An efficient iterative thresholding method for image segmentation
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Abstract (arXiv)
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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Aug 2016.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 2; 2025: 2; 2026: 2.
10 papers.
- 2021Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?cited by 7
- 2024Deep Convolutional Neural Networks Meet Variational Shape Compactness Priors for Image Segmentationcited by 4
- 2023Connections between Operator-splitting Methods and Deep Neural Networks with Applications in Image Segmentationcited by 3
- 2026Topology-Guaranteed Image Segmentation: Enforcing Connectivity, Genus, and Width Constraintscited by 2
- 2023Interpretable Small Training Set Image Segmentation Network Originated from Multi-Grid Variational Modelcited by 1
- 2024Slender Object Scene Segmentation in Remote Sensing Image Based on Learnable Morphological Skeleton with Segment Anything Modelcited by 1
- 2022An Active Contour Model with Local Variance Force Term and Its Efficient Minimization Solver for Multi-phase Image Segmentationcited by 0
- 2025Topology preserving Image segmentation using the iterative convolution-thresholding methodcited by 0
- 2025Contour Field based Elliptical Shape Prior for the Segment Anything Modelcited by 0
- 2026A Unified Variational Framework for Deep Weakly Supervised Image Segmentationcited by 0
- 2026A Unified Variational Framework for Deep Weakly Supervised Image Segmentation22 Jul 2026
- 2026Topology-Guaranteed Image Segmentation: Enforcing Connectivity, Genus, and Width Constraints16 Jan 2026
- 2025Contour Field based Elliptical Shape Prior for the Segment Anything Model17 Apr 2025
- 2025Topology preserving Image segmentation using the iterative convolution-thresholding method22 Mar 2025
- 2024Slender Object Scene Segmentation in Remote Sensing Image Based on Learnable Morphological Skeleton with Segment Anything Model13 Nov 2024
- 2024Deep Convolutional Neural Networks Meet Variational Shape Compactness Priors for Image Segmentation23 May 2024
- 2023Connections between Operator-splitting Methods and Deep Neural Networks with Applications in Image Segmentation18 Jul 2023
- 2023Interpretable Small Training Set Image Segmentation Network Originated from Multi-Grid Variational Model25 Jun 2023
- 2022An Active Contour Model with Local Variance Force Term and Its Efficient Minimization Solver for Multi-phase Image Segmentation17 Mar 2022
- 2021Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?1 Jan 2021