Robust Image Segmentation and Bias Field Correction Model Based on Image Structural Prior Constraint

In this paper, we propose an advanced variational model for image segmentation and bias correction. In contrast to the majority of existing level set segmentation models that only consider illumination bias fields, we additionally consider the impact of image reflectance on segmentation accuracy. Our method is capable of effectively segmenting images with blurry and unclear edge structures affected by non-uniform illumination. In order to enhance segmentation efficiency, we directly segment the underlying structures of the image, construct spatial prior and apply adaptive regularization constraints on the structural components. Therefore, in segmentation applications, the proposed algorithm can accurately identify object boundaries without being affected by the environment. Besides, the GL operator is applied to enhance the robustness of the model against noise. Furthermore, we use alternating minimization and operator splitting algorithms for numerical solution. The experimental results obtained from various sorts of images illustrate that our model outperforms many leading-edge level set models with regard to robustness, corrected results and accuracy.