Fluorescence micrograph segmentation by gestalt-based feature binding

We present the application of a recurrent neural network feature binding model to the segmentation of fluorescence micrographs, images showing fluorescent cells in tonsil tissue. Image primitives, referred to as features, consisting of position and local gradient information, build the input to the model. The competitive layer model is used to provide a binding of features to convex groups, corresponding to fluorescent cell bodies. Although the images contain noise, and the cells' shapes show considerable variation, the fluorescent cell contours are extracted with sufficient accuracy, according to a biomedical expert. The method achieves at the same time grouping and figure-ground segmentation, and does not require us to manually fix the number of groups.