A comparison of nature inspired algorithms for multi-threshold image segmentation

► We present an empirical comparison of three state of the art meta-heuristics. ► Were used blood images to perform thresholding by a mixture of Gaussian functions. ► Compared algorithms were ABC, DE, PSO and Otsu used to perform multithreshold. ► Comparisons were made according to function evaluati...

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Veröffentlicht in:Expert systems with applications Jg. 40; H. 4; S. 1213 - 1219
Hauptverfasser: Osuna-Enciso, Valentı´n, Cuevas, Erik, Sossa, Humberto
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier Ltd 01.03.2013
Elsevier
Schlagworte:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Approximation
Artificial Bee Colony Optimization
Artificial intelligence
Automatic thresholding
Computer science; control theory; systems
Decision theory. Utility theory
Differential Evolution
Exact sciences and technology
Gaussian
Gaussian function sum
Image segmentation
Intelligent image processing
Linear inference, regression
Mathematics
Operational research and scientific management
Operational research. Management science
Optimization
Particle Swarm Optimization
Pattern recognition. Digital image processing. Computational geometry
Pixels
Probability and statistics
Sciences and techniques of general use
Segmentation
Statistics
Theoretical computing
Thresholds
Image segmentation
Artificial Bee Colony Optimization
Automatic thresholding
Gaussian function sum
Differential Evolution
Particle Swarm Optimization
Intelligent image processing
Mixed distribution
Histogram
Grey level image
Image processing
Threshold detection
Evolutionary algorithm
Particle swarm optimization
Multiple image
Statistical regression
Selection problem
Image analysis
Experimental result
Gaussian process
Biomimetics
Swarm intelligence
Threshold function
Pixel
Multiple classification
ISSN:0957-4174, 1873-6793
Online-Zugang:Volltext
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Zusammenfassung:► We present an empirical comparison of three state of the art meta-heuristics. ► Were used blood images to perform thresholding by a mixture of Gaussian functions. ► Compared algorithms were ABC, DE, PSO and Otsu used to perform multithreshold. ► Comparisons were made according to function evaluation and a quality measure. ► DE shows superior performance in convergence and in quality terms similar to ABC. In the field of image analysis, segmentation is one of the most important preprocessing steps. One way to achieve segmentation is by mean of threshold selection, where each pixel that belongs to a determined class is labeled according to the selected threshold, giving as a result pixel groups that share visual characteristics in the image. Several methods have been proposed in order to solve threshold selection problems; in this work, it is used the method based on the mixture of Gaussian functions to approximate the 1D histogram of a gray level image and whose parameters are calculated using three nature inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony Optimization and Differential Evolution). Each Gaussian function approximates the histogram, representing a pixel class and therefore a threshold point. Experimental results are shown, comparing in quantitative and qualitative fashion as well as the main advantages and drawbacks of each algorithm, applied to multi-threshold problem.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2012.08.017