A novel hybrid active contour model for medical image segmentation driven by Legendre polynomials

In this paper, a novel hybrid active contour model for medical image segmentation is proposed, which integrates the global information of image and Legendre level set. It is a region-based segmentation approach, in which the illumination of the regions of interest is represented by a set of Legendre basis functions in a lower dimensional subspace. Firstly, we present a framework which generalizes the Chan-Vese model and segmentation method based on Legendre level set. The weighting parameter is introduced to control the effect of global and local term on the total energy functional. Secondly, a corresponding termination criterion is employed to ensure the evolving curve automatically stops on true boundaries of objects. Thirdly, experiment results on medical images demonstrate that our method is less sensitive to the initial contour and effective to segment images with inhomogeneous intensity distributions.