This work has been supported in part by the projects La Marató de TV3 082131, TIN2009-14404-C02, and CONSOLIDER-INGENIO CSD 2007-00018.

The segmentation of tubular structures is still an open eld of investigation, particularly in medical imaging, where the quality of the image is poor with respect to natural images. Despite the quality of state-of-the-art segmentation methods, little effort has been devoted to the computational effi ciency of the algorithms. E fficiency is an important topic, since intra-operative computer assisted interventions require near real-time performance. In this master thesis, we present a simple, yet effective, algorithm that e fficiently segments vessels in 2D images and 3D volumes. The algorithm requires no initialization and has a computational cost of O(SN logN), where S is the number of scales and N is the number of image pixels. Results on the DRIVE dataset show that the proposed method has near state-of-theart performance with very little computational burden in the 2-dimensional case. Qualitative results on the Rotterdam Coronary Artery dataset show that the method is easily extendable to 3-dimensions.