A Selective Active Contour Model Based on Fractional Order Differentiation
Zhang Minyi, Shurong Li, Xueqin Wang · 2018
A novel active contour model is proposed for image segmentation, which based on fractional order differentiation and the selective segmentation model. The energy functional for the proposed model consists of three term: fractional order fitting term, selective segmentation term and penalty term. Firstly, by constructing a fractional order fitting term, the novel model can protect texture and lower frequency features of images. So it can extract more image details compared with the local binary fitting energy model (LBF). Secondly, due to the combination with the selective segmentation model, the proposed model is able to selective segment images with intensity inhomogeneity and has desirable performance for images with noise. In addition, the time-consuming re-initialization step widely adopted in traditional level set methods can be avoided by introducing a penalizing energy. Finally, experimental results for both synthetic and real image show desirable performance of our method.