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Combining intensity, edge, and shape information for 2D and 3D segmentation of cell nuclei in tissue sections
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
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2004 (English)In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 215, no 1, 67-76 p.Article in journal (Refereed) Published
Abstract [en]

We present a region-based segmentation method in which seeds representing both object and background pixels are created by combining morphological filtering of both the original image and the gradient magnitude of the image. The seeds are then used as starting points for watershed segmentation of the gradient magnitude image. The fully automatic seeding is done in a generous fashion, so that at least one seed will be set in each foreground object. If more than one seed is placed in a single object, the watershed segmentation will lead to an initial over-segmentation, i.e. a boundary is created where there is no strong edge. Thus, the result of the initial segmentation is further refined by merging based on the gradient magnitude along the boundary separating neighbouring objects. This step also makes it easy to remove objects with poor contrast. As a final step, clusters of nuclei are separated, based on the shape of the cluster. The number of input parameters to the full segmentation procedure is only five. These parameters can be set manually using a test image and thereafter be used on a large number of images created under similar imaging conditions. This automated system was verified by comparison with manual counts from the same image fields. About 90% correct segmentation was achieved for two- as well as three-dimensional images.

Place, publisher, year, edition, pages
2004. Vol. 215, no 1, 67-76 p.
Keyword [en]
Automation/methods, Cell Nucleus/*ultrastructure, Cervix Neoplasms/*pathology/ultrastructure, Female, Humans, Image Processing; Computer-Assisted/*methods, Microscopy; Fluorescence/methods, Sensitivity and Specificity
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:uu:diva-90925DOI: 10.1111/j.0022-2720.2004.01338.xPubMedID: 15230877OAI: oai:DiVA.org:uu-90925DiVA: diva2:163445
Available from: 2003-10-09 Created: 2003-10-09 Last updated: 2017-12-14Bibliographically approved
In thesis
1. Algorithms for Applied Digital Image Cytometry
Open this publication in new window or tab >>Algorithms for Applied Digital Image Cytometry
2003 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Image analysis can provide genetic as well as protein level information from fluorescence stained fixed or living cells without loosing tissue morphology. Analysis of spatial, spectral, and temporal distribution of fluorescence can reveal important information on the single cell level. This is in contrast to most other methods for cell analysis, which do not account for inter-cellular variation. Flow cytometry enables single-cell analysis, but tissue morphology is lost in the process, and temporal events cannot be observed.

The need for reproducibility, speed and accuracy calls for computerized methods for cell image analysis, i.e., digital image cytometry, which is the topic of this thesis.

Algorithms for cell-based screening are presented and applied to evaluate the effect of insulin on translocation events in single cells. This type of algorithms could be the basis for high-throughput drug screening systems, and have been developed in close cooperation with biomedical industry.

Image based studies of cell cycle proteins in cultured cells and tissue sections show that cyclin A has a well preserved expression pattern while the expression pattern of cyclin E is disturbed in tumors. The results indicate that analysis of cyclin E expression provides additional valuable information for cancer prognosis, not visible by standard tumor grading techniques.

Complex chains of events and interactions can be visualized by simultaneous staining of different proteins involved in a process. A combination of image analysis and staining procedures that allow sequential staining and visualization of large numbers of different antigens in single cells is presented. Preliminary results show that at least six different antigens can be stained in the same set of cells.

All image cytometry requires robust segmentation techniques. Clustered objects, background variation, as well as internal intensity variations complicate the segmentation of cells in tissue. Algorithms for segmentation of 2D and 3D images of cell nuclei in tissue by combining intensity, shape, and gradient information are presented.

The algorithms and applications presented show that fast, robust, and automatic digital image cytometry can increase the throughput and power of image based single cell analysis.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2003. 73 p.
Series
Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1104-232X ; 896
Keyword
Bildanalys, Bildanalys
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:uu:diva-3608 (URN)91-554-5759-2 (ISBN)
Public defence
2003-10-31, Häggsalen (room 10132), Ångström Laboratory, Uppsala, 10:15
Opponent
Supervisors
Available from: 2003-10-09 Created: 2003-10-09 Last updated: 2016-01-18Bibliographically approved

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Wählby, CarolinaSintorn, Ida-MariaBorgefors, GunillaBengtsson, Ewert

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