Abstract : The morphological analysis of axonal trees is an important problem in neuroscience. The first step for such an analysis is the extraction of the axon. Due to the high volume of generated image data and the tortuous nature of the axons, manual processing is not feasible. Therefore, it is necessary to develop techniques for the automatic extraction of the neuronal structures. In this paper we present a new approach for the automatic extraction of axons from fluorescent confocal microscopy images. It combines algorithms for filament enhancement, binarization, skeletonization and gap filling in a pipeline capable of extracting the axons. The performance of the proposed method was evaluated on real images. Results support the potential use of this technique in helping biologists perform automatic extraction of axons from fluorescent confocal microscopy images.