A variational level set model for multiscale image segmentation

Abstract

In this paper, we propose a variational level set model for multiscale image segmentation that can extract desired object from the background under various scales. The new model consists of three terms: A boundary extraction term, a regularization term and a multiscale representation term. The boundary extraction term is based on the Chan–Vese (CV) functional, which drives the active contour toward the boundaries of the objects. The regularization term penalizes the length of the active contour and keeps the level set function close to a signed distance function, which enables the active contour to maintain its stability during the evolution process. The multiscale representation term is based on the total variation (TV) energy that can provide a multiscale representation of the input image by tuning the scale parameter. We use an alternating iterative algorithm that combines the gradient descent algorithm and the alternating direction method of multipliers (ADMM) to numerically solve the model. The experimental results for both synthetic and real images demonstrate the robustness and effectiveness of the proposed model for multiscale image segmentation. In addition, the proposed model is a promising model for image denoising applications.