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An Overview of Image Segmentation Based on Pulse-Coupled Neural Network

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Abstract

Recent many researchers focus on image segmentation methods due to the rapid development of artificial intelligence technology. Hereinto, pulse-coupled neural network (PCNN) has a great potential based on the properties of neuronal activities. This paper elaborates internal behaviors of the PCNN to exhibit its image segmentation abilities. There are three significant parts: dynamic properties, parameter setting and complex PCNN. Further, we systematically provide the related segmentation contents of the PCNN, and hope to help researchers to understand suitable segmentation applications of PCNN models. Many corresponding examples are also used to exhibit PCNN segmentation effects.

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References

  1. Eckhorn R, Reitbock HJ, Arndt M, Dicke P (1989) A neural network for feature linking via synchronous activity: results from cat visual cortex and from simulations. In: Cotterill RMJ (ed) Models of brain function. Cambridge University Press, Cambridge

    Google Scholar 

  2. 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 2:293–307

    Google Scholar 

  3. Reitboeck HJ, Eckhorn R, Arndt M, Dicke P (1990) A model for feature linking via correlated neural activity. Springer, Berlin, pp 112–125

    Google Scholar 

  4. Eckhorn R (1999) Neural mechanisms of scene segmentation: recordings from the visual cortex suggest basic circuits for linking field models. IEEE Trans Neural Netw 10:464–479

    Google Scholar 

  5. Johnson JL, Ritter D (1993) Observation of periodic waves in a pulse-coupled neural network. Opt Lett 18:1253–1255

    Google Scholar 

  6. Johnson JL (1994) Pulse-coupled neural network, adaptive computing: mathematics, electronics, and optics, pp 47–76

  7. Johnson JL (1994) Pulse-coupled neural nets: translation, rotation, scale, distortion, and intensity signal invariance for images. Appl Opt 33:6239

    Google Scholar 

  8. Johnson JL, Padgett ML (1999) PCNN models and applications. IEEE Trans Neural Netw 10:480

    Google Scholar 

  9. Padgett ML, Johnson JL (1997) Pulse coupled neural networks (PCNN) and wavelets: biosensor applications. In: Proceedings of international conference on neural networks, pp 2507–2512

  10. Johnson JL, Padgett ML, Omidvar O (1999) Guest editorial overview of pulse coupled neural network (PCNN) special issue. IEEE Trans Neural Netw 10:461–463

    Google Scholar 

  11. Ranganath HS, Kuntimad G, Johnson JL (1995) Pulse coupled neural networks for image processing. In: Proceedings IEEE Southeastcon 95 visualize the future, pp 37–43

  12. Ranganath HS, Kuntimad G (1996) Iterative segmentation using pulse-coupled neural networks. In: Applications and science of artificial neural networks II, International Society for Optics and Photonics, pp 543–555

  13. Lindblad T, Kinser JM, Taylor J (2005) Image processing using pulse-coupled neural networks. Springer, Berlin

    MATH  Google Scholar 

  14. Ekblad U, Kinser JM, Atmer J, Zetterlund N (2004) The intersecting cortical model in image processing. Nuclear Instrum Methods Phys Res Sect A Accel Spectrom Detect Assoc Equip 525:392–396

    Google Scholar 

  15. Kinser JM (1996) Simplified pulse-coupled neural network. In: Applications and science of artificial neural networks II, International Society for Optics and Photonics, pp 563–568

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

    Google Scholar 

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

    Google Scholar 

  18. Chen Y, Park S-K, Ma Y, Rajeshkanna A (2011) A new automatic parameter setting method of a simplified PCNN for image segmentation. IEEE Trans Neural Netw 22:880–892

    Google Scholar 

  19. Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV Vancouver

  20. Gao C, Zhou D, Guo Y (2013) Automatic iterative algorithm for image segmentation using a modified pulse-coupled neural network. Neurocomputing 119:332–338

    Google Scholar 

  21. Huang Y, Ma Y, Li S, Zhan K (2016) Application of heterogeneous pulse coupled neural network in image quantization. J Electron Imaging 25:061603

