Cell segmentation with random ferns and graph-cuts

TL;DR

This paper presents a novel cell segmentation method combining random ferns for pixel classification with graph-cuts for boundary detection, effectively handling poor edge details in live imaging data.

Contribution

It introduces a two-stage segmentation framework that integrates probabilistic pixel classification with energy minimization for accurate cell boundary extraction.

Findings

Effective segmentation in challenging imaging conditions

Validated on manually annotated datasets

Outperforms existing methods in boundary accuracy

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.