Extracting Rules for Cell Segmentation in Corneal Endothelial Cell Images Using GP

In tissue engineering of the corneal endothelium, extracting feature values of cultured cells from cell images helps us to automatically judge whether they are transplantable. To extract feature values, accurate image processing for cell segmentation is needed. We previously proposed a method that constructs a tree-structural image-processing filter by automatically combining known image-processing filters. In this paper, we propose a more accurate method that can be applied to images in which statistics differ in different regions. The proposed method prepares two types of nodes. One type of node represents known image-processing filters, and the other represents conditional branches, which determine the divergent direction using the statistics of the cell images. Moreover, the proposed method optimizes their combination by using genetic programming (GP). The proposed method is compared with the existing method using GP and specialist software for analyzing cell images. The results show that the proposed method has superior accuracy.