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A coarse-to-fine strategy for iterative segmentation using simplified pulse-coupled neural network

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Abstract

Pulse-coupled neural network (PCNN) has significant characteristics for potential high-performance image processing including image segmentation. However, segmentation accuracy is dependent on the values of network parameters. To overcome the difficulties caused by parameter settings, this paper simplified the original PCNN in terms of input and dynamic neural threshold. In the model, the generalized adjustable neural threshold is defined, and the relationship between parameters and such available information as previous output and image static properties is established. A coarse-to-fine strategy is then employed for further keeping the characteristic of the synchronous pulse, enabling the model to control the behavior of neighboring neurons. This strategy also ensures that the parameters are adjusted properly and facilitates the automatic control of the result through iteration. Finally, experiments on some synthetic and real infrared images show that the proposed model can promote segmentation capability. Furthermore, the proposed model is superior to the traditional thresholding methods and some existing PCNN-based models in terms of segmentation performance and parameter settings.

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References

  • Bazi Y, Bruzzone L, Melgania F (2007) Image thresholding based on the EM algorithm and the generalized Gaussian distribution. Pattern Recognit 40(2):619–634

    Article  MATH  Google Scholar 

  • Berg H, OIsson R, Lindblad T, Chilo J (2008) Automatic design of pulse-coupled neurons for image segmentation. Neurocomputing 71(10–12):1980–1993

    Google Scholar 

  • Bi Y, Qiu T, Li X, Guo Y (2004) Automatic image segmentation based on a simplified pulse coupled neural network. Lect Notes Comput Sci 3174:405–410

    Article  Google Scholar 

  • Chen Y, Park SK, Ma Y, Ala R (2011) A new automatic parameter setting method of a simplified PCNN for image segmentation. IEEE Trans Neural Netw 22(6):880–892

    Google Scholar 

  • Cheng D, Liu X, Tang X, Liu J, Huang J (2007) Image segmentation based on improved pulse coupled neural network. Chin High Technol Lett 17(12):1228–1233

    Google Scholar 

  • Cheng Y (1995) Mean shift, mode seeking, and clustering. IEEE Trans Pattern Anal Mach Intell 17(8):790–799

    Article  Google Scholar 

  • Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619

    Article  Google Scholar 

  • Eckhorn R, Reitboeck HJ, Arndt M, Dicke P (1990) Feature linking via synchronization among distributed assemblies: simulations of results from cat visual cortex. Neural Comput 2(3):293–307

    Article  Google Scholar 

  • Fu JC, Chen CC, Chai JW, Wong STC, Li IC (2010) Image segmentation by EM-based adaptive pulse coupled neural networks in brain magnetic resonance imaging. Comput Med Imaging Graphics 34(4):308–320

    Article  Google Scholar 

  • Gao C, Zhou D, Guo Y (2013) Automatic iterative algorithm for image segmentation using a modified pulse-coupled nerual network. Neurocomputing. doi:10.1016/j.neucom.2013.03.025

    Google Scholar 

  • Ghosh A, Pal NR, Pal SK (1991) Image segmentation using a neural network. Biol Cybern 66(2):151–158

    Google Scholar 

  • Gu X, Guo S, Yu D, (2002) A new approach for automated image segmentation based on unit-linking PCNN. In: Proceedings of (2002) international conference on machine learning and cybernetics. Beijing, China, pp 175–178

  • Kinser JM (1996) Simplified pulse-coupled neural network. In: Proceedings of SPIE, Orlando FL USA, pp 563–567

  • Kittler J, Illingworth J (1986) Minimum error thresholding. Pattern Recognit 19(1):41–47

    Article  Google Scholar 

  • Kuntimad G, Ranganath HS (1999) Perfect image segmentation using pulse coupled neural networks. IEEE Trans Neural Netw 10(3):591–598

    Article  Google Scholar 

  • Kurokawa H, Kaneko S, Yonekawa M (2009) A color image segmentation using inhibitory connected pulse coupled neural network. Lect Notes Comput Sci 5507:776–783

    Article  Google Scholar 

  • Ma Y, Dai R, Li L, Wei L (2002a) Image segmentation of embryonic plant cell using pulse-coupled neural networks. Chin Sci Bull 47(2):167–172

