Skip to main content
SpringerLink
Log in

A Review of Bio-inspired Algorithms as Image Processing Techniques

  • Conference paper
Software Engineering and Computer Systems (ICSECS 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 179))

Included in the following conference series:

Abstract

This paper reviews 80 out of 130 bio-inspired Algorithm (BIAs) researches published in google scholar and IEEExplore between the periods of 1995 to 2010 used to solve image processing problems. BIAs has been successfully applied in many sciences, medical and engineering fields. The evolving, dynamic and meta-heuristics nature of BIAs makes it more robust, accurate and efficient in solving image processing problems. However finding the appropriate BIAs that matches the problem at hand is a tedious and difficult task. The BIAs investigated in this study are Genetic Algorithms, Evolutionary strategies, Genetic programming, memetic algorithms, swarm intelligence and artificial immune system. The publications are categorized by year of publication, by specific BIAs and by application. The statistics shows exponential increases in the application of BIAs to solve image processing problems and some algorithms have yet to be explored.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Sbalzarini, I.F., Müller, S., Koumoutsakos, P.: Multiobjective optimization using evolutionary algorithms. In: Proceedings of the CTR Summer Program 2000, Center for Turbulence Research, Stanford University, Stanford (2000)

    Google Scholar 

  2. Howse, J., Stapleton, G., Taylor, J.: Spider Diagrams, London Mathematical Society. LMS J. Compt. Math. 8, 145–194 (2005) ISSN 1461-1570

    Article  MathSciNet  MATH  Google Scholar 

  3. Jones, G.: Genetic and evolutionary algorithms. In: von Rague, P. (ed.) Encyclopedia of Computational Chemistry. John Wiley and Sons, Chichester (1998)

    Google Scholar 

  4. Pignalberi, G., Cucchiara, R., Cinque, L., Levialdi, S.: Tuning range segmentation by genetic algorithm. EURASIP Journal Appl. Sig. Proc. 8, 780–790 (2003)

    Article  MATH  Google Scholar 

  5. Lee, Z.J., Su, S.F., Lee, C.Y.: Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics. IEEE Trans. Syst., Man Cybernet. Part B Cybernet. 33, 113–121 (2003)

    Article  Google Scholar 

  6. Ghosh, P., Melanie, M.: Prostate segmentation on pelvic CT images using a genetic algorithm. In: Proceedings of the SPIE on Medical Imaging 2008: Image Processing, vol. 6914, pp. 691442–691442-8 (2008)

    Google Scholar 

  7. Talbi, H., Batouche, M., Draa, A.: A Quantum-Inspired Evolutionary Algorithm for Multiobjective Image Segmentation. World Academy of Science, Engineering and Technology 31 (2007)

    Google Scholar 

  8. Huang, C.F., Rocha, L.M.: A systematic study of genetic algorithms with genotype editing. In: Proc. of 2004 Genetic and Evolutionary Computation Conference, vol. 1, pp. 1233–1245 (2004)

    Google Scholar 

  9. Yuan, X., Zouridakis, G., Situ, N.: Automatic Segmentation of Skin Lesion Images Using Evolution Strategies. Preprint submitted to Elsevier (2008)

    Google Scholar 

  10. Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming. Lulu Enterprises, UK (2008) ISBN 978-1-4092-0073-4

    Google Scholar 

  11. Corno, F., Reorda, M.S., Squillero, G.: Exploiting the Selfish Gene Algorithm for Evolving Cellular Automata. In: IJCNN 2000: IEEE-INNS-ENNS International Joint Conference Neural Networks, Como., vol. (I), pp. 577–581 (2000)

    Google Scholar 

  12. Vasiliauskas, A.: Selfish Gene Algorithm (2008), http://coding-experiments.blogspot.com/2008/04/selfish-gene-algorithm.html

  13. Garg, P.: A Comparison between Memetic algorithm and Genetic algorithm for the cryptanalysis of Simplified Data Encryption Standard algorithm. International Journal of Network Security & Its Applications (IJNSA) 1(1) (2009)

    Google Scholar 

  14. El-Mihoub, T.A., Hopgood, A.A., Nolle, L., Battersby, A.: Hybrid Genetic Algorithms: A Review. Engineering Letters 13(2), EL_13_2_11 (2006)

