Active contour models (ACMs) are efficient frameworks for image segmentation. They can provide smooth and closed contours to recover object boundaries with sub-pixel accuracy. Region-based ACMs identify each region of interest by using region and statistical information as constraints to guide the motion of the active contour. The most popular region-based ACM is the Chan-Vese (C-V) model

which has been successfully used in binary phase segmentation with the assumption that image intensities are homogeneous in each region. However

typical region-based models do not work well on images with intensity inhomogeneity. This paper presents a new level-set-based K-means ACM

which can effectively segment images with intensity inhomogeneity. The model is derived from a linear level-set-based K-means model

which is established based on the properties of the corresponding Euler-Lagrange equation of the traditional ACM. The most widely used region-based ACMs are used

namely

the global region-based ACM

C-V model; the local region-based ACM

the local binary fitting (LBF) model; the local image fitting (LIF) model; and the local correntropy-based K-means (LCK) model. Their fitting terms correspond to the classical K-means. The C-V model has a global segmentation capacity

that is

it can segment all objects in an image; however

it cannot handle images with intensity inhomogeneity and various noises. The LBF and LIF models possess a local segmentation property; thus

they can only segment the desired object with a proper initial contour. In this paper

we propose a new region-based ACM

which integrates the advantages of global and local region-based ACMs. The global information and local information are combined to avoid entrapment in the local minima

and the local correntropy-based adaptive weights are used to ensure robustness against noise and fast convergence. The proposed model can successfully detect objects in a noisy synthetic image with intensity inhomogeneity. Results of the experiments on medical images show that compared with the background models

the proposed model can yield competitive results. Furthermore

when different initial contours are used

the proposed model can still realize correct segmentation for inhomogeneous images

whereas the other models are easily trapped in the local minima. This segmentation results demonstrate that the proposed model is not only capable of obtaining better segmentation results but also robust against noise and initializations. A novel and robust ACM based on K-means is proposed to segment images with intensity inhomogeneity. Relying on the correntropy-based image features

the model uses local adaptive weights to withstand various noises. Moreover

the combination of local and global region information prevents the proposed model from being trapped into a local minimum. To avoid re-initialization and shorten the computational time

we use a signed distance function to regularize the level set function and adopt an iteratively re-weighted method to enhance the speed of our algorithm during the contour evolution. The experimental results show that our algorithm can achieve robust segmentation even in the presence of the intensity inhomogeneity

noise

and blur.