A novel image feature for nuclear-phase classification in high content screening

Pham, Tuan D., and Zhou, Xiaobo (2007) A novel image feature for nuclear-phase classification in high content screening. In: Proceedings of the International Conference on Mass Data Analysis of Images and Signals in Medicine, Biotechnology and Chemistry (4826), pp. 84-93. From: International Conference on Mass Data Analysis of Signals and Images in Medicine, Biotechnology and Chemistry, 18 July 2007, Leipzig, Germany.

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Abstract

Cellular imaging is an exciting area of research in computational life sciences, which provides an essential tool for the study of diseases at the cellular level. In particular, to faciliate the usefulness of cellular imaging for high content screening, image analysis and classification need to be automated. In fact the task of image classification is an important component for any computerized imaging system which aims to automate the screening of high-content, high-throughput fluorescent images of mitotic cells. It can help biomedical and biological researchers to speed up the analysis of mitotic data at dynamic ranges for various applications including the study of the complexity of cell processes, and the screening of novel anti-mitotic drugs as potential cancer therapeutic agents. We propose in this paper a novel image feature based on a spatial linear predictive model. This type of image feature can be effectively used for vector-quantization based classification of nuclear phases. We used a dataset of HeLa cells line to evaluate and compare the proposed method on the classification of nuclear phases. Experimental results obtained from the new feature are found to be superior to some recently published results using the same dataset.

Item ID: 3175
Item Type: Conference Item (Refereed Research Paper - E1)
Keywords: image analysis; patterern recognition; bioinformatics; feature extraction; microscopic imaging; cellular classification; high content screening
Additional Information:

ISBN: 978-3-540-76299-7
ISSN: 1611-3349
Date Deposited: 10 Nov 2009 04:48
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 50%
08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing @ 50%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890205 Information Processing Services (incl. Data Entry and Capture) @ 100%
Citation Count from Web of Science Web of Science 2
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