A fast Geodesic Active Contour model for medical images segmentation using prior analysis

The deformable Geodesic Active Contour (GAC) method is one of the most important techniques used in object boundaries detection in images. In this work, we modify the automatic GAC technique by incorporating priori information extracted from the region of interest. We introduce a new stopping function to speed up convergence and improve accuracy. The proposed technique was applied to both synthetic and real medical images. We show an improvement in speed of more than 40% together with an excellent accuracy compared to the traditional GAC model.