Hybrid image processing technique for the robust identification of unstained cells in bright-field microscope images

Lupica, G., Allinson, Nigel and Botchway, S. W. (2008) Hybrid image processing technique for the robust identification of unstained cells in bright-field microscope images. In: 2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008, 10 - 12 December 2008, Vienna, Austria.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


The initial results of a robust technique for the identification of unstained cells in bright-field microscope images captured with high magnification lenses are presented. This is part of an extensive study on the effects of DNA damage to cell viability induced by multi-photon absorption. The method makes use of a variety of image processing algorithms together with expert knowledge of cell morphology during the cell cycle. Single stem cells are identified by first detecting their cell nucleoli and then clustering into nuclear groups. Experimental results, for a representative sample of images, are compared with ground truth labeling by skilled biologists. Despite the low contrast and high variability in appearance of cells in brightfield microscope images, the reported technique displays a detection rate of over 79 for the correct number of identified cells. The methodology implemented has sufficient accuracy and speed for the development of high- throughput robotic systems. © 2008 IEEE.

Keywords:Brightfield, Cell cycle, Cell morphology, Cell viability, Detection rates, DNA damages, Expert knowledge, Ground truth, High variability, High-throughput, Hybrid image, Image processing algorithm, Low contrast, Microscope images, Multi-photon absorption, Representative sample, Robotic systems, Robust identification, Robust technique, Stem cell, Artificial intelligence, Identification (control systems), Image processing, Imaging systems, Microscopes, Multiphoton processes, Nucleic acids, Cells
Subjects:H Engineering > H671 Robotics
G Mathematical and Computer Sciences > G400 Computer Science
C Biological Sciences > C130 Cell Biology
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:8551
Deposited On:05 Apr 2013 09:57

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