    Google Scholar 

  22. Cheng Y, Tian L, Yin C, Huang X, Cao J, Bai L, Cheng Y, Tian L, Yin C, Huang X (2018) Research on crack detection applications of improved PCNN algorithm in MOI nondestructive test method. Neurocomputing 277:249–259

    Google Scholar 

  23. Ranganath HS, Bhatnagar A (2018) Image segmentation using two-layer pulse coupled neural network with inhibitory linking field. GSTF J Comput (JoC) 2018:1

    Google Scholar 

  24. Li Z, Liu Y, Walker R, Hayward R, Zhang J (2010) Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform. Mach Vis Appl 21:677–686

    Google Scholar 

  25. Zhou D, Zhou H, Gao C, Guo Y (2016) Simplified parameters model of PCNN and its application to image segmentation. Pattern Anal Appl 19:939–951

    MathSciNet  Google Scholar 

  26. Zhan K, Shi J, Wang H, Xie Y, Li Q (2017) Computational mechanisms of pulse-coupled neural networks: a comprehensive review. Arch Comput Methods Eng 24:533–588

    MathSciNet  MATH  Google Scholar 

  27. Wang L, Li S, Chen R, Liu S-Y, Chen J-C (2016) An automatic segmentation and classification framework based on PCNN model for single tooth in MicroCT images. PLoS ONE 11:e0157694

    Google Scholar 

  28. Xiang R (2018) Image segmentation for whole tomato plant recognition at night. Comput Electron Agric 154:434–442

    Google Scholar 

  29. Ma Y, Dai R, Li L (2002) Automated image segmentation using pulse coupled neural networks and images entropy. J China Inst Commun 23:46–50

    Google Scholar 

  30. Lian J, Yang Z, Sun W, Guo Y, Zheng L, Li J, Shi B, Ma Y (2019) An image segmentation method of a modified SPCNN based on human visual system in medical images. Neurocomputing 333:292–306

    Google Scholar 

  31. Wei S, Hong Q, Hou M (2011) Automatic image segmentation based on PCNN with adaptive threshold time constant. Neurocomputing 74:1485–1491

    Google Scholar 

  32. Lian J, Shi B, Li M, Nan Z, Ma Y (2017) An automatic segmentation method of a parameter-adaptive PCNN for medical images. Int J Comput Assist Radiol Surg 12:1511–1519

    Google Scholar 

  33. Helmy AK, El-Taweel GS (2016) Image segmentation scheme based on SOM–PCNN in frequency domain. Appl Soft Comput 40:405–415

    Google Scholar 

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

    Google Scholar 

  35. Xu G, Li X, Lei B, Lv K (2018) Unsupervised color image segmentation with color-alone feature using region growing pulse coupled neural network. Neurocomputing 306:1–16

    Google Scholar 

  36. Yonekawa M, Kurokawa H (2009) An automatic parameter adjustment method of pulse coupled neural network for image segmentation. In: International conference on artificial neural networks, Springer, pp 834–843

  37. Wu C, Liu Z, Jiang H (2018) Catenary image segmentation using the simplified PCNN with adaptive parameters. Optik 157:914–923

    Google Scholar 

  38. Yang N, Chen H, Li Y, Hao X (2012) Coupled parameter optimization of PCNN model and vehicle image segmentation. J Transp Syst Eng Inf Technol 12:48–54

    Google Scholar 

  39. Zhou D, Gao C, Guo Y (2014) A coarse-to-fine strategy for iterative segmentation using simplified pulse-coupled neural network. Soft Comput 18:557–570

    Google Scholar 

  40. Bi Y, Qiu T, Li X, Guo Y (2004) Automatic image segmentation based on a simplified pulse coupled neural network. In: International symposium on neural networks, Springer, pp 405–410

  41. Chacon-Murguia MI, Ramirez-Quintana JA (2018) Bio-inspired architecture for static object segmentation in time varying background models from video sequences. Neurocomputing 275:1846–1860

