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Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-27873

Zeder, M; Kohler, E; Pernthaler, J (2010). Automated quality assessment of autonomously acquired microscopic images of fluorescently stained bacteria. Cytometry Part A, 77(1):76-85.

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Abstract

Quality assessment of autonomously acquired microscopic images is an important issue in high-throughput imaging systems. For example, the presence of low quality images (≥ 10%) in a dataset significantly influences the counting precision of fluorescently stained bacterial cells. We present an approach based on an artificial neural network (ANN) to assess the quality of such images. Spatially invariant estimators were extracted as ANN input data from subdivided images by low level image processing. Different ANN designs were compared and > 400 ANNs were trained and tested on a set of 25000 manually classified images. The optimal ANN featured a correct identification rate of 94% (3% false positives, 3% false negatives) and could process about 10 images per second. We compared its performance with the image quality assessment by different humans and discuss the difficulties in assigning images to the correct quality class. The computer program and the documented source code (VB.NET) are provided under General Public Licence.

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6 citations in Web of Science®
4 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Plant Biology
DDC:580 Plants (Botany)
Language:English
Date:2010
Deposited On:24 Jan 2010 11:54
Last Modified:28 Nov 2013 00:28
Publisher:Wiley-Blackwell
ISSN:0196-4763
Additional Information:The attached file is a preprint (accepted version) of an article published in Cytometry A. 2010 Jan;77(1):76-85. The definitive version is available at www3.interscience.wiley.com
Publisher DOI:10.1002/cyto.a.20810
PubMed ID:19821518

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