A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast level set method for propagating interfaces
Journal of Computational Physics
A fast iterative scheme for multilevel thresholding methods
Signal Processing
On the Topological Derivative in Shape Optimization
SIAM Journal on Control and Optimization
A new dichotomization technique to multilevel thresholding devoted to inspection applications
Pattern Recognition Letters
A PDE-based fast local level set method
Journal of Computational Physics
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
A level set algorithm for minimizing the Mumford-Shah functional in image processing
VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
Fast image segmentation based on multi-resolution analysis and wavelets
Pattern Recognition Letters
Incorporating topological derivatives into level set methods
Journal of Computational Physics
Image segmentation by histogram thresholding using hierarchical cluster analysis
Pattern Recognition Letters
Automatic thresholding for defect detection
Pattern Recognition Letters
Computer Vision and Image Understanding
An efficient iterative algorithm for image thresholding
Pattern Recognition Letters
Engineering Applications of Artificial Intelligence
Dynamic Measurement of Computer Generated Image Segmentations
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
A new criterion for automatic multilevel thresholding
IEEE Transactions on Image Processing
Expert Systems with Applications: An International Journal
Multi-level image thresholding by synergetic differential evolution
Applied Soft Computing
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For the image segmentation by the histogram bilevel thresholding, several methods have been proposed. However, they are computationally time consuming and their effectiveness is reduced when applied to a complex image and when the number of the different regions composing this image is high. In this paper, a fast and efficient method for segmenting complex images is proposed. This method is based on the determination of the number and the values of the thresholds required for the segmentation by introducing a new multilevel thresholding technique using a multiphase level set technique. First, the gray-level histogram of the image is approximated by a weighted sum of Heaviside functions by using the Chan-Vese segmentation model. In order to obtain a better approximation of this histogram and to speed up the calculations, an improved version of the multiphase level set method is introduced. The valleys are then highlighted and isolated by deriving the approximated histogram so that the thresholds are easily extracted by searching the minima of these valleys. Experimental results and a comparative study with three other efficient and known multilevel thresholding methods over synthetic and real images have shown that the proposed method offers very good segmentation results with a low computing time, whatever the complexity of the image and the number of regions composing it.