    Google Scholar 

  15. Premalatha, K., Natarajan, A.M.: Hybrid PSO and GA for Global Maximization. Int. J. Open Problems Compt. Math. 2(4) (2009); ISSN 1998-6262, Copyright © ICSRS Publication

    Google Scholar 

  16. Brumby, S.P., Theiler, J., Perkins, S.J., Harvey, N.R., Szymanski, J.J., Bloch, J.J., Mitchell, M.: Investigation of image feature extraction by a genetic algorithm. In: Proc. SPIE, vol. 3812, pp. 24–31 (1999)

    Google Scholar 

  17. Brumby, S.P., Davis, A.B., Harvey, N.R., Rohde, C.A., Hirsch, K.L.: Genetic refinement of cloud-masking algorithms for the multi-spectral thermal imager (MTI). In: Proc. IGARSS, vol. 3, pp. 1152–1154 (2001)

    Google Scholar 

  18. Gunn, S.R., Nixon, M.S.: Snake Head Boundary Extraction using Local and Global Energy Minimisation. In: Proc. IEEE Int. Conf. on Pattern Recognition, Vienna, Austria, pp. 581–585 (1996)

    Google Scholar 

  19. Yen, G.G., Nithianandan, N.: Facial Feature Extraction Using Genetic Algorithm. In: Proceedings of the IEEE 2002 Congress on Evolutionary Computation, Honolulu, USA, vol. 2, pp. 1895–1900 (2002)

    Google Scholar 

  20. Radtke, P.V.W., Wong, T., Sabourin, R.: A Multi-objective Memetic Algorithm for Intelligent Feature Extraction. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 767–781. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  21. Ghosh, P., Melanie, M.: Segmentation of Medical Images Using a Genetic Algorithm. In: GECCO 2006, Seattle, Washington, USA (2006); Copyright ACM 1-59593-186-4/06/0007

    Google Scholar 

  22. Ganesan, R., Radhakrishnan, S.: Segmentation of Computed Tomography Brain Images using Genetic Algorithm. International Journal of Soft Computing 4, 157–161 (2009)

    Google Scholar 

  23. Zhang, J., Yuan, X., Buckles, B.P.: A fast evolution strategies-based approach to image registration. In: Genetic and Evolutionary Computation Conference, New York (2002)

    Google Scholar 

  24. Cordon, O., Damas, S., Santamaria, J.: A practical review on the applicability of different evolutionary algorithms to 3D feature-based image registration. In: Genetic and Evolutionary Computation for Image Processing and Analysis, p. 241 (2009)

    Google Scholar 

  25. Munteanu, C., Rosa, A.: Color image enhancement using evolutionary principles and the retinex theory of color constancy. In: Proceedings IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing XI, pp. 393–402 (2001)

    Google Scholar 

  26. Wetcharaporn, W., Chaiyaratana, N., Huvanandana, S.: Enhancement of an Automatic Fingerprint Identification System Using a Genetic Algorithm and Genetic Programming. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 368–379. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  27. Paulinas, M., Usinskas, A.: A Survey of Genetic Algorithms Applicatons for Image Enhancement And Segmentation. Information Technology and Control 36(3), 278–284 (2007)

    Google Scholar 

  28. Harvey, N.R., Theiler, J., Brumby, S.P., Perkins, S., Szymanski, J.J., Bloch, J.J., Porter, R.B., Galassi, M., And Young, A.C.: Comparison Of GENIE And Conventional Supervised Classifiers For Multispectral Image Feature Extraction. IEEE Transactions on Geoscience and Remote Sensing 40(2) (February 2002)

    Google Scholar 

  29. Mohammad, D.: Multi Local Feature Selection Using Genetic Algorithm For Face Identification. International Journal of Image Processing 1(2), 1–10 (2007)

    Google Scholar 

  30. Ammar, H.H., Tao, Y.: Fingerprint Registration Using Genetic Algorithms. In: Proceedings of 3rd IEEE Symposium on Application-Specific Systems and Software Engineering Technology, pp. 148–154 (2000)

