An image segmentation method based on Mumford–Shah model with mask factor and neighborhood factor

Abstract

A novel image segmentation model is proposed to improve the stability of existing segmentation methods. In the proposed model, we introduce two factors into the Mumford–Shah model, including mask factor and neighborhood factor. Firstly, the mask factor can express the image more accurately. Therefore, the new segmentation model can more realistically reflect the structure of the image. Moreover, neighborhood factor is used to constrain the evolution of the initial contour. Then the segmentation model is converted into an equivalent form by a level set function. At last, the model can be solved in a simple way based on partial differential equations and extreme values. The experimental results show the proposed method could generate accurate segmentation results, and the segmentation results are not sensitive to initial contour and external disturbances, such as noise and blurring.