An Active Contour Model with Local Variance Force Term and Its Efficient Minimization Solver for Multi-phase Image Segmentation

TL;DR

This paper introduces an active contour model with a local variance force for robust multi-phase image segmentation, along with an efficient minimization algorithm and an improved initialization method, demonstrating effectiveness on noisy and real images.

Contribution

The paper presents a novel active contour model with a local variance force and an efficient ICTM-LVF solver, along with a generalized initialization method for multi-phase segmentation.

Findings

Effective noise robustness demonstrated on synthetic and real images.

The ICTM-LVF algorithm converges with energy decay and high efficiency.

Initialization with IGLIM improves segmentation accuracy.

Abstract

In this paper, we propose an active contour model with a local variance force (LVF) term that can be applied to multi-phase image segmentation problems. With the LVF, the proposed model is very effective in the segmentation of images with noise. To solve this model efficiently, we represent the regularization term by characteristic functions and then design a minimization algorithm based on a modification of the iterative convolution-thresholding method (ICTM), namely ICTM-LVF. This minimization algorithm enjoys the energy-decaying property under some conditions and has highly efficient performance in the segmentation. To overcome the initialization issue of active contour models, we generalize the inhomogeneous graph Laplacian initialization method (IGLIM) to the multi-phase case and then apply it to give the initial contour of the ICTM-LVF solver. Numerical experiments are conducted on synthetic images and real images to demonstrate the capability of our initialization method, and the effectiveness of the local variance force for noise robustness in the multi-phase image segmentation.