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Algorithms for cyto...
Algorithms for cytoplasm segmentation of fluorescence labeled cells
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- Wählby, Carolina (author)
- Uppsala universitet,Centrum för bildanalys,Datoriserad bildanalys
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- Lindblad, Joakim (author)
- Uppsala universitet,Centrum för bildanalys,Datoriserad bildanalys
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Vondrus, Mikael (author)
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Bengtsson, Ewert (author)
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Björkesten, Lennart (author)
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(creator_code:org_t)
- 2002
- 2002
- English.
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In: Analytical Cellular Pathology. - 0921-8912 .- 1878-3651. ; 24:2-3, s. 101-111
- Related links:
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https://urn.kb.se/re...
Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Biologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences (hsv//eng)
Keyword
- Biology
- Biologi
Publication and Content Type
- ref (subject category)
- art (subject category)
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