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Image Segmentation: A Survey of Methods Based on Evolutionary Computation

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Simulated Evolution and Learning (SEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

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Abstract

Image segmentation is mainly used as a preprocessing step in problems of image processing and computer vision. Its performance has a great influence on subsequent tasks. Evolutionary Computation (EC) techniques have been introduced to the area of image segmentation due to their high search capacity. However, there are rarely comprehensive surveys on EC based image segmentation methods, which can enable researchers to get a quick understanding of this area and compare the existing methods. Therefore, this paper provides an overview of EC based image segmentation methods, and discusses the remaining issues in this area. It is observed that among all EC techniques, four of them (genetic algorithms, genetic programming, differential equation and partial swarm optimization) are more frequently used and GAs are the most popular technique. It is noted that low generalization capacity and computational complexity are two common problems in EC techniques applied to image segmentation.

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References

  1. Khan, W.: Image Segmentation Techniques: A Survey. Journal of Image and Graphics 1(4), 166–170 (2013)

    Google Scholar 

  2. Senthilkumaran, N., Rajesh, R.: Edge detection techniques for image segmentation a survey of soft computing approaches. International Journal of Recent Trends in Engineering 1(2), 250–254 (2009)

    Google Scholar 

  3. Bhandarkar, S.M., Zhang, H.: Image Segmentation Using Evolutionary Computation. IEEE Transactions on Evolutionary Computation, 1–21 (1999)

    Google Scholar 

  4. Banimelhem, O., Yahya, Y.A.: Multi-Thresholding Image Segmentation Algorithm Using Genetic Algorithm. In: World Congress in Computer Science, Computer Engineering, and Applied Computing (2011)

    Google Scholar 

  5. Al-Faris, Q.A., Ngah, U.K., Isa, N.A.M., Shuaib, I.L.: Breast MRI Tumour Segmentation using Modified Automatic Seeded Region Growing Based on Particle Swarm Optimization Image Clustering. In: Online Conference on Soft Computing in Industrial Applications Anywhere on Earth, Online Version, pp. 1–11 (2012)

    Google Scholar 

  6. Diazi, I., Branch, J., Boulancer, P.: A Genetic Algorithm to Segment Range Image by Edge Detection. In: International Conference on Industrial Electronics and Control Applications, pp. 7–14 (2005)

    Google Scholar 

  7. Shirakawa, S., Tomoharu, N.: Evolutionary image segmentation based on multiobjective clustering. In: IEEE Congress on Evolutionary Computation. IEEE (2009)

    Google Scholar 

  8. Bilotta, E., Cerasa, A., Pantano, P., Quattrone, A., Staino, A., Stramandinoli, F.: A CNN Based Algorithm for the Automated Segmentation of Multiple Sclerosis Lesions. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplicatons 2010, Part I. LNCS, vol. 6024, pp. 211–220. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  9. Payel, G., Melanie, M., James, T., Arthur, H.: A Genetic Algorithm-based Algorithm-Level Set Set Curve Evolution for Prostate Segmentation on Pelvic CT and MRI Images. In: Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques, pp. 127–149. IGI Global (2010)

    Google Scholar 

  10. Riccardo, P.: Genetic programming for feature detection and image segmentation. In: Fogarty, T.C. (ed.) AISB-WS 1996. LNCS, vol. 1143, Springer, Heidelberg (1996)

    Google Scholar 

  11. Song, A., Ciesielski, V.: Fast Texture Segmentation using Genetic Programming. In: The 2003 Congress on Evolutionary Computation, pp. 2126–2133. IEEE (2003)

    Google Scholar 

  12. Singh, T., Nawwaf, K., Mohmmad, D., Rabab, W.: Genetic Programming based Image Segmentation with Applications to Biomedical Object Detection. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 1123–1130. ACM (2009)

    Google Scholar 

  13. Roberts, M.E.: The Effectiveness of Cost Based Subtree Caching Mechanisms in Typed Genetic Programming for Image Segmentation. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, pp. 444–454. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  14. Brumby, P., Theiler, P., Perkins, J., Harvey, R., Szymanski, J., Bloch, J.: Investigation of Image Feature Extraction by a Genetic Algorithm. In: Proceedings of SPIE, pp. 24–31 (1999)

    Google Scholar 

  15. Zhang, J., Zhan, Z.H., Lin, Y., Chen, N., Gong, Y.J., Zhong, J.H., Shi, Y.H.: Evolutionary Computation Meets Machine Learning: A Survey. IEEE Computational Intelligence Magazine 6(4), 68–75 (2011)

    Article  MATH  Google Scholar 

  16. Evolutionary Algorithm, http://en.wikipedia.org/wiki/Evolutionary_algorithm

  17. Kanungo, P., Nanda, P.K., Samal, U.C.: Image segmentation using thresholding and genetic algorithm (2006)

    Google Scholar 

  18. Duraisamy, S.P., Kayalvizhi, R.: A New Multilevel Thresholding Method Using Swarm Intelligence Algorithm for Image Segmentation. Journal of Intelligent Learning Systems and Applications 2(03), 126 (2010)

    Article  Google Scholar 

  19. Pei, Z., Zhao, Y., Liu, Z.: Image segmentation based on Differential Evolution algorithm. In: International Conference on Image Analysis and Signal Processing, IASP 2009. IEEE Press (2009)