    Google Scholar 

  31. Yuizono T., Wang Y., Satoh K., Nakayama S.: Study On Individual Recognition For Ear Images By Using Genetic Local Search. In: Proceeding Congress Evolutionary Computation, pp. 237-242 (2002)

    Google Scholar 

  32. Maludrottu, S., Sallam, H., Regazzoni, C.S.: Sparse Shapes Prototype Modeling Using Genetic Algorithms. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 978–971 (2010) ISSN: 1522-4880, E-ISBN: 978-1-4244-7993-1, Print ISBN: 978-1-4244-7992-4

    Google Scholar 

  33. Ninot, J., Smadja, L., And Heggarty, K.: Road Sign Recognition Using A Hybrid Evolutionary Algorithm And Primitive Fusion. In: Paparoditis, N., Pierrot-Deseilligny, M., Mallet, C., Tournaire, O. (eds.) IAPRS, Saint-Mandé, France, September 1-3, Part 3A, vol. XXXVIII (2010)

    Google Scholar 

  34. Jabeen, H., And Baig, A.R.: Review of Classification Using Genetic Programming. International Journal of Engineering Science and Technology 2(2), 94–103 (2010)

    Google Scholar 

  35. Trujillo, L., Legrand, P., Olague, G., Pérez, C.B.: Optimization of the hölder image descriptor using a genetic algorithm. In: GECCO 2010: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, t Portland, Oregon, USA, pp. 1147–1154 (2010)

    Google Scholar 

  36. Downey, C., Zhang, M., Browne, W.N.: New crossover operators in linear genetic programming for multiclass object classification. In: GECCO 2010: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, Portland, Oregon, USA, pp. 885–892 (2010)

    Google Scholar 

  37. Sri Rama Krishna, K., Reddy, A.G., Giri Prasad, M.N.: Chandrabushan Rao K. & Madhavi M.: Genetic Algorithm Processor for Image Noise Filtering Using Evolvable Hardware. International Journal of Image Processing 4(3) (2010)

    Google Scholar 

  38. Venkatesan, S., Madane, S.S.R.: Experimental Research on Identification of Face in a Multifaceted Condition with Enhanced Genetic and ANT Colony Optimization Algorithm. International Journal of Innovation, Management and Technology 1(5) (2010) ISSN: 2010-0248

    Google Scholar 

  39. Goranin, N., Cenys, A.: Evolutionary Algorithms Application Analysis in Biometric Systems. Journal of Engineering Science and Technology Review 3(1), 70–79 (2010)

    Google Scholar 

  40. Miller, J.F., Smith, S.L., Zhang, Y.: Detection of microcalcifications in mammograms using multi-chromosome cartesian genetic programming. In: GECCO 2010: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, Portland, Oregon, USA, pp. 1923–1930 (2010)

    Google Scholar 

  41. Ghosh, P., Mitchell, M., Gold, J.: LSGA: combining level-sets and genetic algorithms for segmentation. Evolutionary Intelligence 3(1) (2010)

    Google Scholar 

  42. Aljuaid, H., Muhammad, Z., Sarfraz, M.: A Tool to Develop Arabic Handwriting Recognition System Using Genetic Approach. Journal of Computer Science 6(5), 490–495 (2010) ISSN 1549-3636

    Google Scholar 

  43. Kharrat, A., Gasmi, K., Messaoud, M.B., Benamrane, N., And Abid, M.: A Hybrid Approach for Automatic Classification of Brain MRI Using Genetic Algorithm and Support Vector Machine. Leonardo Journal of Sciences (17), 71–82 (2010) ISSN 1583-0233

    Google Scholar 

  44. Ramos, V.: The Biological Concept of Neoteny in Evolutionary Colour Image Segmentation - Simple Experiments in Simple Non-Memetic Genetic Algorithms. In: Applications of Evolutionary Computation. LNCS, Springer, Heidelberg (2010)

    Google Scholar 

  45. Pedrino, E.C., Saito, J.H., Roda, V.O.: A Genetic Programming Approach to Reconfigure a Morphological Image Processing Architecture. Hindawi Publishing Corporation International Journal of Reconfigurable Computing 2011, Article ID 712494, 10 (2010) doi:10.1155/2011/712494

