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Proceedings Paper

Investigation of autofocus algorithms for brightfield microscopy of unstained cells
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Paper Abstract

In the past decade there has been significant interest in image processing for brightfield cell microscopy. Much of the previous research on image processing for microscopy has focused on fluorescence microscopy, including cell counting, cell tracking, cell segmentation and autofocusing. Fluorescence microscopy provides functional image information that involves the use of labels in the form of chemical stains or dyes. For some applications, where the biochemical integrity of the cell is required to remain unchanged so that sensitive chemical testing can later be applied, it is necessary to avoid staining. For this reason the challenge of processing images of unstained cells has become a topic of increasing attention. These cells are often effectively transparent and appear to have a homogenous intensity profile when they are in focus. Bright field microscopy is the most universally available and most widely used form of optical microscopy and for this reason we are interested in investigating image processing of unstained cells recorded using a standard bright field microscope. In this paper we investigate the application of a range of different autofocus metrics applied to unstained bladder cancer cell lines using a standard inverted bright field microscope with microscope objectives that have high magnification and numerical aperture. We present a number of conclusions on the optimum metrics and the manner in which they should be applied for this application.

Paper Details

Date Published: 1 May 2014
PDF: 12 pages
Proc. SPIE 9131, Optical Modelling and Design III, 91310T (1 May 2014); doi: 10.1117/12.2051944
Show Author Affiliations
Shu Yu Wu, National Univ. of Ireland, Maynooth (Ireland)
Nazim Dugan, National Univ. of Ireland, Maynooth (Ireland)
Bryan M. Hennelly, National Univ. of Ireland, Maynooth (Ireland)

Published in SPIE Proceedings Vol. 9131:
Optical Modelling and Design III
Frank Wyrowski; John T. Sheridan; Jani Tervo; Youri Meuret, Editor(s)

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