The problem of histogram thresholding is tackled using a modular expert network. The modular expert network is a network of expert modules modulated by a gating network. The expert modules incorporate individual experts' opinions on the thresholding problem. The difficult task of integration of conflicting experts' opinions is achieved through a training of the gating network using backpropagation. The resulting network achieves accurate modeling of the solution mapping through the efficient combination of existing experts. Experimental results show the superior performance of the
modular network over classical algorithms. In particular, a near-optimal solution was shown to be achievable using a small training set. Application to a real-world biomedical cell segmentation problem is also given.