Adaptive active contour model driven by fractional order fitting energy

• We proposed an adaptive active contour model based on fractional differentiation.
• The new fitting term can describe the original image more accurately.
• Adaptive length regularization is used to smooth the level set function.

In this paper, a new adaptive active contour model is proposed for image segmentation, which is built based on fractional order differentiation, level set method and curve evolution. The energy functional for the proposed model consists of three terms: fitting term, regularization tern and penalty term. By incorporating the fractional order fitting term, the novel fitting term can describe the original image more accurately, and be robustness to noise. In order to ensure stable evolution of the level set function, a penalty tern is added into the proposed model. The results evolution of the level set function is the gradient flow that minimizes the overall energy functional. Experimental results for both synthetic and real image show desirable performance of our method.