Automatic Classification and Quantification of Cell Adhesion Locations on the Endothelium.

To target tumor hematogenous metastasis and to understand how leukocytes cross the microvessel wall to perform immune functions, it is necessary to elucidate the adhesion location and transmigration pathway of tumor cells and leukocytes on/across the endothelial cells forming the microvessel wall. We developed an algorithm to classify and quantify cell adhesion locations from photomicrographs taken from the experiments of tumor cell/leukocyte adhesion in individual microvessels. The first step is to identify the microvessel by a novel gravity-field dynamic programming (DP) procedure. Next, an anisotropic image smoothing suppresses noises without unduly mitigating crucial visual features. After an adaptive thresholding process further tackles uneven lighting conditions during the imaging process, a series of local mathematical morphological operators and eigenanalysis identify tumor cells or leukocytes. Finally, a novel double component labeling procedure categorizes the cell adhesion locations. This algorithm has generated consistently encouraging performances on photomicrographs obtained from in vivo experiments for tumor cell and leukocyte adhesion locations on the endothelium forming the microvessel wall. Compared with human experts, this algorithm used 1/500-1/200 of the time without having the errors due to human subjectivity. Our automatic classification and quantification method provides a reliable and cost effective approach for biomedical image processing.