Context-Based Separation of Cell Clusters for the Automatic Biocompatibility Testing of Implant Materials

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. 1. Introduction In the process of testing implant materials for biocompatibility, it is important to evaluate whether a material is suitable for use in human bodies. An important aspect of biocompatibility is the determination of the exact number of cells which are in contact with the surface of the material being tested. For the specimen preparation process, a suspension with a defined cell concentration reacts for a certain time with the substrate under test and allows the cells to settle on the contact surface. Afterwards, the cells are stained using the May-Grünwald suspension [1] to be easily identifiable amongst each other. Microscopic images (Figure 1) are used to evaluate the results. A major challenge is the separation of single cells in a cluster due to their very variable morphology. The examination of many samples shows that L929 cells often exhibit cell clusters at various positions. This paper is based on providing a method of identifying individual cells within these cell clusters. Figure 1: L929 cells on the substrate steel. Right: a biological cell division process, two joint cells which have a sand glass appearance. Several papers deal with different image processing methods for cell segmentation [2–7]. Depending on the image quality or the dyeing process, different segmentation methods may be the appropriate choice. If, for example, a noisy image has to be analyzed, the use of active contours could be advisable [3, 5–7]. The separation of connected cells is still a great challenge. Several papers provide different approaches to separate cells of a specific type [8–11]. Due to the often simple morphology of the analyzed cell types, a separation of clusters with simple rules is possible. An iterative erosion method may create a separation of cells or objects at joining points between cells. After each iteration, it has to be checked whether separated objects have been created. The