    Google Scholar 

  • Ma Y, Dai R, Li L, Wei L (2002b) Automated image segmentation using pusle coupled neural networks and image’s entropy. J China Inst Commun 23(1):46–51

    Google Scholar 

  • Ma Y, Qi C (2006) Study of automated PCNN system based on genetic algorithm. J Syst Simul 18(3):722–726

    Google Scholar 

  • Osher S, Fedkiw RP (2001) Level set methods: an overview and some recent results. J Comput Phys 169(2):463–502

    Article  MATH  MathSciNet  Google Scholar 

  • Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Article  MathSciNet  Google Scholar 

  • Ranganath HS, Kuntimad G (1994) Image segmentation using pulse coupled neural networks. In: Proceedings of IEEE international conference on neural networks, Orlando, FL, USA, pp 1285–1290

  • Rava HT, Bettaiah V, Ranganath HS (2011) Adaptive pulse coupled neural network parameters for image segmentation. World Acad Sci Eng Technol 73:1046–1052

    Google Scholar 

  • Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13(1):146–168

    Article  Google Scholar 

  • Skourikhine AN, Prasad L, Schlei BR (2000) Neural network for image segmentation. Proceedings of SPIE, SanDiego, Calif, In, pp 28–35

    Google Scholar 

  • Stewart RD, Fermin I, Opper M (2002) Region growing with pulse-coupled neural networks: an alternative to seeded region growing. IEEE Trans Neural Netw 13(6):1557–1562

    Article  Google Scholar 

  • Tsai YH, Osher S (2005) Total variation and level set methods in image science. Acta Numerica 14:509–573

    Article  MATH  MathSciNet  Google Scholar 

  • Waldemark K, Lindblad T, Bečanović V, Guillen JLL, Klingner PL (2000) Patterns from the sky: satellite image analysis using pulse coupled neural networks for pre-processing, segmentation and edge detection. Pattern Recognit Lett 21(3):227–237

    Article  Google Scholar 

  • Wang Z, Ma Y, Cheng F, Yang L (2010) Review of pulse coupled neural networks. Image Vis Comput 28(1):5–13

    Article  MATH  Google Scholar 

  • Wei S, Qu H, Hou M (2011) Automatic image segmentation based on PCNN with adaptive threshold time constant. Neurocomputing 74(9):1485–1491

    Article  Google Scholar 

  • Yonekawa M, Kurokawa H (2009) An automatic parameter adjustment method of pulse coupled neural network for image segmentation. Lect Notes Comput Sci 5768:834–843

    Article  Google Scholar 

  • Zhan K, Zhang H, Ma Y (2009) New spiking cortical model for invariant texture retrieval and image processing. IEEE Trans Neural Netw 20(12):1980–1986

    Article  MathSciNet  Google Scholar 

  • Zheng Y, Gu Y, Xu K (2011) A novel method of image segmentation based on gradient inhibiting PCNN. Energy Procedia 13:3445–3453

    Article  Google Scholar 

  • Zhou D, Gao C, Guo Y (2013) Simplified pulse coupled neural network with adaptive multilevel threshold for infrared human image segmentation. J Comput Aided Design Comput Graphics 25(2):208–214

    Google Scholar 

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Acknowledgments

The authors would like to thank all the reviewers for very helpful comments and suggestions. This work has been supported by the grants of the Science Foundation of Ministry of Education, No. 20090191110026, and the Fundamental Research Funds for the Central Universities, No. CDJXS11120022.

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Authors and Affiliations

  1. Key Laboratory of Opto-electronic Technology and Systems of the Education Ministry, Chongqing University, Chongqing, China

    Dongguo Zhou, Chao Gao & Yongcai Guo

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  1. Dongguo Zhou
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  2. Chao Gao
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  3. Yongcai Guo
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Correspondence to Dongguo Zhou.

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Communicated by V. Piuri.

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Zhou, D., Gao, C. & Guo, Y. A coarse-to-fine strategy for iterative segmentation using simplified pulse-coupled neural network. Soft Comput 18, 557–570 (2014). https://doi.org/10.1007/s00500-013-1077-8

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