  The automated segmentation of cells in microscopic images is an open research
problem that has important implications for studies of the developmental and
cancer processes based on in vitro models. In this paper, we present the
approach for segmentation of the DIC images of cultured cells using G-neighbor
smoothing followed by Kauwahara filtering and local standard deviation approach
for boundary detection. NIH FIJI/ImageJ tools are used to create the ground
truth dataset. The results of this work indicate that detection of cell
boundaries using segmentation approach even in the case of realistic
measurement conditions is a challenging problem.
