Algorithms for Cytoplasm Segmentation of Fluorescence Labelled 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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2002-01-01
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| Series: | Analytical Cellular Pathology |
| Online Access: | http://dx.doi.org/10.1155/2002/821782 |
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| _version_ | 1832563305126821888 |
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| author | Carolina Wählby Joakim Lindblad Mikael Vondrus Ewert Bengtsson Lennart Björkesten |
| author_facet | Carolina Wählby Joakim Lindblad Mikael Vondrus Ewert Bengtsson Lennart Björkesten |
| author_sort | Carolina Wählby |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9bf150e88f3440978c54d49fde2ed10f |
| institution | Kabale University |
| issn | 0921-8912 1878-3651 |
| language | English |
| publishDate | 2002-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Analytical Cellular Pathology |
| spelling | doaj-art-9bf150e88f3440978c54d49fde2ed10f2025-02-03T01:20:34ZengWileyAnalytical Cellular Pathology0921-89121878-36512002-01-01242-310111110.1155/2002/821782Algorithms for Cytoplasm Segmentation of Fluorescence Labelled CellsCarolina Wählby0Joakim Lindblad1Mikael Vondrus2Ewert Bengtsson3Lennart Björkesten4Centre for Image Analysis at Uppsala University, Uppsala, SwedenCentre for Image Analysis at Uppsala University, Uppsala, SwedenCentre for Image Analysis at Uppsala University, Uppsala, SwedenCentre for Image Analysis at Uppsala University, Uppsala, SwedenAmersham Pharmacia Biotech, Uppsala, SwedenAutomatic 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.http://dx.doi.org/10.1155/2002/821782 |
| spellingShingle | Carolina Wählby Joakim Lindblad Mikael Vondrus Ewert Bengtsson Lennart Björkesten Algorithms for Cytoplasm Segmentation of Fluorescence Labelled Cells Analytical Cellular Pathology |
| title | Algorithms for Cytoplasm Segmentation of Fluorescence Labelled Cells |
| title_full | Algorithms for Cytoplasm Segmentation of Fluorescence Labelled Cells |
| title_fullStr | Algorithms for Cytoplasm Segmentation of Fluorescence Labelled Cells |
| title_full_unstemmed | Algorithms for Cytoplasm Segmentation of Fluorescence Labelled Cells |
| title_short | Algorithms for Cytoplasm Segmentation of Fluorescence Labelled Cells |
| title_sort | algorithms for cytoplasm segmentation of fluorescence labelled cells |
| url | http://dx.doi.org/10.1155/2002/821782 |
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