    Google Scholar 

  46. Cattani, P.T., Johnson, C.G.: Typed cartesian genetic programming for image classification. In: Proceedings of the 2009 UK Workshop on Computational Intelligence, University of Nottingham, pp. 106–111 (September 2009)

    Google Scholar 

  47. Santamaria, J., Cordon, O., Damas, S., Garcia-Torres, J.M., Quirin, A.: Performance evaluation of memetic approaches in 3D reconstruction of forensic objects. Soft. Computing 13(8-9), 883–904 (2009)

    Article  Google Scholar 

  48. Charbuillet, C., Gas, B., Chetouani, M., Zarader, J.L.: Optimizing Feature Complementarity by Evolution Strategy: Application to Automatic SpeakerVerification Université Pierre et Marie Curie-Paris6, UMR 7222 CNRS, Institut des Syst‘emes Intelligents et Robotique (ISIR), Ivry sur Seine, F-94200 France (2009)

    Google Scholar 

  49. Singh, T., Kharma, N., Daoud, M., Ward, R.: Genetic Programming Based Image Segmentation with Applications to Biomedical Object Detection. In: GECCO 2009: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation Montréal, Québec, Canada (2009) Copyright ACM 978-1-60558-325-9

    Google Scholar 

  50. Anam, S., Islam, M.S., Kashem, M.A., Islam, M.N., Islam, M.R., Islam, M.S.: Face Recognition Using Genetic Algorithm and Back Propagation Neural Network. In: Proceedings of the International MultiConference of Engineers and Computer Scientists 2009, IMECS, Hong Kong, March 18–20, vol. I (2009)

    Google Scholar 

  51. Kowaliw, T., Banzhaf, W., Kharma, N., Harding, S.: Evolving novel image features using genetic programming-based image transforms. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2009), pp. 2502–2507 (2009)

    Google Scholar 

  52. Ebner, M.: Engineering of computer vision algorithms using evolutionary algorithms. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) Advanced Concepts for Intelligent Vision Systems, Bordeaux, France, pp. 367–378. Springer, Berlin (2009)

    Chapter  Google Scholar 

  53. 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) (May 2009)

    Google Scholar 

  54. Chen, X., Liu, X., Jia, Y.: Combining evolution strategy and gradient descent method for discriminative learning of bayesian classifiers. In: Proceeding GECCO 2009 Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (2009) ISBN: 978-1-60558-325-9

    Google Scholar 

  55. Hemanth, D.J., Vijila, C.K.S., Anitha, J.: A Survey On Artificial Intelligence Based Brain Pathology Identification Techniques In Magnetic Resonance Images. International Journal of Reviews in Computing (2009)

    Google Scholar 

  56. Li, Y.: Vehicle extraction using histogram and genetic algorithm based fuzzy image segmentation from high resolution UAV aerial imagery. In: IAPRS, vol. XXXVII, part B3b, pp. 529–534 (2008)

    Google Scholar 

  57. Seixas, F.L., Ochi, L.S., Conci, A., Saade, D.M.: Image registration using genetic algorithm. In: GECCO 2008: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (2008)

    Google Scholar 

  58. Harding, S., Banzhaf, W.: Genetic programming on gpus for image processing. In: Proceedings of the First International Workshop on Parallel and Bioinspired Algorithms (WPABA 2008), Toronto, Canada, pp. 65–72. Complutense University of Madrid Press, Madrid (2008)

    Google Scholar 

  59. Kadar, I., Ben-Shaharv, O., Sipper, M.: Evolving boundary detectors for natural images via genetic programming. In: Proceedings of the 19th Internation Conference on Pattern Recognition (2008)

    Google Scholar 

  60. Lu, X., Zhou, J.: Applications of Evolutionary Programming in Markov Random Field to IR Image Segmentation. In: Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Xi’an, China (2008)

    Google Scholar 

  61. Song, A., Ciesielski, V.: Texture segmentation by genetic programming. Evolutionary Computation 16(4), 461–481 (2008)

    Article  Google Scholar 

  62. Trujillo, L., Olague, G.: Automated Design of Image Operators that Detect Interest Points. Evolutionary Computation 16(4), 483–507 (2008)

