A Correntropy-Based Local Additive Bias-Field-Corrected Image Fitting Model for Image Segmentation

Segmenting images with intensity inhomogeneity is a difficult problem in the field of image processing. In order to deal with it, this paper presents a novel local additive bias-field-corrected image fitting model using correntropy criterion. Firstly, a local additive bias-field-corrected fitting image model in the level set formulation is built by using information of bias field and reflection ratio simultaneously. Secondly, an energy function is introduced by minimizing the difference between the fitting image and original input image in a neighborhood, which makes it effective in segmenting images with intensity inhomogeneity. Thirdly, by incorporating the correntropy criterion into the new energy, the proposed method can reduce the impact of noise on segmentation results. Finally, the local energy is integrated with respect to the neighborhood center and a global result of image segmentation in the whole domain is obtained. Experiments show that our method is robust to different kinds of noises, and the computational efficiency is better than the existing bias field correction model.