Skip to main content
SpringerLink
Log in

Spiking Neural P System with weight model of majority voting technique for reliable interactive image segmentation

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Interactive image segmentation is a method for precisely segmenting of the object from background using information entered by the user. However, most interactive segmentation techniques are sensitive to the location and the number of seed points. To obtain a satisfactory result, the user should repeat the segmentation process over and over, and also based on employed technique, it may work well in some limited conditions and applications. To overcome these limitations and enhance the robustness of interactive image segmentation algorithm, this paper proposes a parallel fusion model using the majority voting technique, which not only is more reliable than existing methods, but also requires less user interaction. To this end, at first the input image is segmented by several segmentation methods independently. Then the obtained results are combined using majority voting technique to extract final segmentation result. To reduce the computational overhead of the proposed scheme, a spiking neural-like P system model for parallel implementation of majority voting technique is also proposed. The proposed model has been evaluated and compared with state-of-the-art methods using different metrics, and the obtained results show its efficiency compared to other methods.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Borjigin S, Sahoo PK (2019) Color image segmentation based on multi-level Tsallis–Havrda–Charvát entropy and 2D histogram using PSO algorithms. Pattern Recogn 92:107–118

    Article  Google Scholar 

  2. Cai Q, Liu H, Qian Y, Zhou S, Duan X, Yang Y-H (2019) Saliency-guided level set model for automatic object segmentation. Pattern Recogn 93:147–163

    Article  Google Scholar 

  3. Li Haixing, Luo Haibo, Huan Wang, Shi Zelin, Yan Chongnan, Wang Lanbo, Yueming Mu, Liu Yunpeng (2021) Automatic lumbar spinal MRI image segmentation with a multi-scale attention network. Neural Comput Appl 33(18):11589–11602

    Article  Google Scholar 

  4. Perez-Borrero Isaac, Marin-Santos Diego, Vasallo-Vazquez Manuel J, Gegundez-Arias Manuel E (2021) A new deep-learning strawberry instance segmentation methodology based on a fully convolutional neural network. Neural Comput Appl 33(22):15059–15071

    Article  Google Scholar 

  5. Wang T, Qi S, Ji Z, Sun Q, Peng Fu, Ge Qi (2020) Error-tolerant label prior for interactive image segmentation. Inf Sci 538:384–395

    Article  MathSciNet  Google Scholar 

  6. Wang T, Yang J, Ji Z, Sun Q (2018) Probabilistic diffusion for interactive image segmentation. IEEE Trans Image Process 28(1):330–342

    Article  MathSciNet  MATH  Google Scholar 

  7. Bragantini Jordão, Moura Bruno, Falcao Alexandre X, Cappabianco Fábio AM (2020) Grabber: a tool to improve convergence in interactive image segmentation. Pattern Recognit Lett 140:267–273

    Article  Google Scholar 

  8. Chen D-J, Chen H-T, Chang L-W (2018) Toward a unified scheme for fast interactive segmentation. J Vis Commun Image Represent 55:393–403

    Article  Google Scholar 

  9. Wang Tao, Ji Zexuan, Yang Jian, Sun Quansen, Peng Fu (2020) Global Manifold Learning for Interactive Image Segmentation. IEEE Trans Multimedia 23:3239–3249

    Article  Google Scholar 

  10. Song Gwangmo, Lee Kyoung Mu (2020) Bi-directional seed attention network for interactive image segmentation. IEEE Signal Process Lett 27:1540–1544

    Article  Google Scholar 

  11. Jian M, Jung C (2016) Interactive image segmentation using adaptive constraint propagation. IEEE Trans Image Process 25(3):1301–1311

    MathSciNet  MATH  Google Scholar 

  12. Shi Ran, Ngan King Ngi, Li Songnan, Li Hongliang (2018) Interactive object segmentation in two phases. Signal Process: Image Commun 65:107–114

