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An efficient nuclei segmentation method based on roulette wheel whale optimization and fuzzy clustering

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Abstract

Image segmentation is a technique in which an image is partitioned into different categories or regions that correspond to objects or parts of objects. The performance of segmentation methods is generally reduced for the hematoxylin and eosin stained histopathological images due to complex background and varying intensity values. Therefore, in this paper, the roulette wheel selection whale optimization based fuzzy clustering method is introduced for the nuclei segmentation of histopathological images. The proposed clustering method finds the optimal clusters using the objective function that reduces the sum of squared error or compactness. The performance of the proposed clustering method has been examined on the histopathological image dataset of Triple-Negative Breast Cancer patients and compared with k-means and fuzzy c-means in respect of F1 score and aggregated Jaccard index. The proposed method attains 0.6701 mean F1 scores and 0.7387 mean aggregated Jaccard index value, which are the best values among other methods. The experimental outcomes validate the efficiency of the introduced method over the other clustering-based segmentation methods.

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References

  1. Pal R, Saraswat M (2018) Enhanced bag of features using alexnet and improved biogeography-based optimization for histopathological image analysis. In: Proceedings of eleventh international conference on contemporary computing, pp 1–6

  2. Saraswat M, Arya K (2014) Automated microscopic image analysis for leukocytes identification: a survey. Micron 65:20–33

    Article  Google Scholar 

  3. Zhao M, Tang H, Guo J, Sun Y (2016) A data clustering algorithm using cuckoo search. In: Lecture Notes in Frontier Computing, Springer, pp 225–230

  4. Szeliski R (2010) Computer vision: algorithms and applications. Springer, Berlin

    MATH  Google Scholar 

  5. Veta M, Pluim JP, Van Diest PJ, Viergever MA (2014) Breast cancer histopathology image analysis: a review. IEEE Trans Biomed Eng 61:1400–1411

    Article  Google Scholar 

  6. Veta M, Huisman A, Viergever MA, van Diest PJ, Pluim JP (2011) Marker-controlled watershed segmentation of nuclei in h&e stained breast cancer biopsy images. In: Proceedings of IEEE biomedical imaging: from nano to macro, IEEE, pp 618–621

  7. Xing F, Yang L (2016) Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: a comprehensive review. IEEE Rev Biomed Eng 9:234–263

    Article  Google Scholar 

  8. Naylor P, Laé M, Reyal F, Walter T (2017) Nuclei segmentation in histopathology images using deep neural networks. In: Proceedings of IEEE international symposium on biomedical imaging, IEEE, pp 933–936

  9. Hou L, Nguyen V, Kanevsky AB, Samaras D, Kurc TM, Zhao T, Gupta RR, Gao Y, Chen W, Foran D et al (2019) Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images. Pattern Recogn 86:188–200

    Article  Google Scholar 

  10. Gopinath B, Gupt B (2010) Majority voting based classification of thyroid carcinoma. Procedia Comput Sci 2:265–271

    Article  Google Scholar 

  11. Sharma H, Arya K, Saraswat M (2014) Artificial bee colony algorithm for automatic leukocytes segmentation in histopathological images. In: Proceedings of the international conference on industrial and information systems, IEEE, pp 1–6

  12. Vink J, Van Leeuwen M, Van Deurzen C, De Haan G (2013) Efficient nucleus detector in histopathology images. J Microsc 249:124–135

    Article  Google Scholar 

  13. Jung C, Kim C, Chae SW, Oh S (2010) Unsupervised segmentation of overlapped nuclei using bayesian classification. IEEE Trans Biomed Eng 57:2825–2832

    Article  Google Scholar 

  14. Wang P, Hu X, Li Y, Liu Q, Zhu X (2016) Automatic cell nuclei segmentation and classification of breast cancer histopathology images. Sig Process 122:1–13

    Article  Google Scholar 

  15. Jorgensen AS, Rasmussen AM, Andersen NKM, Andersen SK, Emborg J, Roge R, Ostergaard LR (2017) Using cell nuclei features to detect colon cancer tissue in hematoxylin and eosin stained slides. Cytom Part A 91:785–793

    Article  Google Scholar 

  16. Zheng Y, Jiang Z, Zhang H, Xie F, Ma Y, Shi H, Zhao Y (2018) Histopathological whole slide image analysis using context-based cbir. IEEE Trans Med Imaging 1:1–17

    Google Scholar 

  17. Jain AK (2010) Data clustering: 50 years beyond k-means. Pattern Recogn Lett 31:651–666

    Article  Google Scholar 

  18. Baraldi A, Blonda P (1999) A survey of fuzzy clustering algorithms for pattern recognition. i. IEEE Trans Syst Man Cybernet Part B (Cybernet) 29:778–785

