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Fuzzy modified cuckoo search for biomedical image segmentation

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Abstract

In this article, a new method is proposed for biomedical image segmentation. The proposed method for biomedical image segmentation will be known as fuzzy modified cuckoo search (FMCS). This method falls under the category of unsupervised classification (i.e., clustering). In this work, the concept of a well-known metaheuristic method called cuckoo search is extended, modified, and combined with the modified type 2 fuzzy C-means algorithm, and the name is given accordingly. FMCS method uses a modified cuckoo search to find the optimum cluster centers based on fuzzy membership. The proposed FMCS technique fuses the idea of type 2 fuzzy sets with the MCS strategy, and it is applied in biomedical images segmentation. The proposed approach assists with deciding the clusters without having any affectability on the choice of the underlying centers. The quantity of the control variable for the MCS technique is very sensible contrasted with numerous other metaheuristics approaches. The MCS strategy can come to the global optima even subsequent to stalling out in a neighborhood optimum. The proposed method is applied to different biomedical images and compared with several standard optimization methods like genetic algorithm, particle swarm optimization, cuckoo search, etc. The proposed method does not suffer from the choice of initial cluster centers because it exploits the random behavior of the cuckoo search to initialize the cluster centers. Moreover, FMCS outperforms some of the standard methods in terms of the rate of convergence and other segmentation parameters. The proposed approach blends the type 2 fuzzy system in the modified cuckoo search procedure for efficient biomedical image segmentation. The superiority of the proposed method is verified by both quantitative and qualitative measures.

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References

  1. Qaiser T, Tsang Y-W, Taniyama D et al (2019) Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features. Med Image Anal 55:1–14. https://doi.org/10.1016/J.MEDIA.2019.03.014

    Article  Google Scholar 

  2. Zhang R, Chung ACS (2021) MedQ: lossless ultra-low-bit neural network quantization for medical image segmentation. Med Image Anal 73:102200. https://doi.org/10.1016/J.MEDIA.2021.102200

    Article  Google Scholar 

  3. Liu Q, Chen C, Qin J et al (2021) FedDG: federated domain generalization on medical image segmentation via episodic learning in continuous frequency space, pp 1013–1023

  4. Gao Y, Zhou M, Metaxas DN (2021) UTNet: a hybrid transformer architecture for medical image segmentation, pp 61–71. https://doi.org/10.1007/978-3-030-87199-4_6

  5. Wang K, Zhan B, Zu C et al (2021) Tripled-uncertainty guided mean teacher model for semi-supervised medical image segmentation, pp 450–460. https://doi.org/10.1007/978-3-030-87196-3_42

  6. Tseng K-K, Zhang R, Chen C-M, Hassan MM (2020) DNetUnet: a semi-supervised CNN of medical image segmentation for super-computing AI service. J Supercomput 77(4):3594–3615. https://doi.org/10.1007/S11227-020-03407-7

    Article  Google Scholar 

  7. Liu X, Thermos S, O’Neil A, Tsaftaris SA (2021) Semi-supervised meta-learning with disentanglement for domain-generalized medical image segmentation, pp 307–317. https://doi.org/10.1007/978-3-030-87196-3_29

  8. Zeng G, Lerch TD, Schmaranzer F et al (2021) Semantic consistent unsupervised domain adaptation for cross-modality medical image segmentation, pp 201–210. https://doi.org/10.1007/978-3-030-87199-4_19

  9. Baur C, Denner S, Wiestler B et al (2021) Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study. Med Image Anal 69:101952. https://doi.org/10.1016/J.MEDIA.2020.101952

    Article  Google Scholar 

  10. Huang Q, Zhou Y, Tao L et al (2021) A Chan-Vese model based on the Markov chain for unsupervised medical image segmentation. Tsinghua Sci Technol 26:833–844. https://doi.org/10.26599/TST.2020.9010042

    Article  Google Scholar 

  11. Xu Y, Zhu J-Y, Chang EI-C et al (2014) Weakly supervised histopathology cancer image segmentation and classification. Med Image Anal 18:591–604. https://doi.org/10.1016/J.MEDIA.2014.01.010

    Article  Google Scholar 

  12. Torrents-Barrena J, Piella G, Masoller N et al (2019) Segmentation and classification in MRI and US fetal imaging: recent trends and future prospects. Med Image Anal 51:61–88. https://doi.org/10.1016/J.MEDIA.2018.10.003

    Article  Google Scholar 

  13. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

    Article  MATH  Google Scholar 

  14. Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10:191–203. https://doi.org/10.1016/0098-3004(84)90020-7

    Article  Google Scholar 

  15. Tolias YA, Panas SM (1998) Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions. IEEE Trans Syst Man Cybern Part A Syst Hum 28:359–369. https://doi.org/10.1109/3468.668967