    Google Scholar 

  13. Li H, Gong M, Miao Q, Wang B (2018) Interactive active contour with kernel descriptor. Inf Sci 450:53–72

    Article  MathSciNet  Google Scholar 

  14. Chen D-J, Chen H-T, Chang L-W (2020) SwipeCut: Interactive Segmentation via Seed Grouping. IEEE Trans Circuits Syst Video Technol 30(9):2959–2970

    Article  Google Scholar 

  15. Bampis CG, Maragos P, Bovik AC (2016) Graph-driven diffusion and random walk schemes for image segmentation. IEEE Trans Image Process 26(1):35–50

    Article  MathSciNet  MATH  Google Scholar 

  16. Wang T, Ji Z, Sun Q, Chen Q, Ge Qi, Yang J (2018) Diffusive likelihood for interactive image segmentation. Pattern Recogn 79:440–451

    Article  Google Scholar 

  17. Breve F (2019) Interactive image segmentation using label propagation through complex networks. Expert Syst Appl 123:18–33

    Article  Google Scholar 

  18. Ramadan Hiba, Lachqar Chaymae, Tairi Hamid (2020) A survey of recent interactive image segmentation methods. Computational Vis Media 6(4):355–384

    Article  Google Scholar 

  19. Ning J, Zhang L, Zhang D, Chengke Wu (2010) Interactive image segmentation by maximal similarity based region merging. Pattern Recogn 43(2):445–456

    Article  MATH  Google Scholar 

  20. Dong X, Shen J, Shao L, Yang M-H (2015) Interactive cosegmentation using global and local energy optimization. IEEE Trans Image Process 24(11):3966–3977

    Article  MathSciNet  MATH  Google Scholar 

  21. Dong Xingping, Shen Jianbing, Shao Ling, Van Gool Luc (2015) Sub-Markov random walk for image segmentation. IEEE Trans Image Process 25(2):516–527

    Article  MathSciNet  MATH  Google Scholar 

  22. Tandel Gopal S, Tiwari Ashish, Kakde OG (2021) Performance Optimisation of Deep Learning Models using Majority Voting Algorithm for Brain Tumour Classification. Computers in Biology and Medicine 135:104564

    Article  Google Scholar 

  23. Li R, Chen X (2022) An efficient interactive multi-label segmentation tool for 2D and 3D medical images using fully connected conditional random field. Comput Methods Programs Biomed 213:106534

    Article  Google Scholar 

  24. García-Gutiérrez J, Mateos-García D, Garcia M, Riquelme-Santos JC (2015) An evolutionary-weighted majority voting and support vector machines applied to contextual classification of LiDAR and imagery data fusion. Neurocomputing 163:17–24

    Article  Google Scholar 

  25. Tsai Kuo Chun, Wenyi Hu, Xuqing Wu, Chen Jiefu, Han Zhu (2019) Automatic first arrival picking via deep learning with human interactive learning. IEEE Trans Geosci Remote Sens 58(2):1380–1391

    Article  Google Scholar 

  26. Cornelio Cristina, Donini Michele, Loreggia Andrea, Pini Maria Silvia, Rossi Francesca (2021) Voting with random classifiers (VORACE): theoretical and experimental analysis. Autonomous Agents Multi-Agent Syste 35(2):1–31

    Google Scholar 

  27. Cascado-Caballero D, Diaz-del-Rio F, Cagigas-Muñiz D, Rios-Navarro A, Guisado-Lizar J-L, Pérez-Hurtado I, Riscos-Núñez A (2022) MAREX: A general purpose hardware architecture for membrane computing. Inf Sci 584:360–386

    Article  Google Scholar 

  28. Paun, Gheorghe, Grzegorz Rozenberg, and Arto Salomaa. 2010 The Oxford handbook of membrane computing. Oxford University Press, Inc

  29. Dalvand M, Fathi A, Kamran A (2021) Flooding region growing: a new parallel image segmentation model based on membrane computing. J Real-Time Image Proc 18(1):37–55

