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Improved thresholding based on negative selection algorithm (NSA)

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Abstract

Thresholding is a tool of image segmentation which groups the pixels in a logical way. In this paper, a novel algorithm based on negative selection algorithm a model of artificial immune system is proposed for image thresholding. The proposed algorithm is applied on the thresholded images of lathe tool produced using maximum information entropy (MIE) and global thresholding based technique resulting in an improved image. To verify the algorithm and results, it has also been applied on some of the inbuilt MATLAB (MATrix LABoratory) images. Histogram is employed to analyze the results. Further, the results of improved algorithm are compared with the results of MIE and the global thresholding methods to check the effectiveness of the proposed method. The experimental results confirm the potential of the developed algorithm.

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References

  1. Wenping M, Licheng J, Ronghua S, Fujia Z (2009) Medical image segmentation based on immune clonal optimization. In: Proceedings of the 2009 IEEE International Conference on intelligent computing and intelligent systems, pp 377–381. doi:10.1109/icicisys.2009.5357824

  2. Xu X, Zhang Z, Xie J, Xie K (2009) Threshold image segmentation based on granular immune algorithm. In: Proceedings of the 2009 Chinese Conference on control and decision, pp 3512–3515. doi:10.1109/ccdc.2009.5192493

  3. Lievers WB, Pilkey AK (2004) An evaluation of global thresholding techniques for the automatic image segmentation of automotive aluminum sheet alloys. Mater Sci Eng 381(1–2):134–142. doi:10.1016/j.msea.2004.04.002

    Article  Google Scholar 

  4. Sahoo PK, Soltani S, Wong AKC (1988) A survey of thresholding techniques. Computer Vision, Graphics, and Image Processing 41 (2):233–260. doi:10.1016/0734-189X(88)90022-9

  5. Bradley D, Roth G (2007) Adaptive tusing the integral image. J Graphics GPU, Game tools 12 (2):13–21. doi:10.1080/2151237x.2007.10129236

  6. Liao PS, Chen TS, Chung PC (2001) A fast algorithm for multilevel thresholding. J Inf Sci Eng 17(5):713–727

    Google Scholar 

  7. Al-amri SS, Kalyankar NV, Khamitkar SD (2010) Image segmentation by using threshold techniques. J Comput 2(5):83–86

    Google Scholar 

  8. Kapur JN, Sahoo PK, Wong AKC (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vision Graph 29(3):273–285. doi:10.1016/0734-189x(85)90125-2

    Article  Google Scholar 

  9. Wang S, Chung F, Xiong F (2008) A novel image thresholding method based on Parzen window estimate. Pattern Recogn 41(1):117–129. doi:10.1016/j.patcog.2007.03.029

    Article  MATH  Google Scholar 

  10. Zhang H, Fritts JE, Goldman SA (2008) Image segmentation evaluation: a survey of unsupervised methods. Comput Vis Image Und 110(2):260–280. doi:10.1016/j.cviu.2007.08.003

    Article  Google Scholar 

  11. Cuevas E, Zaldivar D, Pérez-Cisneros M (2010) A novel multi-threshold segmentation approach based on differential evolution optimization. Expert Syst. Appl 37(7):5265–5271. doi:10.1016/j.eswa.2010.01.013

    Article  Google Scholar 

  12. Lai C–C (2006) A novel image segmentation approach based on particle swarm optimization. IEICE Trans Fundam Electron Commun Comput Sci E89-A(1):324–327. doi:10.1093/ietfec/e89-a.1.324

    Article  Google Scholar 

  13. Chandhok C (2012) A novel approach to image segmentation using artificial neural networks and k-means clustering. Int J Eng Res Appl 2(3):274–279

    Google Scholar 

  14. Dutta A, Kar A, Chatterji BN (2011) Fuzzy set theoretic approach to image thresholding. Int J Comput. Sci Eng Appl 1(6):63–72

    Google Scholar 

  15. Castro LNd, Timmis J (2002) Artificial immune systems: a new computational intelligence approach, 1st edn. Springer, London

    Google Scholar 

  16. Greensmith J, Whitbrook A, Aickelin U (2010) Handbook of metaheuristics, 2nd edn, Ch 14 (Artificial immune systems), pp. 421–448 Springer London

  17. Hofmeyr SA, Forrest SA (2000) Architecture for an Artificial Immune System. Evol Comput 8(4):443–473. doi:10.1162/106365600568257

    Article  Google Scholar 

  18. Glickman M, Balthrop J, Forrest S (2005) A Machine learning evaluation of an artificial immune system. Evol Comput 13(2):179–212. doi:10.1162/1063656054088503

