Cell nuclei segmentation is one of the most important tasks in the analysis of biomedical images. With ever-growing sizes and amounts of three-dimensional images to be processed, there is a need for better and faster segmentation methods. Graph-based image segmentation has seen a rise in popularity in recent years, but is seen as very costly with regard to computational demand. We propose a new segmentation algorithm which overcomes these limitations. Our method uses recursive balanced graph partitioning to segment foreground components of a fast and efficient binarization. We construct a model for the cell nuclei to guide the partitioning process. Our algorithm is compared to other state-of-the-art segmentation algorithms in an experimental evaluation on two sets of realistically simulated inputs. Our method is faster, has similar or better quality and an acceptable memory overhead.
@article{arxiv.1702.05413,
title = {3D Cell Nuclei Segmentation with Balanced Graph Partitioning},
author = {Julian Arz and Peter Sanders and Johannes Stegmaier and Ralf Mikut},
journal= {arXiv preprint arXiv:1702.05413},
year = {2017}
}