A two-dimensional image segmentation method based on genetic algorithm and entropy

Abstract Thresholding is a well-known technique for digital image segmentation. A growing number of contributions achieved the thresholding value by maximizing some information theory functions such as entropies. The classical techniques search for the thresholding value by formulating the entropy upon the ordered image gray level distribution. This ordering step does not allow to converge enough to the entropy optimum. In this paper, we propose a novel tow-dimensional image segmentation approach based on the flexible representation of Tsallis and Renyi entropies and employing the Genetic Algorithm (GA). From the information theory point of view, the entropy is used here to measure the amount of information contained in the two-dimensional histogram of the image. The GA is then used to maximize the entropy in order to segment efficiently the image into object and background. The experimental results show that our approach maximizes efficiently the entropy and generates better image segmentation quality compared to the classical thresholding technique.

[1]  A. Rényi.  On Measures of Entropy and Information , 1961 .

[2]  A. Abutaleb,et al.  Automatic Thresholding Of Gray-Level Pictures Using 2-D Entropy , 1988, Optics & Photonics.

[3]  Megha Sahu,et al.  Color Image Segmentation using Genetic Algorithm , 2016 .

[4]  R. Kayalvizhi,et al.  Optimal multilevel thresholding using bacterial foraging algorithm , 2011, Expert Syst. Appl..

[5]  Yun-Chia Liang,et al.  Application of a Hybrid Ant Colony Optimization for the Multilevel Thresholding in Image Processing , 2006, ICONIP.

[6]  C. Tsallis.  Possible generalization of Boltzmann-Gibbs statistics , 1988 .

[7]  Jun-yi Li,et al.  Artificial Bee Colony Optimizer with Bee-to-Bee Communication and Multipopulation Coevolution for Multilevel Threshold Image Segmentation , 2015 .

[8]  S. Lakshmi,et al.  IJCA Special Issue on “Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications” CASCT, 2010. A study of Edge Detection Techniques for Segmentation Computing Approaches , 2022 .

[9]  Jiangbo Li,et al.  Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods , 2013 .

[10]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[11]  Sayed Abdel-Khalek,et al.  Generalized α-Entropy Based Medical Image Segmentation , 2014 .

[12]  Erik Valdemar Cuevas Jiménez,et al.  A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation , 2013, Expert Syst. Appl..

[13]  Márcio Portes de Albuquerque,et al.  Image thresholding using Tsallis entropy , 2004, Pattern Recognit. Lett..

[14]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[15]  Prasanna K. Sahoo,et al.  Image thresholding using two-dimensional Tsallis-Havrda-Charvát entropy , 2006, Pattern Recognit. Lett..

[16]  Jiliu Zhou,et al.  An Improved Quantum-Inspired Genetic Algorithm for Image Multilevel Thresholding Segmentation , 2014 .

[17]  Ying Lin,et al.  Image segmentation by multi-threshold based on Fisher function and histogram algorithm , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[18]  Erik Valdemar Cuevas Jiménez,et al.  A novel multi-threshold segmentation approach based on differential evolution optimization , 2010, Expert Syst. Appl..

[19]  Patrick Siarry,et al.  A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation , 2008, Comput. Vis. Image Underst..

[20]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[21]  Muhammed Fatih Talu,et al.  Segmentation of SAR images using improved artificial bee colony algorithm and neutrosophic set , 2014, Appl. Soft Comput..

[22]  Gamil Abdel-Azim,et al.  A Novel Approach for MRI Brain Images Segmentation , 2013 .

[23]  Carlos A. B. Mello,et al.  Thresholding Images of Historical Documents Using a Tsallis-Entropy Based Algorithm , 2008, J. Softw..

[24]  Claude E. Shannon,et al.  The mathematical theory of communication , 1950 .

[25]  Suchendra M. Bhandarkar,et al.  An edge detection technique using genetic algorithm-based optimization , 1994, Pattern Recognit..

[26]  Jon Atli Benediktsson,et al.  An efficient method for segmentation of images based on fractional calculus and natural selection , 2012, Expert Syst. Appl..

[27]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..

[28]  A. P. Singh,et al.  Edge Detection in Gray Level Images based on the Shannon Entropy , 2008 .

[29]  Mohamed A. El-Sayed,et al.  Novel Approach of Edges Detection for Digital Images Based On Hybrid Types of Entropy , 2013 .

[30]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[31]  Gamil Abdel-Azim,et al.  An Improved Image Segmentation Algorithm Based on MET Method , 2012 .

[32]  Charles C. Brunner,et al.  Image segmentation algorithms applied to wood defect detection , 2003 .

[33]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[34]  Gurdial Arora,et al.  A thresholding method based on two-dimensional Renyi's entropy , 2004, Pattern Recognit..