Recent Advances in Cell Classification for Cancer Research and Drug Discovery

Dat Tran, T Pham

Research output: A Conference proceeding or a Chapter in BookChapter

Abstract

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.
Original languageEnglish
Title of host publicationComputational Biology - Issues and Applications in Oncology
EditorsTuan Pham
Place of PublicationNew York
PublisherSpringer
Chapter9
Pages205-226
Number of pages22
Edition1st
ISBN (Print)9781441908100
DOIs
Publication statusPublished - 2009

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    Tran, D., & Pham, T. (2009). Recent Advances in Cell Classification for Cancer Research and Drug Discovery. In T. Pham (Ed.), Computational Biology - Issues and Applications in Oncology (1st ed., pp. 205-226). Springer. https://doi.org/10.1007/978-1-4419-0811-7_9