Histopathological image classification using enhanced bag-of-feature with spiral biogeography-based optimization

  • Raju PalEmail author
  • Mukesh Saraswat


An exponential growth of histopathological digital images over the Internet requires an efficient method for organizing them properly for better retrieval and analysis process. For the same, an automatic histopathological image classification system can be useful. Moreover, such classification system may also be used to identify the inflamed and healthy tissues from tissue image datasets. However, complex background structures and heterogeneity among histopathological tissue images make it a complicated process. Therefore, this paper introduces an innovative method for categorization of histopathological images using an enhanced bag-of-feature framework. To obtain the optimal visual words in bag-of-features, a new spiral biogeography-based optimization algorithm has been proposed which introduces a spiral search and random search in the mutation operator to generate the suitability index variables. The efficacy of the spiral biogeography-based optimization algorithm has been tested on CEC 2017 benchmarks problems. Moreover, the applicability of the proposed classification method has been observed on two histopathological image datasets, Blue Histology image dataset and ADL Histopathological image dataset. The efficacy of the spiral biogeography-based optimization algorithm based bag-of-features method has been analyzed and compared with other state-of-the-art methods with respect to average accuracy, recall, precision, and F1-measure parameters.


Histopathological image classification Bag-of-features Biogeography-based optimization 



Authors are thankful to Science and Engineering Research Board, Department of Science & Technology, Government of India, New Delhi, India for funding this work as part of the project (ECR/2016/000844).


