A Region-Bias Fitting Model based Level Set for Segmenting Images with Intensity Inhomogeneity

Abstract

Intensity inhomogeneity is a common phenomenon in real world images, and inevitably leads to many difficulties for accurate image segmentation. This paper proposes a novel region-based model, named Region-Bias Fitting (RBF) model, for segmenting images with intensity inhomogeneity by introducing desirable constraint term based on region bias. Specially, we firstly propose a constraint term which includes both the intensity bias and distance information to constrain the local intensity variance of image. Then, the constraint term is utilized to construct the local bias constraint and determine the contribution of each local region so that the image intensity is fitted accurately. Finally, we use the level set method to construct the final energy functional. By using the novel constraint information, the proposed RBF model can accurately delineate the object boundary, which relies on the local statistical intensity bias and local intensity fitting to improve the segmentation results. In order to validate the effectiveness of the proposed method, we conduct thorough experiments on synthetic and real images. Experimental results show that the proposed RBF model clearly outperforms other models in comparison.