LAUSR

Medical image segmentation has been widely used in clinical practice. It is an important basis for medical experts to diagnose the disease. However, weak edges and intensity inhomogeneity (Niu et al. in 2nd IEEE international conference on computational intelligence and applications (ICCIA). https://doi.org/10.1109/ciapp.2017.816722, 2017) in medical images may hinder the accuracy of any traditional active contour segmentation methods. In this paper, we propose an improved active contour method by embedding a boundary constraint factor and adding the local information of images to the energy function of Chan–Vese model. Then graph cuts method is used to optimize the new energy function of the improved active contour method in this paper. There are several salient features: (1) we can obtain more accurate boundaries by embedding a constraint factor and adding local information of images. (2) The energy function is not easily to fall into local optimum. (3) Finally, our method only need to adjust one parameter. The evaluation results on Magnetic Resonance Imaging and Computed Tomography, blood vessel images, and mammograms masses show that the proposed method leads to more accurate boundary detection results than the state-of-the-art edge-based and region-based active contour segmentation methods.