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Recent advances in cell classification for cancer research and drug discovery
University of Canberra, Canberra, ACT, Australia .
Discipline of Information Technology, James Cook University, Australia.ORCID iD: 0000-0002-4255-5130
2009 (English)In: Computational Biology: Issues and Applications in Oncology / [ed] Tuan Pham, Springer-Verlag New York, 2009, 205-226 p.Chapter in book (Refereed)Text
Abstract [en]

Drug effects on cancer cells are investigated through measuring cell cycle progression in individual cells as a function of time. This investigation requires the processing and analysis of huge amounts of image data obtained in time-lapse microscopy. Manual image analysis is very time consuming thus costly, potentially inaccurate, and poorly reproducible. Stages of an automated cellular imaging analysis consist of segmentation, feature extraction, classification, and tracking of individual cells in a dynamic cellular population. The feature extraction and classification of cell phases are considered the most difficult tasks of such analysis. We review several techniques for feature extraction and classification. We then present our work on an automated feature weighting technique for feature selection and combine this technique with cellular phase modeling techniques for classification. These combined techniques perform the two most difficult tasks at the same time and enhance the classification performance. Experimental results have shown that the combined techniques are effective and have potential for higher performance.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2009. 205-226 p.
Series
, Applied Bioinformatics and Biostatistics in Cancer Research
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-125022DOI: 10.1007/978-1-4419-0811-7_9ISBN: 978-1-4419-0810-0 (Print)ISBN: 978-1-4419-0811-7 (Onlie)OAI: oai:DiVA.org:liu-125022DiVA: diva2:902773
Available from: 2016-02-12 Created: 2016-02-12 Last updated: 2016-02-23Bibliographically approved

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Pham, Tuan D
Computer Vision and Robotics (Autonomous Systems)

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