This paper presents a new method to separate cells on microscopic surfaces joined together in cell clusters into individual cells. Important features of this method are that the remaining object geometry is preserved and few contour points are required for finding joints between neighboring cells. There are alternative methods such as morphological operations or the watershed transformation based on the inverse distance transformation but they have certain disadvantages compared to the method presented in this paper. The discussed method contains knowledge-based components in form of a decision function and exchangeable rules to avoid unwanted separations.

This paper presents a method of separating cells that are connected to each other forming clusters. The difference to many other publications covering similar topics is that the cell types we are dealing with form clusters of highly varying morphology. An advantage of our method is that it can be universally used for different cell types. The segmentation method is based on a growth simulation starting from the nuclei areas. To start the evaluation, the cells need to be made visible with a histological stain, in our case with the May-Grünwald solution. After the staining process has been completed, the nuclei areas can be distinguished from the other cell areas by a histogram backprojection algorithm. The presented method can, in addition to histological stained cells, also be applied to fluorescent-stained cells.