    Google Scholar 

  42. Gómez W, Pereira W, Infantosi AFC (2016) Evolutionary pulse-coupled neural network for segmenting breast lesions on ultrasonography. Neurocomputing 175:877–887

    Google Scholar 

  43. Yang Z, Lian J, Li S, Guo Y, Qi Y, Ma Y (2018) Heterogeneous SPCNN and its application in image segmentation. Neurocomputing 285:196–203

    Google Scholar 

  44. Chen Y, Ma Y, Kim DH, Park S-K (2015) Region-based object recognition by color segmentation using a simplified PCNN. IEEE Trans Neural Netw Learn Syst 26:1682–1697

    MathSciNet  Google Scholar 

  45. Lu Y, Miao J, Duan L, Qiao Y, Jia R (2008) A new approach to image segmentation based on simplified region growing PCNN. Appl Math Comput 205:807–814

    MATH  Google Scholar 

  46. Chen N, Qian ZB, Zhao SX, Fan JS (2007) Region growing based on pulse-coupled neural network. In: 2007 International conference on machine learning and cybernetics, IEEE, pp 2832–2836

  47. Ma T, Zhan K, Wang Z (2010) Applications of pulse-coupled neural networks. Springer, Berlin

    MATH  Google Scholar 

  48. Zhou D, Shao Y (2017) Region growing for image segmentation using an extended PCNN model. IET Image Process 12:729–737

    Google Scholar 

  49. Zhao R, Ma Y (2012) A region segmentation method for region-oriented image compression. Neurocomputing 85:45–52

    Google Scholar 

  50. Xiao Z, Shi J, Chang Q (2009) Automatic image segmentation algorithm based on PCNN and fuzzy mutual information. In: 2009 Ninth IEEE international conference on computer and information technology, IEEE, pp 241–245

  51. Nie R, Zhou D, Zhao D (2008) Image segmentation new methods using unit-linking PCNN and image’s entropy. J Syst Simul 20:222–227

    Google Scholar 

  52. Nie R, Cao J, Zhou D, Qian W (2019) Analysis of pulse period for passive neuron in pulse coupled neural network. Math Comput Simul 155:277–289

    MathSciNet  Google Scholar 

  53. Ma Y, Dai R, Li L, Wei L (2002) Image segmentation of embryonic plant cell using pulse-coupled neural networks. Chin Sci Bull 47:169–173

    Google Scholar 

  54. Zhan K, Shi J, Li Q, Teng J, Wang M (2015) Image segmentation using fast linking SCM. In: 2015 International joint conference on neural networks (IJCNN), IEEE, pp 1–8

  55. Li J, Zou B, Ding L, Gao X (2013) Image segmentation with PCNN model and immune algorithm. J Comput 8:2429–2437

    Google Scholar 

  56. Jiao K, Pan Z (2019) A novel method for image segmentation based on simplified pulse coupled neural network and Gbest led gravitational search algorithm. In: IEEE access

  57. Jiao K, Xu P, Zhao S (2018) A novel automatic parameter setting method of PCNN for image segmentation. In: 2018 IEEE 3rd international conference on signal and image processing (ICSIP), IEEE, pp 265–270

  58. Guo Y, Yang Z, Ma Y, Lian J, Zhu L (2018) Saliency motivated improved simplified PCNN model for object segmentation. Neurocomputing 275:2179–2190

    Google Scholar 

  59. Wang D, Terman D (1997) Image segmentation based on oscillatory correlation. Neural Comput 9:805–836

    Google Scholar 

  60. Berg H, Olsson R, Lindblad T, Chilo J (2008) Automatic design of pulse coupled neurons for image segmentation. Neurocomputing 71:1980–1993

    Google Scholar 

  61. Li H, Guo L, Yu P, Chen J, Tang Y (2016) Image segmentation based on iterative self-organizing data clustering threshold of PCNN. In: 2016 2nd International conference on cloud computing and internet of things (CCIOT), IEEE, pp 73–77

  62. Huang Y, Shuang W (2008) Image segmentation using pulse coupled neural networks. In: International conference on multimedia and information technology

