Advertisement

Acute lymphoblastic leukemia image segmentation driven by stochastic fractal search

  • 10 Accesses

Abstract

Cancer is one of the most critical disease. In particular, Leukemia is the most common type of cancer which produces an excessive quantity of leucocytes, replacing normal blood cells. Early detection of leucocytes cells can save human life. Recently, researchers have contributed to the development of computer assisted pathology techniques to automatically detect cancer at early stage. Commonly, assisted pathology systems are based on artificial vision techniques to identify cancer cells in the human body. Blood image segmentation techniques for Leukemia have been proposed based on automatic thresholding schemes involving traditional clustering methods. However, traditional clustering methods are sensitive to initial cluster positions, where the incorrect centering values results into false positive cancer diagnosis. On the other hand, Nature-Inspired Optimization Algorithms (NIOA) are stochastic search methods for finding the optimal solution for complex multimodal functions where traditional optimization approaches are not suitable to operate. Since blood image segmentation is considered as a complex computational task, NIOA methods yield an interesting alternative to proper blood cell segmentation. In this paper, the Stochastic Fractal Search (SFS) algorithm is implemented in order to provide non-false positive segmented outcomes for Leukemia identification. In the experimental study, the proposed approach is compared against traditional clustering methods as well as some NIOAs techniques. The numerical results indicate that SFS, provide superior results in terms of accuracy, time complexity, and quality parameters.

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 199

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  1. 1.

    Aja-Fernandez S, Estepar RSJ, Alberola-Lopez C, Westin C-F (2006) Image quality assessment based on local variance. In 2006 International Conference of the IEEE Engineering in Medicine and Biology Society (Vol. 1, pp. 4815–4818). IEEE. 10.1109/IEMBS.2006.259516

  2. 2.

    Alomoush MI, Oweis ZB (2018) Environmental-economic dispatch using stochastic fractal search algorithm. International Transactions on Electrical Energy Systems 28(5):e2530. https://doi.org/10.1002/etep.2530

  3. 3.

    Amin MM, Kermani S, Talebi A, Oghli MG (2015) Recognition of acute lymphoblastic leukemia cells in microscopic images using k-means clustering and support vector machine classifier. Journal of Medical Signals and Sensors, 5(1), 49–58. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/25709941

  4. 4.

    Arslan S, Ozyurek E, Gunduz-Demir C (2014) A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images. Cytometry Part A 85(6):480–490. https://doi.org/10.1002/cyto.a.22457

  5. 5.

    Betka A, Terki N, Toumi A, Hamiane M, Ourchani A (2019) A new block matching algorithm based on stochastic fractal search. Appl Intell 49(3):1146–1160. https://doi.org/10.1007/s10489-018-1312-1

  6. 6.

    Bhandarkar SM, Zhang H (1999) Image segmentation using evolutionary computation. IEEE Trans Evol Comput 3(1):1–21. https://doi.org/10.1109/4235.752917

  7. 7.

    Das S, Konar A (2009) Automatic image pixel clustering with an improved differential evolution. Appl Soft Comput 9(1):226–236. https://doi.org/10.1016/J.ASOC.2007.12.008

  8. 8.

    De Falco I, Della Cioppa A, Tarantino E (2007) Facing classification problems with particle swarm optimization. Appl Soft Comput 7(3):652–658. https://doi.org/10.1016/J.ASOC.2005.09.004

  9. 9.

    Dhal KGG, Sen M, Das S (2018) Multi-Thresholding of Histopathological images using fuzzy entropy and Parameterless cuckoo search (pp. 339–356). 10.4018/978-1-5225-5134-8.ch013

  10. 10.

    Dorini LB, Minetto R, Leite NJ (2013) Semiautomatic white blood cell segmentation based on multiscale analysis. IEEE Journal of Biomedical and Health Informatics 17(1):250–256. https://doi.org/10.1109/TITB.2012.2207398

  11. 11.

    Duan J, Yu L (2011). A WBC segmentation methord based on HSI color space. In 2011 4th IEEE International Conference on Broadband Network and Multimedia Technology (pp. 629–632). IEEE. 10.1109/ICBNMT.2011.6156011

  12. 12.

    García S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms. J Heuristics 15:617–644

  13. 13.

    Ghane N, Vard A, Talebi A, Nematollahy P (2017) Segmentation of white blood cells from microscopic images using a novel combination of K-means clustering and modified watershed algorithm. Journal of Medical Signals and Sensors, 7(2), 92–101. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/28553582

  14. 14.

    Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B (2009) Histopathological image analysis: a review. IEEE Rev Biomed Eng 2:147–171. https://doi.org/10.1109/RBME.2009.2034865

  15. 15.

    He K, Wang R, Tao D, Cheng J, Liu W (2018) Color transfer pulse-coupled neural networks for underwater robotic visual systems. IEEE Access 6:32850–32860. https://doi.org/10.1109/ACCESS.2018.2845855

  16. 16.

    Irshad H, Veillard A, Roux L, Racoceanu D (2014) Methods for nuclei detection, segmentation, and classification in digital histopathology: a review—current status and future potential. IEEE Rev Biomed Eng 7:97–114. https://doi.org/10.1109/RBME.2013.2295804

  17. 17.

    Kapoor S, Zeya I, Singhal C, Nanda SJ (2017) A Grey wolf optimizer based automatic clustering algorithm for satellite image segmentation. Procedia Computer Science 115:415–422. https://doi.org/10.1016/J.PROCS.2017.09.100

  18. 18.

    Karaboga, D. (2005) An idea based on honey bee swarm for numerical optimization. Computer Engineering Department, Engineering Faculty, Erciyes University

  19. 19.

    Kennedy J, Eberhart RC (1995) Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks 4:1942–1948

  20. 20.

    Khalilpourazari S, Khalilpourazary S (2018) A robust stochastic fractal search approach for optimization of the surface grinding process. Swarm and Evolutionary Computation 38:173–186. https://doi.org/10.1016/J.SWEVO.2017.07.008

  21. 21.

    Ko BC, Gim J-W, Nam J-Y (2011) Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake. Micron 42(7):695–705. https://doi.org/10.1016/j.micron.2011.03.009

  22. 22.

    Kovesi P (1995) Image Features From Phase Congruency. Retrieved from https://pdfs.semanticscholar.org/7b21/0794d603bcfb54ad7baf303301cfa8950747.pdf

  23. 23.

    Labati RD, Piuri V, Scotti F (2011) All-IDB: the acute lymphoblastic leukemia image database for image processing. In 2011 18th IEEE International Conference on Image Processing (pp. 2045–2048). IEEE. 10.1109/ICIP.2011.6115881

  24. 24.

    Li H, He X, Tao D, Tang Y, Wang R (2018) Joint medical image fusion, denoising and enhancement via discriminative low-rank sparse dictionaries learning. Pattern Recogn 79:130–146. https://doi.org/10.1016/J.PATCOG.2018.02.005

  25. 25.

    Li H, Zhang S, Zhang C, Li P, Cropp R (2017) A novel unsupervised levy flight particle swarm optimization (ULPSO) method for multispectral remote-sensing image classification. Int J Remote Sens 38(23):6970–6992. https://doi.org/10.1080/01431161.2017.1368102

  26. 26.

    Li Y, Zhu R, Mi L, Cao Y, Yao D (2016) Segmentation of white blood cell from acute lymphoblastic leukemia images using dual-threshold method. Computational and Mathematical Methods in Medicine 2016:1–12. https://doi.org/10.1155/2016/9514707

  27. 27.

    Lin Z, Zhang L, Mou X, Zhang D (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386. https://doi.org/10.1109/TIP.2011.2109730

  28. 28.

    Ma L, Li Y, Fan S, Fan R (2015) A hybrid method for image segmentation based on artificial fish swarm algorithm and fuzzy c -means clustering. Computational and Mathematical Methods in Medicine 2015:1–10. https://doi.org/10.1155/2015/120495

  29. 29.

    Manda, K., Satapathy, S. C., & Rajasekhara Rao, K. (2012). Artificial bee Colony based image clustering (pp. 29–37). Springer, Berlin, Heidelberg. 10.1007/978-3-642-27443-5_4

  30. 30.

    Mishra S, Majhi B, Sa PK, Sharma L (2017) Gray level co-occurrence matrix and random forest based acute lymphoblastic leukemia detection. Biomedical Signal Processing and Control 33:272–280. https://doi.org/10.1016/J.BSPC.2016.11.021

  31. 31.

    Mohapatra S, Patra D, Satpathi S (2010) Image analysis of blood microscopic images for acute leukemia detection. In 2010 International Conference on Industrial Electronics, Control and Robotics (pp. 215–219). IEEE. 10.1109/IECR.2010.5720171

  32. 32.

