  The progress in imaging techniques have allowed the study of various aspect
of cellular mechanisms. To isolate individual cells in live imaging data, we
introduce an elegant image segmentation framework that effectively extracts
cell boundaries, even in the presence of poor edge details. Our approach works
in two stages. First, we estimate pixel interior/border/exterior class
probabilities using random ferns. Then, we use an energy minimization framework
to compute boundaries whose localization is compliant with the pixel class
probabilities. We validate our approach on a manually annotated dataset.

Computer Vision and Pattern Recognition;Machine Learning