Background: Automatic 3D digital reconstruction (tracing) of neurons embedded in noisy microscopic images is challenging, especially when the cell morphology is complex.Results: We have developed a novel approach, named DF-Tracing, to tackle this challenge. This method first extracts the neurite signal (foreground) from a noisy image by using anisotropic filtering and automated thresholding. Then, DF-Tracing executes a coupled distance-field (DF) algorithm on the extracted foreground neurite signal and reconstructs the neuron morphology automatically. Two distance-transform based " force" fields are used: one for " pressure" , which is the distance transform field of foreground pixels (voxels) to the background, and another for " thrust" , which is the distance transform field of the foreground pixels to an automatically determined seed point. The coupling of these two force fields can " push" a " rolling ball" quickly along the skeleton of a neuron, reconstructing the 3D cell morphology.Conclusion: We have used DF-Tracing to reconstruct the intricate neuron structures found in noisy image stacks, obtained with 3D laser microscopy, of dragonfly thoracic ganglia. Compared to several previous methods, DF-Tracing produces better reconstructions.