    Article  Google Scholar 

  16. Yang XS, Deb S (2009) Cuckoo search via Levy flights. In: 2009 world congress on nature and biologically inspired computing, NABIC 2009—Proceedings, pp 210–214

  17. Walton S, Hassan O, Morgan K, Brown MR (2011) Modified cuckoo search: a new gradient free optimisation algorithm. Chaos Solitons Fractals 44:710–718. https://doi.org/10.1016/j.chaos.2011.06.004

    Article  Google Scholar 

  18. Chakraborty S, Chatterjee S, Dey N et al (2017) Modified cuckoo search algorithm in microscopic image segmentation of hippocampus. Microsc Res Tech. https://doi.org/10.1002/jemt.22900

    Article  Google Scholar 

  19. Chandrasekaran K, Simon SP (2012) Multi-objective scheduling problem: hybrid approach using fuzzy assisted cuckoo search algorithm. Swarm Evol Comput 5:1–16. https://doi.org/10.1016/j.swevo.2012.01.001

    Article  Google Scholar 

  20. Ding X, Xu Z, Cheung NJ, Liu X (2015) Parameter estimation of Takagi-Sugeno fuzzy system using heterogeneous cuckoo search algorithm. Neurocomputing 151:1332–1342. https://doi.org/10.1016/j.neucom.2014.10.063

    Article  Google Scholar 

  21. George G, Parthiban L (2013) FCM-FCS: hybridization of fractional cuckoo search with FCM for high dimensional data clustering process. Int Rev Comput Softw 8:2576–2585

    Google Scholar 

  22. Holland JH (1992) Genetic algorithms. Sci Am 267:66–72. https://doi.org/10.1038/scientificamerican0792-66

    Article  Google Scholar 

  23. Chakraborty S, Seal A, Roy M (2015) An elitist model for obtaining alignment of multiple sequences using genetic algorithm. In: 2nd national conference NCETAS 2015. International Journal of innovative research in science, engineering and technology, pp 61–67

  24. Pandian SR, Modrák V (2009) Possibilities, obstacles and challenges of genetic algorithm in manufacturing cell formation. Adv Logist Syst 3(1):63–70

    Google Scholar 

  25. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Neural Networks, 1995 Proceedings, IEEE Int Conf. vol 4, pp 1942–1948. https://doi.org/10.1109/ICNN.1995.488968

  26. Particle Swarm Optimization: Tutorial. http://www.swarmintelligence.org/tutorials.php. Accessed 29 Apr 2018

  27. Suresh S, Lal S (2016) An efficient cuckoo search algorithm based multilevel thresholding for segmentation of satellite images using different objective functions. Expert Syst Appl 58:184–209. https://doi.org/10.1016/j.eswa.2016.03.032

    Article  Google Scholar 

  28. Chakraborty S, Chatterjee S, Dey N et al (2017) Modified cuckoo search algorithm in microscopic image segmentation of hippocampus. Microsc Res Tech 80:1051–1072. https://doi.org/10.1002/jemt.22900

    Article  Google Scholar 

  29. Melin P, Mendoza O, Castillo O (2010) An improved method for edge detection based on interval type-2 fuzzy logic. Expert Syst Appl 37:8527–8535. https://doi.org/10.1016/j.eswa.2010.05.023

    Article  Google Scholar 

  30. Rhee FCH (2007) Uncertain fuzzy clustering: Insights and recommendations. IEEE Comput Intell Mag 2:44–56

    Google Scholar 

  31. Brajevic I, Tuba M (2014) Cuckoo search and firefly algorithm applied to multilevel image thresholding. Springer, Cham, pp 115–139

    Book  Google Scholar 

  32. Bhandari AK, Singh VK, Kumar A, Singh GK (2014) Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Syst Appl 41:3538–3560. https://doi.org/10.1016/J.ESWA.2013.10.059

    Article  Google Scholar 

  33. Agrawal S, Panda R, Bhuyan S, Panigrahi BK (2013) Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm. Swarm Evol Comput 11:16–30. https://doi.org/10.1016/J.SWEVO.2013.02.001

    Article  Google Scholar 

  34. Kurban T, Civicioglu P, Kurban R, Besdok E (2014) Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding. Appl Soft Comput 23:128–143. https://doi.org/10.1016/J.ASOC.2014.05.037

    Article  Google Scholar 

  35. Linguraru MG, Marias K, English R, Brady M (2006) A biologically inspired algorithm for microcalcification cluster detection. Med Image Anal 10:850–862. https://doi.org/10.1016/J.MEDIA.2006.07.004

    Article  Google Scholar 

  36. Brown CT, Liebovitch LS, Glendon R (2006) Lévy flights in Dobe Ju/’hoansi foraging patterns. Hum Ecol. https://doi.org/10.1007/s10745-006-9083-4

