Automated segmentation of stem cell colonies for phase contrast imaging is challenging and requires specialized algorithms to handle the problems of halo artifacts and overlapping of the colony edges with the feeder cells . The currently available image analysis techniques to achieve stem cell colony selection are based on morphological operations, thresholding, and watershed transformation. A combination of these techniques is designed to examine the status of the colony in each individual research [7–9]. Alternatively, other approaches have adopted commercial software tools that basically use filtering, automatic thresholding, and Voronoi algorithms for stem cell segmentation and tracking [1, 5, 10, 11]. In addition, the morphological categories of colonies based on commercial program require manual interpretation to locate the colony area for feature measurement . Because these aforementioned image analysis techniques are quite problem specific and rely strictly on parametric settings, they lack controllability to manipulate variations among the stem cell heterogeneity on a large scale.