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.
2000. Vol. 256, 86-95 p.