Cell Segmentation with Random Ferns and Graph-cuts
Arnaud Broweta, Christophe De Vleeschouwera, Laurent Jacquesa, Navrita Mathiahb, Bechara Saykalib, Isabelle Migeottec
- aUniversité catholique de Louvain, ICTEAM Institute, Belgium
- bUniversité Libre de Bruxelles, IRIBHM, Belgium
- cUniversité catholique de Louvain , ICTEAM Institute , Belgium
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Abstract
ABSTRACT 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.
TL;DRThis work introduces an elegant image segmentation framework that effectively extracts cell boundaries, even in the presence of poor edge details, in live imaging data. (Semantic Scholar)