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

Valentín Osuna-Enciso, Erik Cuevas, Humberto Sossa

Research output: Contribution to journalArticle

94 Citations (Scopus)

Abstract

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. © 2012 Elsevier Ltd. All rights reserved.
Original languageAmerican English
Pages (from-to)1213-1219
Number of pages1091
JournalExpert Systems with Applications
DOIs
StatePublished - 1 Mar 2013

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Image segmentation
Pixels
Particle swarm optimization (PSO)
Image analysis

Cite this

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A comparison of nature inspired algorithms for multi-threshold image segmentation. / Osuna-Enciso, Valentín; Cuevas, Erik; Sossa, Humberto.

In: Expert Systems with Applications, 01.03.2013, p. 1213-1219.

Research output: Contribution to journalArticle

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