RVLSM: Robust variational level set method for image segmentation with intensity inhomogeneity and high noise

Intensity inhomogeneity and high noise are two common but challenging issues in image segmentation and is particularly pronounced when the two issues simultaneously appear in one image. As a result, most existing level set methods yield poor performance when applied to these images. To address this issue, this paper proposes a robust variational level set method (RVLSM) based on adaptive diffusion mechanism and local cluster criterion, which can not only correct the severe inhomogeneous intensity but also denoise in segmentation. Specifically, we first define an adaptive-scale representation term using the proposed adaptive diffusion mechanism to transform the image data towards diffusion induced space, which successfully restrains different types/levels of noise while enhancing image details. Then, a new bias field correction term is constructed via estimating the bias in transformed domain to better correct the severe inhomogeneous intensity while segmentation. Finally, an enhanced fourth-order piecewise polynomial penalty term is designed to eradicate numerical calculation instability and tedious re-initialization during the evolution of the level set. The experimental results on synthetic and real images with severe intensity inhomogeneity and high noise demonstrate the superiority of the proposed method over most existing methods in both accuracy and robustness.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a robust variational level set method to address the challenges of intensity inhomogeneity and high noise in image segmentation. The method utilizes adaptive diffusion mechanism and local cluster criterion to correct the intensity inhomogeneity and denoise the image, achieving superior performance in accuracy and robustness.