Hierarchical Markov Random Fields for mast cell segmentation in electron microscopic recordings

We present a hierarchical Markov Random Field (HMRF) for multi-label image segmentation. With such a hierarchical model, we can incorporate global knowledge into our segmentation algorithm. Solving the MRF is formulated as a MAX-SUM problem for which there exist efficient solvers based on linear programming. We show that our method allows for automatic segmentation of mast cells and their cell organelles from 2D electron microscopic recordings. The presented HMRF outperforms classical MRFs as well as local classification approaches wrt. pixelwise segmentation accuracy. Additionally, the resulting segmentations are much more consistent regarding the region compactness.