Model-based graph segmentation in 2-D fluorescence microscopy images

In biology and pathology, immunofluorescence microscopy approaches are leading techniques for deciphering molecular mechanisms of cell activation and disease progression. Although several solutions for image analysis exist, totally nonsubjective image analysis remains difficult. There is therefore a strong need for analysis procedures highly reproducible, avoiding thresholds and selection of objects of interest by hand. To address this need, we describe a fully automatic segmentation of cell nuclei in 2-D immunofluorescence images. The method merges segments of the image to fit with a nuclei model learned by a trained Random Forest classifier. The merging procedure explores efficiently the fusion configurations space of an over-segmented image by using minimum spanning tree of its region adjacency graph.