    Google Scholar 

  20. Kaganami, H.G., Beij, Z.: Region Based Detection versus Edge Detection. IEEE Transactions on Intelligent Information Hiding and Multimedia Signal Processing, 1217–1221 (2009)

    Google Scholar 

  21. Saha, I., Maulik, U., Bandyopadhyay, S.: An Improved Multi-objective Technique for Fuzzy Clustering with Application to IRS Image Segmentation. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., Machado, P. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 426–431. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  22. Maulik, U., Bandyopadhyay, S.: Fuzzy Partitioning using a Real-coded Variable-length Genetic Algorithm for Pixel Classification. IEEE Transaction on Geoscience and Remote Sending 41(5), 1075–1081 (2003)

    Article  Google Scholar 

  23. Bandyopadhyay, S., Maulik, U., Anirban, M.: Multiobjective genetic clustering for pixel classification in remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing 45(5), 1506–1511 (2007)

    Article  Google Scholar 

  24. Jiang, X., Zhang, R., Nie, S.: Image Segmentation Based on PDEs Model: a Survey. In: 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, pp. 1–4 (2009)

    Google Scholar 

  25. Chan, T.F., Shen, J., Vese, L.: Variational PDE models in image processing. Notices of AMS 50(1), 14–26 (2003)

    MATH  MathSciNet  Google Scholar 

  26. De Jong, K.A.: An analysis of the behavior of a class of genetic adaptive systems. Dissertation Abstracts International. 36(10) (1975)

    Google Scholar 

  27. Prewitt, J., Mendelsohn, M.L.: The analysis of cell images. Annals of the New York Academy of Sciences 128(3), 1035–1053 (1966)

    Article  Google Scholar 

  28. Jiao, L.: Evolutionary-based image segmentation methods. Image Segmentation (10), 180–224 (2011)

    Google Scholar 

  29. RIDER Breast MRI. National Biomedical Imaging Archive (NBIA), U.o. Michigan, Editor 2007, U.S. National Cancer Institute (2011)

    Google Scholar 

  30. Senthilkumaran, N., Rajesh, R.: Edge Detection Techniques for Image Segmentation-A Survey of Soft Computing Approaches. International Journal of Recent Trends in Engineering 1(2), 250–254 (2009)

    Google Scholar 

  31. Pathegama, M., Göl, Ö.: Edge-end pixel extraction for edge-based image segmentation. Transactions on Engineering, Computing and Technology 2, 213–216 (2004)

    Google Scholar 

  32. Drio, O., Raul, F.: Liver Segmentation using Level Sets and Genetic Algorithms. In: Fourth International Conference on Computer Vision Theory and Applications, pp. 154–159 (2009)

    Google Scholar 

  33. Roula, A., Bouridane, A., Kurugollu, F.: An evolutionary snake algorithm for the segmentation of nuclei in histopathological images. In: 2004 International Conference on Image Processing. IEEE (2004)

    Google Scholar 

  34. Cruz-Aceves, I., Avina-Cervantes, G., Lopez-Hernandez, M., Rostro-Gonzalez, H., Garcia-Capulin, H., Torres-Cisneros, M., Guzman-Cabrera, R.: Multiple Active Contours guided by Differential evolution for Medical image segmentation. In: Computational and Mathematical Methods in Medicine (2013)

    Google Scholar 

  35. Wang, K., Guo, Q., Zhuang, D., Chu, H., Fu, B.: Application of Snake Model based on PSO in The Image Segmentation. In: The Sixth World Congress on Intelligent Control and Automation, WCICA 2006, pp. 9637–9640 (2006)

    Google Scholar 

  36. Koza, J.: Genetic Programming as a Means for Programming Computers by Natural Selection. Statistics and Computing Journal (1993)

    Google Scholar 

  37. Tackett, W.A.: Genetic Programming for Feature Discovery and Image Discrimination. In: ICGA, pp. 303–311 (1993)

    Google Scholar 

  38. Belpaeme, T.: Evolving visual feature detectors. In: Floreano, D., Mondada, F. (eds.) ECAL 1999. LNCS, vol. 1674, pp. 266–270. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  39. Eshatian, K., Hang, M., Ndreae, P.: A Filter Approach to Multiple Feature Construction for Symbolic Learning Classifiers using Genetic Programming. IEEE Transactions on Evolutionary Computation 16(5), 645–661 (2012)

    Article  Google Scholar 

  40. Smart, W., Zhang, M.: Classification Strategies for Image Classification in Genetic Programming. In: Proceeding of Image and Vision Computing Conference, pp. 402–407. Palmerston North, New Zealand (2003)

    Google Scholar 

  41. Jabeen, H., Baig, R.: Review of classification using genetic programming. International Journal of Engineering Science and Technology 2(2), 94–103 (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, P.O. Box 600, Wellington, 6140, New Zealand

    Yuyu Liang, Mengjie Zhang & Will N. Browne

Authors
  1. Yuyu Liang
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  2. Mengjie Zhang
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  3. Will N. Browne
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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Liang, Y., Zhang, M., Browne, W.N. (2014). Image Segmentation: A Survey of Methods Based on Evolutionary Computation. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_71

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_71

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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