    MoradiAmin M, Memari A, Samadzadehaghdam N, Kermani S, Talebi A (2016) Computer aided detection and classification of acute lymphoblastic leukemia cell subtypes based on microscopic image analysis. Microsc Res Tech 79(10):908–916. https://doi.org/10.1002/jemt.22718

  33. 33.

    MoradiAmin M, Nasser S, Kermani S, Talebi A (2015) Enhanced recognition of acute lymphoblastic leukemia cells in microscopic images based on feature reduction using principle component analysis. Frontiers in Biomedical Technologies, 2(3), 128–136. Retrieved from https://www.semanticscholar.org/paper/Enhanced-Recognition-of-Acute-Lymphoblastic-Cells-MoradiAmin-Samadzadehaghdam/53404421b6f4660a5f73e238c16fc903b596e190

  34. 34.

    Omran MGH, Engelbrecht AP (2006) Self-adaptive differential evolution methods for unsupervised image classification. In 2006 IEEE Conference on Cybernetics and Intelligent Systems (Vol. 2, pp. 966–973). IEEE. 10.1109/ICCIS.2006.252239

  35. 35.

    Patel N, Mishra A (2015) Automated Leukaemia detection using microscopic images. Procedia Computer Science 58:635–642. https://doi.org/10.1016/J.PROCS.2015.08.082

  36. 36.

    Piuri, V, Scotti F (2004) Morphological classification of blood leucocytes by microscope images. In IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (pp. 103–108). IEEE. 10.1109/CIMSA.2004.1397242

  37. 37.

    Qin P, Chen J, Zeng J, Chai R, Wang L (2018) Large-scale tissue histopathology image segmentation based on feature pyramid. EURASIP Journal on Image and Video Processing 2018(1):75–79. https://doi.org/10.1186/s13640-018-0320-8

  38. 38.

    Rawat J, Singh A, Bhadauria HS, Virmani J, Devgun JS (2017) Classification of acute lymphoblastic leukaemia using hybrid hierarchical classifiers. Multimed Tools Appl 76(18):19057–19085. https://doi.org/10.1007/s11042-017-4478-3

  39. 39.

    Romero H (1972) La diabétes en el panorama de la salubridad Chilena. Rev Med Chil 100(4):464–467. https://doi.org/10.1142/S0218001405004083

  40. 40.

    Salimi H (2015) Stochastic fractal search: a powerful metaheuristic algorithm. Knowl-Based Syst 75:1–18. https://doi.org/10.1016/J.KNOSYS.2014.07.025

  41. 41.

    Storn R, Price K (1997) Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim

  42. 42.

    Suresh S, Lal S (2017) Multilevel thresholding based on chaotic Darwinian particle swarm optimization for segmentation of satellite images. Appl Soft Comput 55:503–522. https://doi.org/10.1016/J.ASOC.2017.02.005

  43. 43.

    Wu J, Zeng P, Zhou Y, Olivier C (2006) A novel color image segmentation method and its application to white blood cell image analysis. In 2006 8th international conference on signal processing. IEEE. 10.1109/ICOSP.2006.345700

  44. 44.

    Ye A-X, Jin Y-X (2016) A Fuzzy C-Means Clustering Algorithm Based on Improved Quantum Genetic Algorithm. Retrieved from https://www.semanticscholar.org/paper/A-Fuzzy-C-Means-Clustering-Algorithm-Based-on-Ye-Jin/8cbdcb6b1eb9e4ab5098c5544e7296bee9591831

  45. 45.

    Zhang L, Gao Y, Xia Y, Lu K, Shen J, Ji R (2014) Representative discovery of structure cues for weakly-supervised image segmentation. IEEE Transactions on Multimedia 16(2):470–479. https://doi.org/10.1109/TMM.2013.2293424

  46. 46.

    Zhou, C., Sun, C., Wang, B., & Wang, X. (2018). An improved stochastic fractal search algorithm for 3D protein structure prediction. J Mol Model, 24(6), 125. 10.1007/s00894-018-3644-5

Download references

Author information

Correspondence to Jorge Gálvez.

Ethics declarations

Conflict of interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dhal, K.G., Gálvez, J., Ray, S. et al. Acute lymphoblastic leukemia image segmentation driven by stochastic fractal search. Multimed Tools Appl (2020). https://doi.org/10.1007/s11042-019-08417-z

Download citation

Keywords

  • Pathology image segmentation
  • Clustering, stochastic fractal search
  • Swarm intelligence optimization