    Article  Google Scholar 

  37. Barthelemy P, Bertolotti J, Wiersma DS (2008) A Lévy flight for light. Nature 453:495–498. https://doi.org/10.1038/nature06948

    Article  Google Scholar 

  38. Hughes BD (1998) Random walks and random environments. Bull Am Math Soc 35:347–349

    Article  Google Scholar 

  39. Samoradnitsky G, Taqqu MS (1994) Stable non-Gaussian random processes: stochastic models with infinite variance. CRC Press, Boca Raton

    Google Scholar 

  40. Siswantoro A (2013) Soft computing applications and intelligent systems

  41. Mantegna RN (1994) Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Phys Rev E 49:4677–4683

    Article  Google Scholar 

  42. Chambers JM, Mallows CL, Stuck BW (1976) A method for simulating stable random variables. J Am Stat Assoc 71:340–344. https://doi.org/10.1080/01621459.1976.10480344

    Article  MathSciNet  MATH  Google Scholar 

  43. Leccardi M (2005) Comparison of three algorithms for Levy noise generation. In: Proceedings of fifth EUROMECH nonlinear dynamics conference

  44. Rhee FCH, Hwang C (2004) A type-2 fuzzy C-means clustering algorithm. In: Proceedings joint 9th IFSA world congress and 20th NAFIPS international conference (Cat. No. 01TH8569). IEEE, pp 1926–1929

  45. Salgotra R, Singh U, Saha S (2018) New cuckoo search algorithms with enhanced exploration and exploitation properties. Expert Syst Appl 95:384–420. https://doi.org/10.1016/J.ESWA.2017.11.044

    Article  Google Scholar 

  46. Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell PAMI-1:224–227. https://doi.org/10.1109/TPAMI.1979.4766909

    Article  Google Scholar 

  47. Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 13:841–847. https://doi.org/10.1109/34.85677

    Article  Google Scholar 

  48. Dunn JC (1974) Well-separated clusters and optimal fuzzy partitions. J Cybern 4:95–104. https://doi.org/10.1080/01969727408546059

    Article  MathSciNet  MATH  Google Scholar 

  49. Pal SK, Ghosh A, Shankar BU (2000) Segmentation of remotely sensed images with fuzzy thresholding, and quantitative evaluation. Int J Remote Sens 21:2269–2300. https://doi.org/10.1080/01431160050029567

    Article  Google Scholar 

  50. File: Head CT scan.jpg—Wikimedia Commons. https://commons.wikimedia.org/wiki/File:Head_CT_scan.jpg. Accessed 6 May 2018

  51. TAAF: Detection. http://www.taafonline.org/conditions/aneurysm/detection. Accessed 6 May 2018

  52. Ackerman Michael J (1998) The visible human project. In: Proceedings of the IEEE 86.3, pp 504-511

  53. Radiology MRI: Contrast Perfusion MRI. http://radiologymri.blogspot.in/2010/12/contrast-perfusion-mri.html. Accessed 6 May 2018

  54. CDC: NIOSH publications and products—application of the ILO international classification of radiographs of pneumoconioses to digital chest radiographic images (2008-139). https://www.cdc.gov/niosh/docs/2008-139/manuscript-flynn-processingdisplay.html. Accessed 6 May 2018

  55. File: medical X-Ray imaging AAC02 nevit.jpg—Wikimedia Commons. https://commons.wikimedia.org/wiki/File:Medical_X-Ray_imaging_AAC02_nevit.jpg#filehistory. Accessed 6 May 2018

  56. 3-D mammography finds more tumors, but questions remain: shots—health news: NPR. https://www.npr.org/sections/health-shots/2014/06/24/325216641/3-d-mammography-finds-more-tumors-but-questions-remain. Accessed 6 May 2018

  57. Dupuis CS, Kim YH (2015) Ultrasonography of adnexal causes of acute pelvic pain in pre-menopausal non-pregnant women. Ultrasonography 34:258–267. https://doi.org/10.14366/usg.15013

    Article  Google Scholar 

  58. Positron emission tomography (PET Scan): harvard health. https://www.health.harvard.edu/medical-devices-and-technology/positron-emission-tomography-pet-scan. Accessed 7 May 2018

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Acknowledgements

The authors would like to express their gratitude and thank the anonymous reviewers and referees for their precious comments and suggestions which are helpful in further improvement of the research work.

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  1. Department of Computer Science & Engineering, University of Kalyani, Kalyani, India

    Shouvik Chakraborty & Kalyani Mali

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  1. Shouvik Chakraborty
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Chakraborty, S., Mali, K. Fuzzy modified cuckoo search for biomedical image segmentation. Knowl Inf Syst 64, 1121–1160 (2022). https://doi.org/10.1007/s10115-022-01659-8

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