Despite more than a century of research, we still lack a concise description of the different types of neurons in the brain. This is a result of their large variance in morphology and the time consuming reconstruction process. In this thesis we design novel fully automated algorithms to release neuroscientists from such burdern. To that end, we first propose a filtering method that enhances neurite structures while rejecting structured noise. Prior work rely on filters optimized for signals of a particular shape, such as an ideal edge or ridge. While these approaches are optimal when the image conforms to these ideal shapes, their performance quickly degrades on many types of real data where the image deviates from such ideal model. We show that by learning rotational features, we can outperform state-of-the-art filament detection techniques on many different kinds of imagery. More specifically, we demonstrate superior performance for the detection of blood vessel in retinal scans, neurons in brightfield microscopy imagery, and streets in satellite imagery. Afterwards we propose algorithms that extract automatically the tree structures present on the images by maximizing a global likelihood under a generative model in noisy 2D images and 3D image stacks. Unlike earlier methods that rely mostly on local evidence, our method builds a set of candidate trees over many different subsets of points likely to belong to the final one and then chooses the best one according to a global objective function. Since we are not systematically trying to span all nodes, our algorithm is able to eliminate noise while retaining the right tree structure. We performed extended evaluation of the proposed algorithms and participated on a prestigious neuron reconstruction challenge. We obtained an award "for our deeper potential, more original approach, and ultimate scalability of our proposed solution". Finally, we propose system that tracks, segments and traces neurons in time-lapse microscopy and is suitable for high throughput image analysis. We examined neuronal differentiation datasets where gene expressions were inhibited using RNA interference. We were able to corroborate previously known behaviours and to derive new ones.