    Article  Google Scholar 

  19. Muda AK, Shamsuddin SM (2005) An overview of artificial immune system in pattern recognition. Proceedings of the Postgraduate Annual Research Seminar, In, pp 119–126

    Google Scholar 

  20. Twycross J, Aickelin U, Whitbrook A (2010) Detecting anomalous process behaviour using second generation artificial immune systems. Int J Unconventional Comp 6(3–4):301–326

    Google Scholar 

  21. Dal D, Abraham S, Abraham A, Sanyal S, Sanglikar M Evolution induced secondary immunity: an artificial immune system based intrusion detection system. In: computer information systems and industrial management applications 2008. CISIM ‘08. 7th, 26–28 June 2008 pp 65–70. doi:10.1109/cisim.2008.31

  22. Jakimovski B, Maehle E (2008) Artificial immune system based robot anomaly detection engine for fault tolerant robots. In: Rong C, Jaatun M, Sandnes F, Yang L, Ma J (eds) Autonomic and trusted computing vol 5060 Lecture notes in Computer Science, Springer Berlin Heidelberg pp 177–190. doi:10.1007/978-3-540-69295-9_16

  23. Castro LNd, Timmis J (2002) Artificial immune systems: A novel paradigm to pattern recognition. In artificial neural networks in pattern recognition JM Corchado, L Alonso and C Fyfe (eds) SOCO-2002, University of Paisley, UK, pp. 67–84

  24. Castiglione F, Motta S, Nicosia G (2001) Pattern recognition by primary and secondary response of an artificial immune system. theory in biosciences 120 (2):93–106. doi:10.1078/1431-7613-00032

  25. Alexandrino JL, Costa de Barros Carvalho Filho E (2006) Investigation of a new artificial immune system model applied to pattern recognition. In: Proceedings of the 2006 Sixth International Conference on hybrid intelligent systems, pp 16–16 doi:10.1109/his.2006.264899

  26. Yanfei Z, Liangpei Z, Bo H, Pingxiang L (2006) An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery. IEEE Transactions on Geosci Remote 44(2):420–431. doi:10.1109/tgrs.2005.861548

    Article  Google Scholar 

  27. Rodin V, Benzinou A, Guillaud A, Ballet P, Harrouet F, Tisseau J, Le Bihan J (2004) An immune oriented multi-agent system for biological image processing. Pattern Recognit 37(4):631–645. doi:10.1016/j.patcog.2003.09.014

    Article  Google Scholar 

  28. McCoy DF, Devarajan V (1997) Artificial immune systems and aerial image segmentation. In: Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics, 1 pp 867–872. doi:10.1109/icsmc.1997.626211

  29. Ghamisi P, Couceiro MS, Ferreira NMF, Kumar L (2012)Use of darwinian particle swarm optimization technique for the segmentation of remote sensing images. In: Proceedings of the 2012 IEEE International Symposium on Geoscience and Remote Sensing pp 4295–4298. doi:10.1109/igarss.2012.6351718

  30. Analoui M, Beheshti M (2012) A New clustering algorithm for noisy image retrieval. In: Ao SI, Castillo O, Huang X (eds) intelligent control and innovative computing, vol 110, Lecture notes in electrical engineering Springer US, pp 289–301. doi:10.1007/978-1-4614-1695-1_22

  31. Dasgupta D, Ji Z, Gonzalez F (2003) Artificial immune system (AIS) research in the last five years. In: Proceedings of the 2003 Congress on. Evolut Comput vol 121:123–130. doi:10.1109/cec.2003.1299565

    Google Scholar 

  32. Aickelin U (2004) Artificial immune systems (AIS)–a new paradigm for heuristic decision making. In: Proceedings of the (2004) Annual Conference on Operational Research, OR 46. University of York, UK

    Google Scholar 

  33. Forrest S, Perelson AS, Allen L, Cherukuri R (1994) Self-nonself discrimination in a computer. In: Proceedings of the 1994 IEEE Computer Society Symposium on Research in Security and Privacy, pp 202–212. doi:10.1109/risp.1994.296580

  34. Ji Z, Dasgupta D (2007) Revisiting negative selection algorithms. Evolut Comput 15(2):223–251. doi:10.1162/evco.2007.15.2.223

    Article  Google Scholar 

  35. Dongmei F, Xiaochen W, Xiaoping H (2008) An extraction of infrared occluded-object based on maximum variance and negative selection. In: Proceedings of the 2008 International Workshop on Geoscience and Remote Sensing, pp 686–690 doi:10.1109/ETTandGRS.2008.287