    Article  Google Scholar 

  30. Song B, Zhang C, Pan L (2017) Tissue-like P systems with evolutional symport/antiport rules. Inf Sci 378:177–193

    Article  MathSciNet  MATH  Google Scholar 

  31. Alsalibi Bisan, Mirjalili Seyedali, Abualigah Laith, Gandomi Amir H (2022) A comprehensive survey on the recent variants and applications of membrane-inspired evolutionary algorithms. Arch Computational Methods Eng 1–17:3041–3057

    Article  MathSciNet  Google Scholar 

  32. Peng H, Li Bo, Wang J, Song X, Wang T, Valencia-Cabrera L, Pérez-Hurtado I, Riscos-Núñez A, Pérez-Jiménez MJ (2020) Spiking neural P systems with inhibitory rules. Knowl-Based Syst 188:105064

    Article  Google Scholar 

  33. Wu Tingfang, Pan Linqiang, Qiang Yu, Tan Kay Chen (2020) Numerical spiking neural P systems. IEEE Trans Neural Netw Learning Syst 32(6):2443–2457

    Article  MathSciNet  Google Scholar 

  34. Peng H, Yang J, Wang J, Wang T, Sun Z, Song X, Luo X, Huang X (2017) Spiking neural P systems with multiple channels. Neural Netw 95:66–71

    Article  MATH  Google Scholar 

  35. Peng H, Wang J, Pérez-Jiménez MJ, Riscos-Núñez A (2019) Dynamic threshold neural P systems. Knowl-Based Syst 163:875–884

    Article  Google Scholar 

  36. Xue J, Wang Z, Kong D, Wang Y, Liu X, Fan W, Yuan S, Niu S, Li D (2021) Deep ensemble neural-like P systems for segmentation of central serous chorioretinopathy lesion. Inf Fusion 65:84–94

    Article  Google Scholar 

  37. Li Bo, Peng H, Wang J, Huang X (2020) Multi-focus image fusion based on dynamic threshold neural P systems and surfacelet transform. Knowl-Based Syst 196:105794

    Article  Google Scholar 

  38. Díaz-Pernil D, Peña-Cantillana F, Gutiérrez-Naranjo MA (2013) A parallel algorithm for skeletonizing images by using spiking neural P systems. Neurocomputing 115:81–91

    Article  Google Scholar 

  39. Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916

    Article  Google Scholar 

  40. Gite Shilpa, Mishra Abhinav, Kotecha Ketan (2022) Enhanced lung image segmentation using deep learning. Neural Comput Appl 5(2):1–15

    Google Scholar 

  41. Schuman Catherine D, Kulkarni Shruti R, Maryam Parsa J, Mitchell Parker, Date Prasanna, ans Bill Kay. (2022) Opportunities for neuromorphic computing algorithms and applications. Nat Comput Sci 2:10–19

    Article  Google Scholar 

Download references

Funding

(Information that explains whether and by whom the research was supported): No funds, grants, or other support was received for this study.

Author information

Authors and Affiliations

  1. Department of Computer Engineering and Information Technology, Razi University, Kermanshah, Iran

    Mehran Dalvand, Abdolhossein Fathi & Arezoo Kamran

Authors
  1. Mehran Dalvand
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdolhossein Fathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arezoo Kamran
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conceptualization and design. Material preparation, data collection, analysis and writing of the first draft of paper were performed by Mehran Dalvand. The supervision and writing, review and editing of the manuscript were done by Abdolhossein Fathi and Arezoo Kamran. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Abdolhossein Fathi.

Ethics declarations

Conflicts of interest

(Include appropriate disclosures): The authors have no conflicts of interest to declare that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dalvand, M., Fathi, A. & Kamran, A. Spiking Neural P System with weight model of majority voting technique for reliable interactive image segmentation. Neural Comput & Applic 35, 9035–9051 (2023). https://doi.org/10.1007/s00521-022-08162-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-08162-9

Keywords

Search

Navigation