Stem cell microscopic image segmentation using supervised normalized cuts

Xinyu Huang*, Chen Li, Minmin Shen, Kimiaki Shirahama, Johanna Nyffeler, Marcel Leist, Marcin Grzegorzek, Oliver Deussen

*Corresponding author for this work


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.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing (ICIP)
Number of pages5
Publication date2016
Publication statusPublished - 2016


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