There is a growing need to study and examine microscope slides in various fields of science. However, this task can be cumbersome and vulnerable to human error. This is especially true when large numbers of slides have to be examined. It is apparent that the existing computer and image processing technology should be utilized to speed up the process of cell examination. In our research, an existing method of image segmentation, called pyramid node linking, has been applied with a few modifications to cell segmentation. In pyramid node linking, a pyramid is constructed by successively reducing the resolution of the original image by factors of two to obtain the first and subsequent levels, until there are four pixels left on the top most level. In a bottom-to-top iterative process, the pixels from level to level are linked using information from the level above, the level below, and from the neighbor pixels on the same level. This results in several trees with roots at one of the upper levels and leaves on the original image. This process results in smooth simply-connected regions with well-defined boundaries on the bottom level. We have applied pyramid node linking to complex images consisting of clusters of rat liver cells grown in culture and damaged to different degrees by exposure to various chemicals. The algorithm has been applied to classify the rat liver cells in three categories: undamaged, slightly damaged, and disintegrated.