    Article  Google Scholar 

  19. Çetin M, Dokur Z, Ölmez T (2019) Fuzzy local information c-means algorithm for histopathological image segmentation. In: Scientific meeting on electrical-electronics & biomedical engineering and computer science, IEEE, pp 1–6

  20. Tan XJ, Mustafa N, Mashor MY, Ab Rahman KS (2019) An improved initialization based histogram of k-mean clustering algorithm for hyperchromatic nucleus segmentation in breast carcinoma histopathological images. In: 10th international conference on robotics. vision, signal processing and power applications, Springer, pp 529–535

  21. Hancer E, Karaboga D (2017) A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number. Swarm Evolut Comput 32:49–67

    Article  Google Scholar 

  22. Anari B, Torkestani JA, Rahmani A (2017) Automatic data clustering using continuous action-set learning automata and its application in segmentation of images. Appl Soft Comput 51:253–265

    Article  Google Scholar 

  23. Basavanhally A, Madabhushi A (2013) Em-based segmentation-driven color standardization of digitized histopathology. In: Medical imaging: digital pathology, international society for optics and photonics

  24. Koohababni NA, Jahanifar M, Gooya A, Rajpoot N (2018) Nuclei detection using mixture density networks. In: International workshop on machine learning in medical imaging, Springer, pp 241–248

  25. Jothi JAA, Rajam VMA (2015) Segmentation of nuclei from breast histopathology images using pso-based otsu’s multilevel thresholding. In: Artificial intelligence and evolutionary algorithms in engineering systems, Springer, pp 835–843

  26. Mittal H, Saraswat M (2019) An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering. Swarm Evolut Comput 45:15–32

    Article  Google Scholar 

  27. Abdolhoseini M, Kluge MG, Walker FR, Johnson SJ (2019) Segmentation of heavily clustered nuclei from histopathological images. Sci Rep 9(1):4551

    Article  Google Scholar 

  28. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  29. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  30. Rajabioun R (2011) Cuckoo optimization algorithm. Appl Soft Comput 11(8):5508–5518

    Article  Google Scholar 

  31. Pandey AC, Pal R, Kulhari A (2018) Unsupervised data classification using improved biogeography based optimization. Int J Syst Assur Eng Manag 9(4):821–829

    Article  Google Scholar 

  32. Srinivas M, Patnaik LM (1994) Genetic algorithms: a survey. Computer 27:17–26

    Article  Google Scholar 

  33. Gao S, Vairappan C, Wang Y, Cao Q, Tang Z (2014) Gravitational search algorithm combined with chaos for unconstrained numerical optimization. Appl Math Comput 231:48–62

    MathSciNet  MATH  Google Scholar 

  34. Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483

    Article  Google Scholar 

  35. Yang XS (2009) Firefly algorithms for multimodal optimization. Stochastic algorithms: foundations and applications. Springer, Berlin, pp 169–178

    Chapter  Google Scholar 

  36. Price KV (2013) Differential evolution. In: Handbook of Optimization, Springer, Berlin, pp 187–214

  37. Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359

    Article  MathSciNet  Google Scholar 

  38. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization, Springer, pp 65–74

  39. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  40. Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312

    Article  Google Scholar 

  41. Lipowski A, Lipowska D (2012) Roulette-wheel selection via stochastic acceptance. Physica A 391(6):2193–2196

    Article  Google Scholar 

  42. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Article  Google Scholar 

  43. (2018) Multi-organ Nuclei Segmentation Challenge (MICCAI 2018). https://peterjacknaylor.github.io. Accessed 19 July 2018

  44. Mahmood F, Borders D, Chen R, McKay GN, Salimian KJ, Baras A, Durr NJ (2018) Deep adversarial training for multi-organ nuclei segmentation in histopathology images. arXiv preprint arXiv:181000236

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Authors and Affiliations

  1. Department of Comuter Science and Engineering, Banasthali Vidyapith, Niwai, Rajasthan, India

    Susheela Vishnoi

  2. Swami Keshvanand Institute of Technology, Management and Gramothan, Jaipur, India

    Susheela Vishnoi

  3. Department of Computer Science, Banasthali Vidyapith, Niwai, Rajasthan, India

    Ajit Kumar Jain & Pradeep Kumar Sharma

Authors
  1. Susheela Vishnoi
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  2. Ajit Kumar Jain
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  3. Pradeep Kumar Sharma
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Correspondence to Susheela Vishnoi.

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Vishnoi, S., Jain, A.K. & Sharma, P.K. An efficient nuclei segmentation method based on roulette wheel whale optimization and fuzzy clustering. Evol. Intel. 14, 1367–1378 (2021). https://doi.org/10.1007/s12065-019-00288-5

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  • DOI: https://doi.org/10.1007/s12065-019-00288-5

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