The paper presents a method that enables automated segmentation of the low-contrast shadowgraph images, e.g., acquired in the studies of laser induced shockwave phenomena. The method is especially suitable for the analysis of large image data sets, such as obtained at studying the evolution of laser-induced shockwaves with high spatial and temporal resolution. The method comprises two active contours algorithms. First, the approximate shape of the shockwave is detected by a traditional snake algorithm using external energies that base on texture cues. The outcome of the coarse detection serves as an initialization to the second refining stage detection introducing a Greedy snake algorithm. Local optimum is searched with respect to responses of steerable filtering and edge orientation similarity by exploiting the Bayesian formalism. The paper presents validation of the method on sample of 12 low contrast shadowgraphs by comparison to the manual segmentation technique. The obtained results demonstrate overall good performance, robustness and high accuracy of the method.