    Article  Google Scholar 

  63. Ciesielski, V., Song, A., Lam, B.: Visual Texture Classification and Segmentation by Genetic Programming. In: Ciesielski, V., Song, A., Lam, B., Cagnoni, Lutton, Olague (eds.) Genetic and Evolutionary Image Processing and Analysis. Hindawi Publishing Corporation (2007)

    Google Scholar 

  64. Braik, M., Sheta, A., Ayesh, A.: Image Enhancement Using Particle Swarm Optimization. In: Proceedings of the World Congress on Engineering, WCE 2007, London, U.K, July 2-4, vol. I (2007)

    Google Scholar 

  65. Wijesinghe, G., Ciesielski, V.: Using restricted loops in genetic programming for image classification. In: Proc. IEEE Congr. Evol. Comput., pp. 4569–4576. IEEE, Singapore (2007)

    Google Scholar 

  66. Espejo, P., Ventura, S., Herrera, F.: A Survey on the Application of Genetic Programming to Classification. IEEE Transactions on Systems, Man and Cybernetics 40(2), 121–144 (2010)

    Article  Google Scholar 

  67. Sheng, W., Howells, G., Fairhurst, M., Deravi, F.: A memetic fingerprint matching algorithm. IEEE Transactions on Information Forensics and Security 2(3), 402–412 (2007)

    Article  Google Scholar 

  68. Kucukural, A., Yeniterzi, R., Yeniterzi, A., Sezerman, O.U.: Evolutionary Selection of Minimum Number of Features for Classification of Gene Expression Data Using Genetic Algorithms. In: GECCO 2007, London, England, United Kingdom (2007); Copyright ACM 978-1-59593-697-4/07/0007

    Google Scholar 

  69. Imam, M.H.: An Extremely Simple Operation For Drastic Performance Enhancement Of Genetic Algorithms For Engineering Design Optimization. International Journal of Engineering Science and Technology 2(11), 6630–6645 (2010)

    Google Scholar 

  70. Pérez, O., Patricio, M.A., García, J., Molina, J.M.: Improving the segmentation stage of a pedestrian tracking video-based system by means of evolution strategies. In: 8th European Workshop on Evolutionary Computation in Image Analysis and Signal Processing. EvoIASP, Budapest, Hungary (April 2006)

    Google Scholar 

  71. Zhang, Y., Rockett, P.I.: A generic optimal feature extraction method using multiobjective genetic programming: Methodology and applications. Submitted to IEEE Transactions on Knowledge and Data Engineering (2006)

    Google Scholar 

  72. Quintana, M.I., Poli, R., Claridge, E.: Morphological algorithm design for binary images using genetic programming. Genetic Programming and Evolvable Machines 7(1), 81–102 (2006) ISSN 1389-2576

    Article  Google Scholar 

  73. Ji, Z., Dasgupta, D., Yang, Z., Teng, H.: Analysis of Dental Images using Artificial Immune Systems. In: IEEE Congress of Evolutionary Computation (CEC), Vancouver, BC, Canada (2006)

    Google Scholar 

  74. Su, L., Liu, X., Wang, X., Jiang, N.: Dimensional Reduction In Hyperspectral Images By Danger Theory Based Artificial Immune System. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Beijing, vol. XXXVII, Part B7 (2008)

    Google Scholar 

  75. Jackson, J.T., Gunsch, G.H., Claypoole, R.L., Lamont, G.B.: Blind Steganography Detection Using a Computational Immune System Approach. A Proposal Work in Progress International Journal of Digital Evidence (Winter 2002)

    Google Scholar 

  76. Zheng, H., Li, L.: An Artificial Immune Approach for Vehicle Detection from High Resolution Space Imagery. IJCSNS International Journal of Computer Science and Network Security 7(2) (February 2007)

    Google Scholar 

  77. Wachowiak, M.P., Smolíková, R., Zheng, Y., Zurada, J.M., Elmaghraby, A.S.: An Approach to Multimodal Biomedical Image Registration Utilizing Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation 8(3), 289 (2004)

    Article  Google Scholar 

  78. Kundra, E.H., Panchal, V.K., Singh, K., Kaura, H., Arora, S.: Extraction of Satellite Image using Particle Swarm Optimization. International Journal of Engineering (IJE) 4(1) (2010)