  63. Wang M, Han G, Tu Y, Chen G, Gao Y (2008) Unsupervised texture Image segmentation based on Gabor wavelet and multi-PCNN. In: 2008 Second international symposium on intelligent information technology application, IEEE, pp 376–381

  64. Ma Y, Qi C (2006) Study of automated PCNN system based on genetic algorithm. J Syst Simul 18:722–725

    Google Scholar 

  65. Yang L, Lei K (2010) A new algorithm of image segmentation based on bidirectional search pulse-coupled neural network. In: 2010 International conference on computational aspects of social networks, IEEE, pp 101–104

  66. Yang Z, Lian J, Li S, Guo Y, Ma Y (2019) A study of sine–cosine oscillation heterogeneous PCNN for image quantization. Soft Comput 2019:1–12

    Google Scholar 

  67. Yang Z, Dong M, Guo Y, Gao X, Wang K, Shi B, Ma Y (2016) A new method of micro-calcifications detection in digitized mammograms based on improved simplified PCNN. Neurocomputing 218:79–90

    Google Scholar 

  68. Guo Y, Dong M, Yang Z, Gao X, Wang K, Luo C, Ma Y, Zhang J (2016) A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN. Comput Methods Progr Biomed 130:31–45

    Google Scholar 

  69. Xie W, Li Y, Ma Y (2016) PCNN-based level set method of automatic mammographic image segmentation. Optik Int J Light Electron Opt 127:1644–1650

    Google Scholar 

  70. Hassanien AE, Kim T-H (2012) Breast cancer MRI diagnosis approach using support vector machine and pulse coupled neural networks. J Appl Log 10:277–284

    MathSciNet  Google Scholar 

  71. Ali JM, Hassanien AE (2006) PCNN for detection of masses in digital mammogram. Neural Netw World 16:129

    Google Scholar 

  72. Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer WP (2000) The digital database for screening mammography. In: Proceedings of the 5th international workshop on digital mammography Medical Physics Publishing, pp 212–218

  73. Suckling P (1994) The mammographic image analysis society digital mammogram database. In: Digital Mammo, 375–386

  74. Hassanien AE, Al-Qaheri H, El-Dahshan E-SA (2011) Prostate boundary detection in ultrasound images using biologically-inspired spiking neural network. Appl Soft Comput 11:2035–2041

    Google Scholar 

  75. Lian J, Ma Y, Ma Y, Shi B, Liu J, Yang Z, Guo Y (2017) Automatic gallbladder and gallstone regions segmentation in ultrasound image. Int J Comput Assist Radiol Surg 12:553–568

    Google Scholar 

  76. Fu J, Chen C, Chai J, Wong ST, Li I (2010) Image segmentation by EM-based adaptive pulse coupled neural networks in brain magnetic resonance imaging. Comput Medl Imaging Gr 34:308–320

    Google Scholar 

  77. Murugavel M, Sullivan JM Jr (2009) Automatic cropping of MRI rat brain volumes using pulse coupled neural networks. Neuroimage 45:845–854

    Google Scholar 

  78. Harris MA, Van AN, Malik BH, Jabbour JM, Maitland KC (2015) A pulse coupled neural network segmentation algorithm for reflectance confocal images of epithelial tissue. PLoS ONE 10:e0122368

    Google Scholar 

  79. Xu X, Liang T, Wang G, Wang M, Wang X (2017) Self-adaptive PCNN based on the ACO algorithm and its application on medical image segmentation. Intell Autom Soft Comput 23:303–310

    Google Scholar 

  80. Jebaseeli TJ, Durai CAD, Peter JD (2019) Segmentation of retinal blood vessels from ophthalmologic diabetic retinopathy images. Comput Electr Eng 73:245–258

    Google Scholar 

  81. Zhu S, Wang L, Duan S (2017) Memristive pulse coupled neural network with applications in medical image processing. Neurocomputing 227:149–157

    Google Scholar 

  82. Guo Y, Gao X, Yang Z, Lian J, Du S, Zhang H, Ma Y (2018) SCM-motivated enhanced CV model for mass segmentation from coarse-to-fine in digital mammography. Multimed Tools Appl 77:24333–24352

