  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.
