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Fuzzy information fusion of classification models for high-throughput image screening of cancer cells in time-lapse microscopy
Bioinformatics Applications Research Centre and Information Technology Discipline, School of Mathematics, Physics, and Information Technology, James Cook University, Townsville, Australia.ORCID iD: 0000-0002-4255-5130
School of Information Sciences and Engineering, University of Canberra, ACT 2601, Australia .
HCNR-Center for Bioinformatics and Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02215, USA .
2007 (English)In: Journal of Knowledge-based & Intelligent Engineering Systems, ISSN 1327-2314, E-ISSN 1875-8827, International Journal of Knowledge-based and Intelligent Engineering Systems, Vol. 11, no 4, p. 237-246Article in journal (Refereed) Published
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Abstract [en]

Bioimaging at molecular and cellular levels requires specific image analysis methods to help life scientists develop methodologies and hypotheses in biology and biomedicine. In particular, this is true when dealing with microscopic images of cells and vessels. To facilitate the automation of cell screening, we have developed methods based on vector quantization and Markov model for classification of cellular phases using time-lapse fluorescence microscopic image sequences. Because of ambiguity inherently existing in the labeling of cell-phase feature vectors, we proposed to use relaxation labeling technique to reduce uncertainty among cell-phase models having overlapping properties. To further improve the classification rate we applied a fuzzy fusion strategy for combining individual results obtained from multiple classifiers. Our proposed image-classification methods can be useful for the task of high-content cell-cycle screening which is essential for biomedical research in the study of structures and functions of cells and molecules.

Place, publisher, year, edition, pages
IOS Press, 2007. Vol. 11, no 4, p. 237-246
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Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
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URN: urn:nbn:se:liu:diva-125015Scopus ID: 2-s2.0-85013572478OAI: oai:DiVA.org:liu-125015DiVA, id: diva2:902783
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2017-12-08Bibliographically approved

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Pham, Tuan D

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