    Google Scholar 

  83. Gao X, Wang K, Guo Y, Yang Z, Ma Y (2015) Mass segmentation in Mammograms based on the combination of the spiking cortical model (SCM) and the improved CV Model. In: International symposium on visual computing, Springer, pp 664–671

  84. Ma Y, Wang L, Ma Y, Dong M, Du S, Sun X (2016) An SPCNN-GVF-based approach for the automatic segmentation of left ventricle in cardiac cine MR images. Int J Comput Assist Radiol Surg 11:1951–1964

    Google Scholar 

  85. Ma Y, Wang D, Ma Y, Lei R, Wang K (2017) Novel approach for automatic segmentation of LV endocardium via SPCNN. In: Eighth international conference on graphic and image processing (ICGIP 2016), International Society for Optics and Photonics, pp 1022519

  86. Wang K, Ma Y, Lei R, Yang Z, Ma Y (2017) Automatic right ventricle segmentation in cardiac MRI via anisotropic diffusion and SPCNN. In: Eighth international conference on graphic and image processing (ICGIP 2016), International Society for Optics and Photonics, pp 1022527

  87. Guo Y, Wang X, Yang Z, Wang D, Ma Y (2016) Improved saliency detection for abnormalities in mammograms. In: 2016 International conference on computational science and computational intelligence (CSCI), IEEE, pp 786–791

  88. Hage IS, Hamade RF (2013) Segmentation of histology slides of cortical bone using pulse coupled neural networks optimized by particle-swarm optimization. Comput Med Imaging Gr 37:466–474

    Google Scholar 

  89. Huang Y, Ma Y, Li S (2015) A new method for image quantization based on adaptive region related heterogeneous PCNN. In: International symposium on neural networks, Springer, pp 269–278

  90. Li H, Jin X, Yang N, Yang Z (2015) The recognition of landed aircrafts based on PCNN model and affine moment invariants. Pattern Recognit Lett 51:23–29

    Google Scholar 

  91. Waldemark K, Lindblad T, Bečanović V, Guillen JL, 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 21:227–237

    Google Scholar 

  92. Karvonen JA (2004) Baltic sea ice SAR segmentation and classification using modified pulse-coupled neural networks. IEEE Trans Geosci Remote Sens 42:1566–1574

    Google Scholar 

  93. Del Frate F, Latini D, Pratola C, Palazzo F (2013) PCNN for automatic segmentation and information extraction from X-band SAR imagery. Int J Image Data Fusion 4:75–88

    Google Scholar 

  94. Wang B, Wan L, Li Y (2016) Saliency motivated pulse coupled neural network for underwater laser image segmentation. J Shanghai Jiaotong Univ (Sci) 21:289–296

    Google Scholar 

  95. Guo Y, Luo C, Ma Y (2017) Object detection system based on multimodel saliency maps. J Electron Imaging 26:023022

    Google Scholar 

  96. Wang Z, Sun X, Zhang Y, Ying Z, Ma Y (2016) Leaf recognition based on PCNN. Neural Comput Appl 27:899–908

    Google Scholar 

  97. Guo X, Zhang M, Dai Y (2018) Image of plant disease segmentation model based on pulse coupled neural network with Shuffle Frog Leap Algorithm. In: 2018 14th International conference on computational intelligence and security (CIS), IEEE, pp 169–173

  98. Wang Z, Li H, Zhu Y, Xu T (2017) Review of plant identification based on image processing. Arch Comput Methods Eng 24:637–654

    MathSciNet  MATH  Google Scholar 

  99. Xu G, Zhang Z, Ma Y (2008) An image segmentation based method for iris feature extraction. J China Univ Posts Telecommun 96:101–117

    Google Scholar 

  100. Wang Z, Ma Y, Xu G (2006) A novel method of iris feature extraction based on the ICM. In: 2006 IEEE international conference on information acquisition IEEE, pp 814–818

  101. He F, Guo Y, Gao C (2019) A parameter estimation method of the simple PCNN model for infrared human segmentation. Opt Laser Technol 110:114–119