    Google Scholar 

  79. Kwok, N.M., Ha, Q.P., Liu, D.K., Fang, G.: Intensity-Preserving Contrast Enhancement for Gray-Level Images using Multi-objective Particle Swarm Optimization. In: Proceeding of the IEEE International Conference on Automation Science and Engineering, Shanghai, China, October 7-10 (2006)

    Google Scholar 

  80. Wang, C.M., Kuo, C.T., Lin, C.Y., Chang, G.H.: Application of Artificial Immune System Approach in MRI Classification. EURASIP Journal on Advances in Signal Processing, Article ID 547684, 8 (2008)

    Google Scholar 

  81. Corno, F., Reorda, M.S., Squillero, G.: A New Evolutionary Algorithm Inspired by the Selfish Gene Theory. In: Proceedings of the ACM Symposium on Applied Computing, San Antonio, Texas, United States, pp. 333–338 (1998) ISBN:1-58113-086-4

    Google Scholar 

  82. Das, S., Abraham, A., Konar, A.: Spatial Information Based Image Segmentation Using a Modified Particle Swarm Optimization Algorithm. In: Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications, vol. 02, pp. 438–444 (2006) ISBN:0-7695-2528-8

    Google Scholar 

  83. Eiben, A.E., Smith, J.E.: What is an evolutionary algorithm? In: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  84. Kanungo, P., Nanda, P.K., Samal, U.C.: Image Segmentation Using Thresholding and Genetic Algorithm. In: Proceedings of the Conference on Soft Computing Technique for Engineering Applications, Rourkela, India, pp. 24–26 (2006)

    Google Scholar 

  85. Foon, D.W., Mandava, R., Ramachandram, D.: Deformable Boundary initialization for object Detection in Natural Images Using Multiple Scale Edges, Computer Science Postgraduate Colloquium, School of Computer Sciences, Universiti Sains Malaysia (USM), Penang (2004)

    Google Scholar 

  86. Ibrahim, S., Abdul Khalid, N.E., Manaf, M.: Particle Swarm Optimization – Brain Abnormalities Segmentation. In: International Conference on Robotics, Vision, Information and Signal Processing, ROVISP 2009, Langkawi, Malaysia (2009)

    Google Scholar 

  87. Ooi, T.H., Ngah, U.K., Abd. Khalid, N.E., Venkatachalam, P.A.: Mammographic Calcification Clusters Using The Region Growing Technique. In: New Millenium International Conference on Pattern Recognition, Image Processing and Robot Vision (PRIPOV 2000), pp. 157–163. Terengganu Advanced Technical Institute (TATI), Terengganu (2000)

    Google Scholar 

  88. Ji, Z., Dasgupta, D., Yang, Z., Teng, H.: Analysis of dental images using artificial immune systems. In: Proceedings of Congress on Evolutionary Computation (CEC), pp. 528–535. IEEE Press, Los Alamitos (2006)

    Google Scholar 

  89. Zhang, Y.: Multiobjective genetic programming optimal search for feature extraction. Ph.D. thesis, University of Sheffield (2006)

    Google Scholar 

  90. Afifi, A., Nakaguchi, T., Tsumura, N., Iyake, Y.: 2Shape and Texture Priors for Liver Segmentation in Abdominal Computed Tomography Scans Using the Particle Swarm Optimization Algorithm. Medical Imaging Technology 28(1) (2010)

    Google Scholar 

  91. Wang, C.M., Kuo, C.T., Lin, C.Y., Chang, G.H.: Application of Artificial Immune System Approach in MRI Classification. EURASIP Journal on Advances in Signal Processing, Article ID 547684, 8 (2008)

    Google Scholar 

  92. Hofmeyr, S.A., Forrest, S.: Immunity by design: an artificial immune system. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 1289–1296. Morgan Kaufmann, San Francisco (2004)

    Google Scholar 

  93. Poli, R.: Analysis of the Publications on the Applications of Particle Swarm Optimisation. Journal of Artificial Evolution and Applications, Article ID 685175, 10 (2008), doi:10.1155/2008/685175

