RT Journal Article
SR Electronic
T1 Cell Segmentation with Random Ferns and Graph-cuts
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 039958
DO 10.1101/039958
A1 A. Browet
A1 C. De Vleeschouwer
A1 L. Jacques
A1 N. Mathiah
A1 B. Saykali
A1 I. Migeotte
YR 2016
UL http://biorxiv.org/content/early/2016/02/17/039958.abstract
AB 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.