Optimal thresholding—a new approach
Pattern Recognition Letters
Digital Image Processing
Journal of Global Optimization
Optimal multi-thresholding using a hybrid optimization approach
Pattern Recognition Letters
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
A multi-level thresholding approach using a hybrid optimal estimation algorithm
Pattern Recognition Letters
Image histogram thresholding based on multiobjective optimization
Signal Processing
Expert Systems with Applications: An International Journal
Automatic image pixel clustering with an improved differential evolution
Applied Soft Computing
Automatic Threshold Selection Based on Particle Swarm Optimization Algorithm
ICICTA '08 Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation - Volume 01
Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm
Information Sciences: an International Journal
A novel multi-threshold segmentation approach based on differential evolution optimization
Expert Systems with Applications: An International Journal
A clustering method combining differential evolution with the K-means algorithm
Pattern Recognition Letters
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Improving differential evolution algorithm by synergizing different improvement mechanisms
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Color image segmentation using parallel OptiMUSIG activation function
Applied Soft Computing
Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies
Applied Soft Computing
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The multi-level image thresholding is often treated as a problem of optimization. Typically, finding the parameters of these problems leads to a nonlinear optimization problem, for which obtaining the solution is computationally expensive and time-consuming. In this paper a new multi-level image thresholding technique using synergetic differential evolution (SDE), an advanced version of differential evolution (DE), is proposed. SDE is a fusion of three algorithmic concepts proposed in modified versions of DE. It utilizes two criteria (1) entropy and (2) approximation of normalized histogram of an image by a mixture of Gaussian distribution to find the optimal thresholds. The experimental results show that SDE can make optimal thresholding applicable in case of multi-level thresholding and the performance is better than some other multi-level thresholding methods.