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Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images

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Abstract

This paper presents an automatic mitosis detection approach of histopathology slide imaging based on using neutrosophic sets (NS) and moth-flame optimization (MFO). The proposed approach consists of two main phases, namely candidate’s extraction and candidate’s classification phase. At candidate’s extraction phase, Gaussian filter was applied to the histopathological slide image and the enhanced image was mapped into the NS domain. Then, morphological operations have been implemented to the truth subset image for more enhancements and focus on mitosis cells. At candidate’s classification phase, several features based on statistical, shape, texture and energy features were extracted from each candidate. Then, a principle of the meta-heuristic MFO algorithm was adopted to select the best discriminating features of mitosis cells. Finally, the selected features were used to feed the classification and regression tree (CART). A benchmark dataset consists of 50 histopathological images was adopted to evaluate the performance of the proposed approach. The adopted dataset consists of five distinct breast pathology slides. These slides were stained with H&E acquired by Aperio XT scanners with 40-x magnification. The total number of mitoses in 50 database images is 300, which were annotated by an expert pathologist. Experimental results reveal the capability of the MFO feature selection algorithm for finding the optimal feature subset which maximizing the classification performance compared to well-known and other meta-heuristic feature selection algorithms. Also, the high obtained value of accuracy, recall, precision and f-score for the adopted dataset prove the robustness of the proposed mitosis detection and classification approach. It achieved overall 65.42 % f-score, 66.03 % recall, 65.73 % precision and accuracy 92.99 %. The experimental results show that the proposed approach is fast, robust, efficient and coherent. Moreover, it could be used for further early diagnostic suspicion of breast cancer.

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References

  1. Şahin R (2014) Neutrosophic hierarchical clustering algoritms. Neutrosophic Sets and Systems. 2:18–25

    Google Scholar 

  2. Anter A, Hassenian A, AbuElSoud M (2014) Neutrosophic sets and fuzzy c-means clustering for improving ct liver image segmentation. In: 5th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2014. Springer, pp 193–203

  3. Chang F, Yang C, Rong X, Lu C (2010) Tree decomposition for large-scale svm problems. J Mach Learn Res 11:2935–2972

    MathSciNet  MATH  Google Scholar 

  4. Cires D, Giusti A, Gambardella L, Schmidhuber J (2013) Mitosis Detection in breast cancer histology images with deep neural networks. In: 16th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Springer, Nagoya, pp 411–418

  5. Contest I (2012) Mitosis detection in breast cancer histological images. http://ipal.cnrs.fr/ICPR2012/

  6. Damjanov I, Fan F (2007) Cancer grading manual

  7. Elston C, Ellis I (1991) Pathological prognostic factors in breast cancer. i. the value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology. 19(5):403–410

    Article  Google Scholar 

  8. Guican M, Boucheron L, Can A, Madabhushi A, Rajpoot N, Yener B (2009) Histopathological image analysis: a review. Biomed Eng EEE 2:147–171

    Google Scholar 

  9. Guo Z, Zhang D (2010) A completed modelling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657–1663

    Article  MathSciNet  Google Scholar 

  10. Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621

    Article  Google Scholar 

  11. Huang C, Lee H (2012) Automated mitosis detection based on exclusive independent component analysis. In: Proceedings of the IEEE international conference on pattern recognition. IEEE, Tsukuba, pp 1856–1859

  12. Irshad H (2013) Automated mitosis detection in histopathology using morphological and multi-channel statistics features. J Pathol Informatics 4(1):4–10

    Article  Google Scholar 

  13. Irshad H (2014) Automated mitosis detection in color and multi-spectral high-content images in histopathology: application to breast cancer grading in digital pathology. In: 12th European Congress of Digital Pathology, Paris, pp 18–22

  14. Irshad H, Jalali S, Roux L, Racoceanu D, Hwee LJ, Le Naour G (2013) Automated mitosis detection using texture, sift features and hmax biologically inspired approach. J Pathol Inform 4(2):12–18

    Article  Google Scholar 

  15. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J Glob Optim 39(3):459–471

    Article  MathSciNet  MATH  Google Scholar 

  16. Khalid M, Abd Elfattaha M, Hassanien A, Schaefer G (2014) A binarization algorithm for historical arabic manuscript images using a neutrosophic approach. In: 9th International Conference on In Computer Engineering and Systems (ICCES). IEEE, pp 266–270

  17. Khan A, ElDaly H, Rajpoot N (2013) A gamma-gaussian mixture model for detection of mitotic cells in breast cancer histopathology images. J Pathol Inf 4(1):1–11

    Article  Google Scholar 

  18. Khan AM, El-Daly H, Rajpoot NM 2012. A gamma-gaussian mixture model for detection of mitotic cells in breast cancer histopathology images. In: 21st International conference on Pattern Recognition. IEEE, Tsukuba, pp 149–152

