Stem cell microscopic image segmentation using supervised normalized cuts

A vast amount of toxicological data can be obtained from feature analysis of cells treated in vitro. However, this requires microscopic image segmentation of cells. To this end, we propose a new strategy, namely Supervised Normalized Cut Segmentation (SNCS), to segment cells that partially overlap and have a large amount of curved edges. SNCS approach is a machine learning based method, where loosely annotated images are used first to train and optimise parameters, and then the optimal parameters are inserted into a Normalized Cut segmentation process. Furthermore, we compare our segmentation results using SNCS to another four classical and two state-of-the-art methods. The overall experimental result shows the usefulness and effectiveness of our method over the six comparison methods.