An efficient iterative thresholding method for image segmentation

Abstract

We proposed an efficient iterative thresholding method for multi-phase image segmentation. The algorithm is based on minimizing piecewise constant Mumford-Shah functional in which the contour length (or perimeter) is approximated by a non-local multi-phase energy. The minimization problem is solved by an iterative method. Each iteration consists of computing simple convolutions followed by a thresholding step. The algorithm is easy to implement and has the optimal complexity $O(N\log N)$ per iteration. We also show that the iterative algorithm has the total energy decaying property. We present some numerical results to show the efficiency of our method.

14 pages, 21 figures

References in corpus (1)

• An efficient threshold dynamics method for wetting on rough surfaces

Cited by in corpus (13)

• A provably efficient monotonic-decreasing algorithm for shape optimization in Stokes flows by phase-field approaches
• An efficient diffusion generated motion method for wetting dynamics
• Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?
• Fast operator splitting methods for obstacle problems
• Binary Level Set Method for Variational Implicit Solvation Model
• Diffusion generated methods for denoising target-valued images
• Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images
• Explicit-Solute Implicit-Solvent Molecular Simulation with Binary Level-Set, Adaptive-Mobility, and GPU
• Volume Preserving Image Segmentation with Entropic Regularization Optimal Transport and Its Applications in Deep Learning
• Two-phase image segmentation by the Allen-Cahn equation and a nonlocal edge detection operator
• An efficient iterative method for reconstructing surface from point clouds
• A Characteristic Function-based Algorithm for Geodesic Active Contours
• Color image segmentation based on a convex K-means approach