  19. Kotecha JH (2003) Gaussian particle filtering. IEEE Trans Signal Process 51(10):2592–2601

    Article  MathSciNet  Google Scholar 

  20. Lawrence R, Wright A (2001) Rule-based classification systems using classification and regression tree (cart) analysis. Photogramm.Eng Remote Sens 67(10):1137–1142

    Google Scholar 

  21. Lopez A, Li X, Yu W (2013) Support vector machine classification for large datasets using decision tree and fisher linear discriminant, vol 36. Gener Comput Syst Elsevier, pp 57–65

  22. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst Elsevier 89:228–249

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Naik S, Doyle S, Agner S, Madabhushi A, Feldman M, Tomaszewski J (2008) Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. In: Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, pp 284–287

  25. Nateghi R, Danyali H, Helfroush M, Tashk A (2014) Intelligent cad system for automatic detection of mitotic cells from breast cancer histology slide images based on teaching-learning-based optimization. Comput Biol J 2014:1–9

    Article  Google Scholar 

  26. NCI (2012) National cancer institute. (Last Accessed: 14/02/2016); Available from: http://www.cancer.gov/cancertopics

  27. Palm C, Lehmann TM (2002) Classification of color textures by gabor filtering. Machine Graphics and Vision 3(11):195–220

    Google Scholar 

  28. Roullier V, Lézoraya O, Elmoataz A (2011) Multi-resolution graph-based analysis of histopathological whole slide images: application to mitotic cell extraction and visualization. Comput Med Imaging Graph Elsevier. 35(7):603–615

    Article  Google Scholar 

  29. Roux L, Tutac A, Lomenie N, Balensi D, Racoceanu D, Veillard A, Leow W, Klossa J, Putti T (2009) A cognitive virtual microscopic framework for knowlege-based exploration of large microscopic images in breast cancer histopathology. In: Annual international conference of the IEEE in engineering in medicine and biology society, Minneapolis, pp 3697–3702

  30. Sharma R, sharma R (2014) Image segmentation using morphological operation for automatic region growing. International Journal of Innovative Research in Computer and Communication Engineering 2(9):5686–5693

    Google Scholar 

  31. Simon D, Member S, Simon D (2010) Analytic confusion matrix bounds for fault detection and isolation using a sum-of-squared-residuals approach. IEEE Trans Reliab 59(2):287–296

    Article  Google Scholar 

  32. Sommer C, Fiaschi L, Hamprecht FA, Gerlich DW (2012) Learning-based mitotic cell detection in histopathological images. In: 21st International Conference on Pattern Recognition (ICPR, vol 2012. IEEE, Tsukuba, pp 2306–2309

  33. Sutar GT, Shah AV (2014) Number plate recognition using improved segmentation. Int J Innov Res Sci Eng Technol 3(5):12360–12368

    Google Scholar 

  34. Tang X (1998) Texture information in run-length matrices. IEEE Trans Image Process 7(11):1057–7149

    Google Scholar 

  35. Tashk A, Helfroush M, Danyali H, Akbarzadehjahromi M (2013) An automatic mitosis detection method for breast cancer histopathology slide images based on objective and pixel-wise textural features classification. In: Proceedings of the 5th Conference on Information and Knowledge Technology, Tehran, pp 406–410

  36. Tashk A, Helfroush M, Danyali H, Akbarzadehjahromi M (2014a) A cad mitosis detection system from breast cancer histology images based on fused features. In: Proceedings of the 22nd Iranian conference on electrical engineering, Tehran, pp 1925–1927

  37. Tashk A, Helfroush MS, Danyali H, Akbarzadeh-Jahromi M (2014b) A novel cad system for mitosis detection using histopathology slide images. Journal of Medical Signals and Systems 4(2):139–149

  38. Tek F (2013) Mitosis detection using generic features and an ensemble of cascade adaboosts. J Pathol Inform 4(1):4–12

    Article  Google Scholar 

  39. V G (2012) An overview of classification algorithms for imbalanced. Int J Emerg Technol Adv Eng 2(4):42–47

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Authors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Gehad Ismail Sayed & Aboul Ella Hassanien

  2. Scientific Research Group, Giza, Egypt

    Gehad Ismail Sayed & Aboul Ella Hassanien

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  1. Gehad Ismail Sayed
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  2. Aboul Ella Hassanien
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Correspondence to Gehad Ismail Sayed.

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Sayed, G.I., Hassanien, A.E. Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images. Appl Intell 47, 397–408 (2017). https://doi.org/10.1007/s10489-017-0897-0

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  • DOI: https://doi.org/10.1007/s10489-017-0897-0

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