  36. Qian W, Xiao-kai F (2008) A detector generation algorithm based on negative selection. In: Proceedings of the Fourth International Conference on Natural Computation, pp 605-611. doi:10.1109/icnc.2008.617

  37. Xiaojun B, Guifang J (2007) Image segmentation algorithm based on quantum immune programming. In: Proceedings of the 2007 IEEE International Conference on Integration Technology, pp 403–407 doi:10.1109/icitechnology.2007.4290506

  38. Kim J, Bentley PJ (2001) An evaluation of negative selection in an artificial immune system for network intrusion detection. Proceedings of the International Conference Genetic and Evolutionary Computation, In, pp 1330–1337

    Google Scholar 

  39. Stibor T, Mohr P, Timmis J, Eckert C (2005) Is negative selection appropriate for anomaly detection?. In: Proceedings of the 2005 International conference on Genetic and evolutionary computation, pp 321–328

  40. Jinquan Z, Tao L, Xiaojie L, Caiming L, Lingxi P, Feixian S (2007) A feedback negative selection algorithm to anomaly detection In: Proceedings of the Third International Conference on Natural Computation pp 604–608. doi:10.1109/icnc.2007.28

  41. Gong M, Zhang J, Ma J, Jiao L (2012) An efficient negative selection algorithm with further training for anomaly detection. Knowledge-Based Sys 30:185–191. doi:10.1016/j.knosys.2012.01.004

    Article  Google Scholar 

  42. Stibor T, Timmis J, Eckert C (2005) A comparative study of real-valued negative selection to statistical anomaly detection techniques In: Jacob C, Pilat M, Bentley P, Timmis J (eds) Artificial immune systems, vol 3627 Lecture Notes in Computer Science, Springer Berlin Heidelberg, pp 262–275 doi:10.1007/11536444_20

  43. Dasgupta D, KrishnaKumar K, Wong D, Berry M (2004) Negative selection algorithm for aircraft fault detection In: Nicosia G, Cutello V, Bentley P, Timmis J (eds) Artificial Immune Systems vol 3239 Lecture Notes in Computer Science,Springer Berlin Heidelberg, pp 1–13 doi:10.1007/978-3-540-30220-9_1

  44. Gonz′alez FA, Dasgupta D (2003) Anomaly detection using real-valued negative selection. Genet Program Evolvable Mach 4(4):383–403. doi:10.1023/a:1026195112518

    Article  Google Scholar 

  45. Otsu N (1979) A Threshold selection method from gray-level histograms Syst Man Cyb IEEE Transactions on 9 (1):62–66 doi:10.1109/tsmc.1979.4310076

  46. Jinquan Z, Xiaojie L, Tao L, Caiming L, Lingxi P, Feixian S (2009) A self-adaptive negative selection algorithm used for anomaly detection. Prog Nat Sci 19(2):261–266. doi:10.1016/j.pnsc.2008.06.008

    Article  Google Scholar 

  47. Cao X, Qiao H, Xu Y (2007) Negative selection based immune optimization. Advances in Engineering Software 38 (10):649–656 doi:http://dx.doi.org/10.1016/j.advengsoft.2006.11.006

  48. http://www.mathworks.in/help/images/ref/graythresh.html

  49. Gonzalez RC, Woods RE, Eddins SL (2011) Digital image processing using MATLAB, 2nd edn. Tata McGraw-Hill Education Private Ltd, India

    Google Scholar 

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Acknowledgments

Authors would like to thank Director, CSIR-CSIO for giving opportunity to work on this Network Project (ESC-0112) funded by Council of Scientific and Industrial Research (CSIR), New Delhi.

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Authors and Affiliations

  1. V-2 (Biomedical Instrumentation Division), CSIR-Central Scientific Instruments Organisation, Sector-30, Chandigarh, 160030, India

    Prasant Kumar Mahapatra, Mandeep Kaur, Spardha Sethi, Rishabh Thareja & Amod Kumar

  2. University Institute of Engineering and Technology, Punjab University, Chandigarh, India

    Spardha Sethi

  3. Department of Electronics and Communication, National Institute of Technical Teachers’ Training and Research, Sector-26, Chandigarh, 160026, India

    Swapna Devi

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  1. Prasant Kumar Mahapatra
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Correspondence to Prasant Kumar Mahapatra.

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Mahapatra, P.K., Kaur, M., Sethi, S. et al. Improved thresholding based on negative selection algorithm (NSA). Evol. Intel. 6, 157–170 (2014). https://doi.org/10.1007/s12065-013-0089-8

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  • DOI: https://doi.org/10.1007/s12065-013-0089-8

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