Real-world images are often distorted by unknown noise and intensity inhomogeneity

thereby making segmentation a challenging task. The local correntropy-based K-means (LCK) model shows significant improvements on images with unknown noise and uneven gray distribution. However

the segmentation results are sensitive to the initial contour

and the speed of the segmentation convergence is slow. To solve these problems

this paper presents a new two-stage segmentation model based on LCK model. The new model is a combination of two stages

and each stage is based on LCK model. In the first step

the convolution result of image information and Gauss kernel was down-sampled

and the down-sampled result was segmented based on LCK model resulting on coarse segmentation results and coarse level set functions accordingly. The down-sampling of the original image resulted in a coarse scale image

which could reduce data size. With the benefit of data size reduction

the down-sampled image could be rapidly segmented to an approximate result. Compared with direct down-sampling operation

down-sampling with convolution of the image information and Gauss kernel lost lesser information and could calculate local weighted average. Therefore

the gray image value was suitable. In the second step

with the smoothness constraints of level set functions

the coarse segmentation results and according coarse level set functions were up-sampled to the original image scale. The coarse level set functions of up-sampling were then used as initial value of explicit segmentation based on the LCK model. Given that the initial value was a close approximate of the object boundary

less iteration was needed to obtain results. The proposed model could provide improved contour

which was close to the object boundary for LCK model. The results of segmentation of synthetic image show that

compared with LCK model

the proposed model converged faster and was more accurate. By utilizing F-score value as an evaluation criterion

the proposed model obtained higher values than the LCK model. In addition

when images were intensity inhomogeneous or distorted by different noises

the proposed model could secure improved results with iterations of less than 50

whereas iterates of the LCK model could reach at least 1000. The proposed model was more robust than the LCK model on natural and synthetic images with complex noises. A fast and accurate segmentation based on LCK model is proposed. Based on the feature of down-sampling

the processing time is reduced without losing much information. The proposed model combines down-sampling with Gauss kernel to reserve much image information. To avoid the sensitivity of LCK model to the initial contour

the coarse segmentation provides an initial contour close to real object boundary. The proposed algorithm can rapidly segment an image with unknown complex noise.