A New Active Contour Medical Image Segmentation Method Based on Fractional Varying-Order Differential

Abstract

Image segmentation technology is dedicated to the segmentation of intensity inhomogeneous at present. In this paper, we propose a new method that incorporates fractional varying-order differential and local fitting energy to construct a new variational level set active contour model. The energy functions in this paper mainly include three parts: the local term, the regular term and the penalty term. The local term combined with fractional varying-order differential can obtain more details of the image. The regular term is used to regularize the image contour length. The penalty term is used to keep the evolution curve smooth. True positive (TP) rate, false positive (FP) rate, precision (P) rate, Jaccard similarity coefficient (JSC), and Dice similarity coefficient (DSC) are employed as the comparative measures for the segmentation results. Experimental results for both synthetic and real images show that our method has more accurate segmentation results than other models, and it is robust to intensity inhomogeneous or noises.