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.
|Title of host publication||Computational Biology - Issues and Applications in Oncology|
|Place of Publication||New York|
|Number of pages||22|
|Publication status||Published - 2009|
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