Algorithms for cytoplasm segmentation of fluorescence labeled cells
2002 (English)In: Analytical Cellular Pathology, ISSN 0921-8912, Vol. 24, no 2-3, 101-111 p.Article in journal (Refereed) Published
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
2002. Vol. 24, no 2-3, 101-111 p.
IdentifiersURN: urn:nbn:se:uu:diva-90086PubMedID: 12446959OAI: oai:DiVA.org:uu-90086DiVA: diva2:162264