    Google Scholar 

  102. Wang X, Lei L, Wang M (2012) Palmprint verification based on 2D–Gabor wavelet and pulse-coupled neural network. Knowl Based Syst 27:451–455

    Google Scholar 

  103. Shi M, Jiang S, Wang H, Xu B (2009) A simplified pulse-coupled neural network for adaptive segmentation of fabric defects. Mach Vis Appl 20:131–138

    Google Scholar 

  104. Shang L, Yi Z, Ji L (2009) Constrained ZIP code segmentation by a PCNN-based thinning algorithm. Neurocomputing 72:1755–1762

    Google Scholar 

  105. Ruan C, Zhao D, Chen X, Jia W, Liu X (2016) Aquatic image segmentation method based on hs-PCNN for automatic operation boat in crab farming. J Comput Theor Nanosci 13:7366–7374

    Google Scholar 

  106. Skourikhine AN, Prasad L, Schlei BR (2000) Neural network for image segmentation. In: Proceedings of SPIE: the international society for optical engineering, vol 4120, pp 28–35

  107. Ji L, Yi Z, Shang L (2008) An improved pulse coupled neural network for image processing. Neural Comput Appl 17:255–263

    Google Scholar 

  108. Gu X, Zhang L, Yu D (2005) General design approach to unit-linking PCNN for image processing. In: Proceedings. 2005 IEEE international joint conference on neural networks, IEEE, pp 1836–1841

  109. Gu X (2008) Feature extraction using unit-linking pulse coupled neural network and its applications. Neural Process Lett 27:25–41

    Google Scholar 

  110. Mohammed MM, Badr A, Abdelhalim M (2015) Image classification and retrieval using optimized pulse-coupled neural network. Expert Syst Appl 42:4927–4936

    Google Scholar 

  111. Mureşan RC (2003) Pattern recognition using pulse-coupled neural networks and discrete Fourier transforms. Neurocomputing 51:487–493

    Google Scholar 

  112. Zhan K, Shi J, Wang H, Xie Y, Li Q (2017) Computational mechanisms of pulse-coupled neural networks: a comprehensive review. Arch Comput Methods Eng 24:573–588

    MathSciNet  MATH  Google Scholar 

  113. Yang Z, Lian J, Guo Y, Li S, Wang D, Sun W, Ma Y (2018) An overview of PCNN model’s development and its application in image processing. Arch Comput Methods Eng 2018:1–15

    Google Scholar 

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

    Google Scholar 

  115. Subashini MM, Sahoo SK (2014) Pulse coupled neural networks and its applications. Expert Syst Appl 41:3965–3974

    Google Scholar 

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Acknowledgments

The authors thank all the reviewers for their valuable comments, which further improved the quality of the paper. This study was funded National Natural Science Foundation of China (Grant Nos. 61175012, 61962034 and 61861024), Natural Science Foundation of Gansu Province of China (Grant Nos. 148RJZA044 and 18JR3RA288) and Youth Foundation of Lanzhou Jiaotong University of China (Grant No. 2014005).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, No. 88 West Anning Road, Lanzhou, 730070, Gansu, People’s Republic of China

    Jing Lian, Zhen Yang, Wenhao Sun, Li Zheng, Xiaogang Du & Zetong Yi

  2. School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, 730000, Gansu, People’s Republic of China

    Jing Lian, Zhen Yang, Wenhao Sun & Yide Ma

  3. School of Data Science and Computer Science, Sun Yat-sen University, No. 132 East Outer Ring Road, Guangzhou, 510275, Guangdong, People’s Republic of China

    Jizhao Liu

  4. Equipment Management Department, Gansu Provincial Hospital, No. 204 West Donggang Road, Lanzhou, 730000, Gansu, People’s Republic of China

    Bin Shi

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  1. Jing Lian
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Lian, J., Yang, Z., Liu, J. et al. An Overview of Image Segmentation Based on Pulse-Coupled Neural Network. Arch Computat Methods Eng 28, 387–403 (2021). https://doi.org/10.1007/s11831-019-09381-5

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