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A detailed analysis of cyclin A accumulation at the G1/S border in normal and transformed cells.
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
Interfaculty Units, Centre for Image Analysis. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
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2000 (English)In: Experimental Cell Research, ISSN 0014-4827/00, Vol. 256, 86-95 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2000. Vol. 256, 86-95 p.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:uu:diva-42655OAI: oai:DiVA.org:uu-42655DiVA: diva2:70557
Available from: 2008-01-07 Created: 2008-01-07 Last updated: 2017-02-08

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http://dx.doi.org/10.1006/excr.2000.4889

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Erlandsson, FredrikBengtsson, Ewert

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