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Classifying Human Blood Samples Using Characteristics of Single Molecules and Cell Structures on Microscopy Images

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Computer Aided Systems Theory – EUROCAST 2015 (EUROCAST 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9520))

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Abstract

In this paper we present a method for the definition of characteristics of single molecules as well as of cell structures on fluorescence microscopy images for classifying human disease states. Fluorescence microscopy is one of the most emerging fields in modern laboratory diagnostics and is used in various research areas, for instance in studies of protein-protein interactions, analyses of cell interactions, diagnostics, or drug distribution studies. We have developed a new combinatory workflow comprising image processing and machine learning techniques to define characteristics out of given fluorescence microscopy images and to classify given images of blood samples according to their level of protein expression (high or low), i.e. according to their disease state. This combinatory workflow is not adapted to a specific illness but is usable for all kinds of diseases that can be characterized using single molecule fluorescence microscopy.

The work described in this paper has been done within the FFG FIT-IT project NanoDetect: A Bioinformatics Image Processing Framework for Automated Analysis of Cellular Macro and Nano Structures (project number 835918) sponsored by the Austrian Research Promotion Agency.

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  1. 1.

    http://dev.heuristiclab.com/.

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Author information

Authors and Affiliations

  1. Bioinformatics Research Group, University of Applied Sciences Upper Austria, Hagenberg Campus, Softwarepark 13, 4232, Hagenberg, Austria

    Daniela Borgmann, Lisa Obritzberger, Susanne Schaller, Viktoria Dorfer & Stephan Winkler

  2. Department of Medical Engineering, University of Applied Sciences Upper Austria, Linz Campus, Garnisonstrasse 21, 4020, Linz, Austria

    Sandra Mayr & Jaroslaw Jacak

  3. Red Cross Blood Transfusion Service of Upper Austria, Krankenhausstrasse 7, 4017, Linz, Austria

    Helene Polin

Authors
  1. Daniela Borgmann
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  2. Sandra Mayr
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  3. Helene Polin
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  4. Lisa Obritzberger
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  5. Susanne Schaller
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  6. Viktoria Dorfer
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  7. Jaroslaw Jacak
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  8. Stephan Winkler
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Corresponding author

Correspondence to Daniela Borgmann .

Editor information

Editors and Affiliations

  1. de Gran Canaria, Universidad de las Palmas de Gran Canari, Las Palmas de Gran Canaria, Spain

    Roberto Moreno-Díaz

  2. Johannes Kepler University Linz, Linz, Austria

    Franz Pichler

  3. de Gran Canaria, Universidad de las Palmas de Gran Canari, Las Palmas de Gran Canaria, Spain

    Alexis Quesada-Arencibia

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Borgmann, D. et al. (2015). Classifying Human Blood Samples Using Characteristics of Single Molecules and Cell Structures on Microscopy Images. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2015. EUROCAST 2015. Lecture Notes in Computer Science(), vol 9520. Springer, Cham. https://doi.org/10.1007/978-3-319-27340-2_39

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  • DOI: https://doi.org/10.1007/978-3-319-27340-2_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27339-6

  • Online ISBN: 978-3-319-27340-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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