    Google Scholar 

  94. Aickelin, U., Dasgupta, D.: Artificial immune systems tutorial. In: Burke, E., Kendall, G. (eds.) Search Methodologies—Introductory Tutorials in Optimization and Decision Support Techniques, pp. 375–399. Kluwer, Dordrecht (2005)

    Google Scholar 

  95. Eberhart, R.C., Shi, Y.: Comparison between genetic algorithms and Particle Swarm Optimization. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds.) Evolutionary Programming VII, pp. 611–616. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  96. Felzenszwalb, P., Huttenlocher, D.: Efficient Graph-Based Image Segmentation. Int’l J. Computer Vision 59(2), 167–181 (2004)

    Article  Google Scholar 

  97. Lange, H., Ferris, D.G.: Computer-aided-diagnosis (CAD) for colposcopy. In: Proceedings of Medical Imaging: Image Processing, vol. 5747, pp. 71–84 (2005)

    Google Scholar 

  98. Clow, B., White, T.: An evolutionary race: A comparison of genetic algorithms and particle swarm optimization for training neural networks. In: Proceedings of the International Conference on Artificial Intelligence, IC-AI 2004, vol. 2, pp. 582–588. CSREA Press (2004)

    Google Scholar 

  99. Jadhav, D.G., Pattnaik, S.S., Devi, S., Lohokare, M.R., And Bakwad, K.M.: Approximate Memetic Algorithm For Consistent Convergence. In: National Conference on Computational Instrumentation, NCCI 2010, CSIO Chandigarh, India (2010)

    Google Scholar 

  100. Ciesielski, V., Mawhinney, D.: Prevention of early convergence in genetic programming by replacement of similar programs. In: Xin, Y. (ed.) Proceedings of the Congress on Evolutionary Computation (2002)

    Google Scholar 

  101. Popa, R.: Hybridated Selfish Gene Algorithm. In: IEEE International Conference on Artificial Intelligence Systems, ICAIS 2002 (2002)

    Google Scholar 

  102. Beyer, H.-G., Schwefel, H.P.: Evolution strategies: A comprehensive introduction. Nat. Comput. 1(1), 3–52 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  103. Angeline, P.J.: Genetic programming and emergent intelligence. In: Kinnear Jr, K.E. (ed.) Advances in Genetic Programming, ch. 4, pp. 75–98. MIT Press, Cambridge (1994)

    Google Scholar 

  104. Digalakis, J., Margaritis, K.: Performance comparison of memetic algorithms. Journal of Applied Mathematics and Computation 158, 237–252 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  105. Villalobos-Arias, M., Coello Coello, C.A., Hernández-Lerma, O.: Convergence analysis of a multiobjective artificial immune system algorithm. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 226–235. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  106. Timmis, J.: Artificial immune systems: Today and tomorow. Natural Computing 6(1), 1–18 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  107. Yang, C., Li, Y., Lin, Z.: SGEGC: A Selfish Gene Theory Based Optimization Method by Exchanging Genetic Components. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds.) ISICA 2009. LNCS, vol. 5821, pp. 53–62. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia

    Noor Elaiza Abdul Khalid, Norharyati Md Ariff, Saadiah Yahya & Noorhayati Mohamed Noor

Authors
  1. Noor Elaiza Abdul Khalid
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Norharyati Md Ariff
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saadiah Yahya
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Noorhayati Mohamed Noor
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300, Gambang, Kuantan Pahang, Malaysia

    Jasni Mohamad Zain

  2. Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Lebuhraya Tun Razak, Gambang, 26300, Kuantan Pahang, Malaysia

    Wan Maseri bt Wan Mohd

  3. Information Systems Department, King Saud University, 11543, Riyadh, Saudi Arabia

    Eyas El-Qawasmeh

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Abdul Khalid, N.E., Md Ariff, N., Yahya, S., Mohamed Noor, N. (2011). A Review of Bio-inspired Algorithms as Image Processing Techniques. In: Mohamad Zain, J., Wan Mohd, W.M.b., El-Qawasmeh, E. (eds) Software Engineering and Computer Systems. ICSECS 2011. Communications in Computer and Information Science, vol 179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22170-5_57

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-22170-5_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22169-9

  • Online ISBN: 978-3-642-22170-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation