This thesis addresses the segmentation and the tracking of thin curvilinear structures. The proposed methodology is applied to the delineation and the tracking of the guide-wires that are used during cardiac angioplasty. During these interventions, cardiologists assess the displacement of the different devices with a real-time fluoroscopic imaging system. The obtained images are very noisy and, as a result, guide-wires are particularly challenging to segment and track. The contributions of this thesis can be grouped into three parts. The first part is devoted to the detection of the guide-wires, the second part addresses their segmentation and the last part focuses on their spatio-temporal tracking. Partial detection of guide-wires is addressed either through the selection of appropriate filter operators or using modern machine learning methods. First, a learning framework using an asymmetric Boosting algorithm for training a guidewire detector is presented. A second method enhancing the output of a steerable filter by using an efficient tensor voting variant is then described. In the second part, a bottom-up method is proposed, that consists in grouping points selected by the wire detector, in extracting primitives from these aggregates and in linking these primitives together. Two local grouping procedures are investigated: one based on unsupervised graph-based clustering followed by a linesegment extraction and one based on a graphical model formulation followed by a graph-based centerline extraction. Subsequently, two variants of linking methods are investigated: one is based on integer programming and one on a local search heuristic. In the last part, registration methods are exploited for improving the segmentation via an image fusion method and then for tracking the wires. This latter is performed by a graph-based iconic tracking method coupled with a graphbased geometric tracking that encodes to certain extend a predictive model. This method uses a coupled graphical model that seeks both optimal position (segmentation) and spatio-temporal correspondences (tracking). The optimal solution of this graphical model simultaneously determines the guide-wire displacements and matches the landmarks that are extracted along it, what provides a robust estimation of the wire deformations with respect to large motion and noise.
