The local correntropy-based k-means (LCK) model can segment an image that contains unknown noise and has an uneven gray distribution. However

the segmentation result is sensitive to the initial contour. To solve this problem

a new dynamic model based on global correntropy-based k-means (GCK) and LCK is presented. The dynamic model is a combination of two models. A new algorithm

i.e.

GCK

is proposed by introducing correntropy to the coefficient of variation (CV) model and improving the CV model. A global and local correntropy-based k-means (GLCK) model is then proposed by combining GCK and LCK dynamically to retain each method's advantages. The GLCK model is not a simple linear combination of the two models. The model implements two steps to complete segmentation. First

the GCK model isutilized to segment an image and obtain the general outline of the image. Second

the image with the initial contour as segmentation results of GCK is segmented finely by LCK. To improve segmentation accuracy

a dynamic combination algorithm is designed by controlling the time when the GCK model transforms into the LCK model automatically. The segmentation result of the proposed method is compared to that of three other similar segmentation methods

namely

LCK

local binary fitting

and CV models

on natural and synthetic images. Results showed that the proposed model is more robust than the three other models. By segmenting two natural images on the BSD library and using the Jaccard similarity ratio for quantitative analysis

accuracy rates of 91.37% and 89.12% are obtained. The proposed algorithm can effectively segment medical images and the simple structure of natural images with unknown noise and an uneven gray distribution; the result is robust to the initial outline.