  1. 1.
    Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B (2009) Histopathological image analysis: a review. IEEE Rev Biomed Eng 2:147–171Google Scholar
  2. 2.
    Saraswat M, Arya KV (2014) Automated microscopic image analysis for leukocytes identification: a survey. Micron 65:20–33Google Scholar
  3. 3.
  4. 4.
    Gutiérrez R, Rueda A, Romero E (2013) Learning semantic histopathological representation for basal cell carcinoma classification. In: Proceedings of medical imaging: digital pathology, vol 8676, pp 86760U–1Google Scholar
  5. 5.
    Vu TH, Mousavi HS, Monga V, Rao G, Rao UKA (2016) Histopathological image classification using discriminative feature-oriented dictionary learning. IEEE Trans Med Imaging 35(3):738–751Google Scholar
  6. 6.
    Jun X, Luo X, Wang G, Gilmore H, Madabhushi A (2016) A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing 191:214–223Google Scholar
  7. 7.
    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 1. IEEE, pp 886–893Google Scholar
  8. 8.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110Google Scholar
  9. 9.
    Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recognit 29(1):51–59Google Scholar
  10. 10.
    Mittal H, Saraswat M (2017) Classification of histopathological images through bag-of-visual-words and gravitational search algorithm. In: International conference soft computing for problem solvingGoogle Scholar
  11. 11.
    Caicedo JC, Cruz A, Gonzalez FA (2009) Histopathology image classification using bag of features and kernel functions. In: Proceedings of conference on artificial intelligence in medicine in Europe. Springer, pp 126–135Google Scholar
  12. 12.
    Bong CW, Rajeswari M (2012) Multiobjective clustering with metaheuristic: current trends and methods in image segmentation. IET Image Process 6(1):1–10MathSciNetGoogle Scholar
  13. 13.
    Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713Google Scholar
  14. 14.
    Ma H, Simon D, Siarry P, Yang Z, Fei M (2017) Biogeography-based optimization: a 10-year review. IEEE Trans Emerg Top Comput Intell 1(5):391–407Google Scholar
  15. 15.
    Wu G, Mallipeddi R, Suganthan PN (2016) Problem definitions and evaluation criteria for the cec 2017 competition on constrained real-parameter optimization. National University of Defense Technology, Changsha, Hunan, PR China and Kyungpook National University, Daegu, South Korea and Nanyang Technological University, Singapore, Technical ReportGoogle Scholar
  16. 16.
    Mohamed AW, Hadi AA, Fattouh AM, Jambi KM (2017) LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems. In: Proceedings of IEEE congress on evolutionary computationGoogle Scholar
  17. 17.
    Niu Q, Zhang L, Li K (2014) A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells. Energy Convers Manag 86:1173–1185Google Scholar
  18. 18.
    Garg V, Deep K (2016) Performance of laplacian biogeography-based optimization algorithm on CEC 2014 continuous optimization benchmarks and camera calibration problem. Swarm Evol Comput 27:132–144Google Scholar
  19. 19.
    Pal R, Saraswat M (2017) Data clustering using enhanced biogeography-based optimization. In: Proceedings of international conference on contemporary computingGoogle Scholar
  20. 20.
    Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191Google Scholar
  21. 21.
    Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61Google Scholar
  22. 22.
    Mittal H, Pal R, Kulhari A, Saraswat M (2016) Chaotic kbest gravitational search algorithm (ckgsa). In: Proceedings of international conference on contemporary computing. IEEE, pp 1–6Google Scholar
  23. 23.
    Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67Google Scholar
  24. 24.
    Srinivas U, Mousavi HS, Monga V, Hattel A, Jayarao B (2014) Simultaneous sparsity model for histopathological image representation and classification. IEEE Trans Med Imaging 33(5):1163–1179Google Scholar
  25. 25.
    Feng Z (2012) Data clustering using genetic algorithms. Evolutionary computation: project report, CSE484Google Scholar
  26. 26.
    Dimitrovski I, Kocev D, Loskovska S, Džeroski S (2011) Hierarchical annotation of medical images. Pattern Recognit 44(10–11):2436–2449Google Scholar
  27. 27.
    Cruz-Roa A, Díaz G, Romero E, González FA (2011) Automatic annotation of histopathological images using a latent topic model based on non-negative matrix factorization. J Pathol Inf 2:1–10Google Scholar
  28. 28.
    Díaz G, Romero E (2010) Histopathological image classification using stain component features on a plsa model. In: Proceedings of Iberoamerican congress on pattern recognition. Springer, pp 55–62Google Scholar
  29. 29.
    Zhang R, Shen J, Wei F, Li X, Sangaiah AK (2017) Medical image classification based on multi-scale non-negative sparse coding. Artif Intell Med 83:44–51Google Scholar
  30. 30.
    Kingsland S (2002) The theory of island biogeography. J Hist Biol 35(1):178–179Google Scholar
  31. 31.
    Pal R, Saraswat M (2017) Improved biogeography-based optimization. Int J Adv Intell Paradigms, (In Press)Google Scholar
  32. 32.
    Du D, Simon D, Ergezer M (2009) Biogeography-based optimization combined with evolutionary strategy and immigration refusal. In: Proceedings of IEEE international conference on systems, man and cybernetics, pp 997–1002Google Scholar
  33. 33.
    Gong W, Cai Z, Ling CX, Li H (2010) A real-coded biogeography-based optimization with mutation. Appl Math Comput 216(9):2749–2758MathSciNetzbMATHGoogle Scholar
  34. 34.
    Ma H, Simon D (2011) Blended biogeography-based optimization for constrained optimization. Eng Appl Artif Intell 24(3):517–525Google Scholar
  35. 35.
    Lohokare MR, Pattnaik SS, Panigrahi BK, Das S (2013) Accelerated biogeography-based optimization with neighborhood search for optimization. Appl Soft Comput 13(5):2318–2342Google Scholar
  36. 36.
    Gong W, Cai Z, Ling CX (2010b) De/bbo: a hybrid differential evolution with biogeography-based optimization for global numerical optimization. Soft Comput 15(4):645–665Google Scholar
  37. 37.
    Lim WL, Wibowo A, Desa MI, Haron H (2016) A biogeography-based optimization algorithm hybridized with tabu search for the quadratic assignment problem. Comput Intell Neurosci 2016:27Google Scholar
  38. 38.
    Ma H (2010) An analysis of the equilibrium of migration models for biogeography-based optimization. Inf Sci 180(18):3444–3464zbMATHGoogle Scholar
  39. 39.
    Pakhira MK, Bandyopadhyay S, Maulik U (2004) Validity index for crisp and fuzzy clusters. Pattern Recognit 37(3):487–501zbMATHGoogle Scholar
  40. 40.
    Mahoney MS (1994) The mathematical career of Pierre de Fermat, 1601–1665. Princeton University Press, PrincetonzbMATHGoogle Scholar
  41. 41.
    Awad NH, Ali MZ, Liang JJ, Qu BY, Suganthan PN Problem definitions and evaluation criteria for the cec 2017 special session and competition on single objective bound constrained real-parameter numerical optimization. Technical Report. NTU, SingaporeGoogle Scholar
  42. 42.
    Theodorsson-Norheim E (1987) Friedman and quade tests: basic computer program to perform nonparametric two-way analysis of variance and multiple comparisons on ranks of several related samples. J Comput Biol Med 17 (2):85–99Google Scholar
  43. 43.
    Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6(1):37–66Google Scholar
  44. 44.
    Lin We-C, Tsai C-F, Chen Z-Y, Ke S-W (2016) Keypoint selection for efficient bag-of-words feature generation and effective image classification. Inf Sci 329:33–51Google Scholar
  45. 45.
    Monga V (2018) Adl data set.
  46. 46.
    Chapelle O, Haffner P, Vapnik VN (1999) Support vector machines for histogram-based image classification. IEEE Trans Neural Netw 10(5):1055–1064Google Scholar
  47. 47.
    Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227Google Scholar
  48. 48.
    Chen Y, Nasrabadi NM, Tran TD (2011) Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans Geosci Remote Sens 49(10):3973–3985Google Scholar
  49. 49.
    Orlov N, Shamir L, Macura T, Johnston J, Eckley DM, Goldberg IG (2008) Wnd-charm: multi-purpose image classification using compound image transforms. Pattern Recognit Lett 29(11):1684–1693Google Scholar
  50. 50.
    Wang G-G, Gandomi AH, Alavi AH (2014) An effective krill herd algorithm with migration operator in biogeography-based optimization. Appl Math Modell 38(9-10):2454–2462MathSciNetzbMATHGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Jaypee Institute of Information TechnologyNoidaIndia

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