# Cell segmentation with random ferns and graph-cuts

arXiv: https://arxiv.org/abs/1602.05439 · HTML: https://mlsanity.com/paper/1602.05439/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2016.

## Built on by (most cited first, 1 of 1)

- [Random Ferns for Semantic Segmentation of PolSAR Images](https://mlsanity.com/paper/2202.03498/) (2022)

## Abstract (arXiv)

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.