical Sensing and Imaging, COSI 2016
Publisher	Optica Publishing Group (formerly OSA)
ISBN (Print)	9781943580156
DOIs	https://doi.org/10.1364/COSI.2016.CM4D.1
State	Published - 18 Jul 2016
Event	Computational Optical Sensing and Imaging, COSI 2016 - Heidelberg, Germany Duration: 25 Jul 2016 → 28 Jul 2016

Publication series

Name	Optics InfoBase Conference Papers
ISSN (Electronic)	2162-2701

Conference

Conference	Computational Optical Sensing and Imaging, COSI 2016
Country/Territory	Germany
City	Heidelberg
Period	25/07/16 → 28/07/16

Bibliographical note

Publisher Copyright:
© OSA 2016.

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10.1364/COSI.2016.CM4D.1

Cite this

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title = "Automated disease identification using computational 3D optical sensing and imaging systems",

abstract = "In this invited paper, we present an overview of our reported work on 3D sensing and imaging applied to automated cell identification. The sensing and imaging methods include digital holographic imaging, interferometric systems, and integral imaging. We show that 3D sensing and imaging approaches combined with appropriate pattern recognition algorithms provide an impressive approach for automated cell identification with compact field portable systems.",

author = "Arun Anand and Inkyu Moon and Eriko Watanabe and Adrian Stern and Bahram Javidi",

note = "Publisher Copyright: {\textcopyright} OSA 2016.; Computational Optical Sensing and Imaging, COSI 2016 ; Conference date: 25-07-2016 Through 28-07-2016",

year = "2016",

month = jul,

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doi = "10.1364/COSI.2016.CM4D.1",

language = "English",

isbn = "9781943580156",

series = "Optics InfoBase Conference Papers",

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Anand, A, Moon, I, Watanabe, E, Stern, A & Javidi, B 2016, Automated disease identification using computational 3D optical sensing and imaging systems. in Computational Optical Sensing and Imaging, COSI 2016. Optics InfoBase Conference Papers, Optica Publishing Group (formerly OSA), Computational Optical Sensing and Imaging, COSI 2016, Heidelberg, Germany, 25/07/16. https://doi.org/10.1364/COSI.2016.CM4D.1

Automated disease identification using computational 3D optical sensing and imaging systems. / Anand, Arun; Moon, Inkyu; Watanabe, Eriko et al.
Computational Optical Sensing and Imaging, COSI 2016. Optica Publishing Group (formerly OSA), 2016. (Optics InfoBase Conference Papers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Automated disease identification using computational 3D optical sensing and imaging systems

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AU - Moon, Inkyu

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AU - Stern, Adrian

AU - Javidi, Bahram

PY - 2016/7/18

Y1 - 2016/7/18

N2 - In this invited paper, we present an overview of our reported work on 3D sensing and imaging applied to automated cell identification. The sensing and imaging methods include digital holographic imaging, interferometric systems, and integral imaging. We show that 3D sensing and imaging approaches combined with appropriate pattern recognition algorithms provide an impressive approach for automated cell identification with compact field portable systems.

AB - In this invited paper, we present an overview of our reported work on 3D sensing and imaging applied to automated cell identification. The sensing and imaging methods include digital holographic imaging, interferometric systems, and integral imaging. We show that 3D sensing and imaging approaches combined with appropriate pattern recognition algorithms provide an impressive approach for automated cell identification with compact field portable systems.

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DO - 10.1364/COSI.2016.CM4D.1

M3 - Conference contribution

AN - SCOPUS:85165739260

SN - 9781943580156

T3 - Optics InfoBase Conference Papers

BT - Computational Optical Sensing and Imaging, COSI 2016

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ER -

Automated disease identification using computational 3D optical sensing and imaging systems

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