A detailed analysis of cyclin A accumulation at the G1/S border in normal and transformed cells

Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented....

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Bibliographic Details
Published in:Experimental cell research 2000, Vol.256, p.86
Main Authors: Erlandsson, Fredrik, Wählby (nee Linnman), Carolina, Ekholm, Susanna, Bengtsson, Ewert, Zetterberg, Anders
Format: Article
Language:English
Subjects:
Bildanalys
Image analysis
Information technology
Informationsteknik
TECHNOLOGY
TEKNIKVETENSKAP
Online Access:Get full text
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Summary:Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre-processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of correctly segmented single cells can be further processed by a splitting step. By statistical analysis we therefore get a feedback system for separation of clustered cells. After the segmentation is completed, the quality of the final segmentation is evaluated. By training the algorithm on a representative set of training images, the algorithm is made fully automatic for subsequent images created under similar conditions. Automatic cytoplasm segmentation was tested on CHO-cells stained with calcein. The fully automatic method showed between 89% and 97% correct segmentation as compared to manual segmentation.
ISSN:0014-4827