Skip to main content

Acute Lymphoblastic Leukemia Cell Detection in Microscopic Digital Images Based on Shape and Texture Features

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11656)

Abstract

Leukemia or blood cancer is a disease that affects a large population, especially children. Fast and early detection of four main types of leukemia is crucial for successful treatment and patient’s recovery. Leukemia can be detected in microscope blood images by detecting blasts, i.e. not fully developed white blood cells. Computer-aided diagnostic systems can improve the quality and speed of abnormal lymphocytes detection. In this paper we proposed a method for automatic detection of one type of leukemia, acute lymphoblastic leukemia, by classifying white blood cells into normal cells and blasts. The proposed method uses shape and texture features as input vector for support vector machine optimized by bare bones fireworks algorithm. Based on the results obtained on the standard benchmark set, ALL-IDB, our proposed method shows a competitive accuracy of classification comparing to other state-of-the-art method.

Keywords

  • Acute lymphoblastic leukemia detection
  • Segmentation
  • Local binary pattern
  • Support vector machine
  • Optimization
  • Swarm intelligence
  • Bare bone fireworks algorithm

This research is supported by Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-26354-6_14
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   69.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-26354-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   89.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.

References

  1. Stojak, A., Tuba, E., Tuba, M.: Framework for abnormality detection in magnetic resonance brain images. In: 24th Telecommunications Forum (TELFOR), pp. 1–4. IEEE (2016)

    Google Scholar 

  2. Tuba, E., Mrkela, L., Tuba, M.: Retinal blood vessel segmentation by support vector machine classification. In: 27th International Conference Radioelektronika (RADIOELEKTRONIKA), pp. 1–6. IEEE (2017)

    Google Scholar 

  3. Tuba, E., Tuba, M., Dolicanin, E.: Adjusted fireworks algorithm applied to retinal image registration. Stud. Inform. Control 26(1), 33–42 (2017)

    CrossRef  Google Scholar 

  4. Tuba, E., Jovanovic, R., Beko, M., Tallón-Ballesteros, A.J., Tuba, M.: Bare bones fireworks algorithm for medical image compression. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A.J. (eds.) IDEAL 2018, Part II. LNCS, vol. 11315, pp. 262–270. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03496-2_29

    CrossRef  Google Scholar 

  5. Pirnstill, C.W., Coté, G.L.: Malaria diagnosis using a mobile phone polarized microscope. Sci. Rep. 5, 13368 (2015)

    CrossRef  Google Scholar 

  6. Tao, Z., et al.: Early tumor detection afforded by in vivo imaging of near-infrared II fluorescence. Biomaterials 134, 202–215 (2017)

    CrossRef  Google Scholar 

  7. McCann, M.T., Ozolek, J.A., Castro, C.A., Parvin, B., Kovacevic, J.: Automated histology analysis: opportunities for signal processing. IEEE Signal Process. Mag. 32(1), 78–87 (2015)

    CrossRef  Google Scholar 

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

    CrossRef  Google Scholar 

  9. Bhattacharjee, R., Saini, L.M.: Detection of acute lymphoblastic leukemia using watershed transformation technique. In: International Conference on Signal Processing, Computing and Control, pp. 383–386. IEEE (2015)

    Google Scholar 

  10. Shankar, V., Deshpande, M.M., Chaitra, N., Aditi, S.: Automatic detection of acute lymphoblasitc leukemia using image processing. In: International Conference on Advances in Computer Applications, pp. 186–189. IEEE (2016)

    Google Scholar 

  11. Amin, M.M., Kermani, S., Talebi, A., Oghli, M.G.: Recognition of acute lymphoblastic leukemia cells in microscopic images using k-means clustering and support vector machine classifier. J. Med. Signals Sens. 5(1), 49 (2015)

    Google Scholar 

  12. Kumar, A., Shaik, F., Abdul Rahim, B., Sravan Kumar, D.: Image enhancement of leukemia microscopic images. In: Signal and Image Processing in Medical Applications. SAST, pp. 17–37. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-0690-6_4

    CrossRef  Google Scholar 

  13. Mohapatra, S., Patra, D., Satpathy, S.: An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images. Neural Comput. Appl. 24(7–8), 1887–1904 (2014)

    CrossRef  Google Scholar 

  14. Putzu, L., Caocci, G., Di Ruberto, C.: Leucocyte classification for leukaemia detection using image processing techniques. Artif. Intell. Med. 62(3), 179–191 (2014)

    CrossRef  Google Scholar 

  15. Joshi, M.D., Karode, A.H., Suralkar, S.: White blood cells segmentation and classification to detect acute leukemia. Int. J. Emerg. Trends Technol. Comput. Sci. 2(3), 147–151 (2013)

    Google Scholar 

  16. Rawat, J., Singh, A., Bhadauria, H., Virmani, J.: Computer aided diagnostic system for detection of leukemia using microscopic images. Procedia Comput. Sci. 70, 748–756 (2015)

    CrossRef  Google Scholar 

  17. Patel, N., Mishra, A.: Automated leukaemia detection using microscopic images. Procedia Comput. Sci. 58, 635–642 (2015)

    CrossRef  Google Scholar 

  18. Viswanathan, P.: Fuzzy C means detection of leukemia based on morphological contour segmentation. Procedia Comput. Sci. 58, 84–90 (2015)

    CrossRef  Google Scholar 

  19. Mishra, S., Majhi, B., Sa, P.K., Sharma, L.: Gray level co-occurrence matrix and random forest based acute lymphoblastic leukemia detection. Biomed. Signal Process. Control 33, 272–280 (2017)

    CrossRef  Google Scholar 

  20. Abdeldaim, A.M., Sahlol, A.T., Elhoseny, M., Hassanien, A.E.: Computer-aided acute lymphoblastic leukemia diagnosis system based on image analysis. In: Hassanien, A.E., Oliva, D.A. (eds.) Advances in Soft Computing and Machine Learning in Image Processing. SCI, vol. 730, pp. 131–147. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-63754-9_7

    CrossRef  Google Scholar 

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

    Google Scholar 

  22. Li, J., Tan, Y.: The bare bones fireworks algorithm: a minimalist global optimizer. Appl. Soft Comput. 62, 454–462 (2018)

    CrossRef  Google Scholar 

  23. Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010, Part I. LNCS, vol. 6145, pp. 355–364. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13495-1_44

    CrossRef  Google Scholar 

  24. Tuba, E., Tuba, M., Simian, D., Jovanovic, R.: JPEG quantization table optimization by guided fireworks algorithm. In: Brimkov, V.E., Barneva, R.P. (eds.) IWCIA 2017. LNCS, vol. 10256, pp. 294–307. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59108-7_23

    CrossRef  Google Scholar 

  25. Tuba, E., Dolicanin, E., Tuba, M.: Guided fireworks algorithm applied to the maximal covering location problem. In: Tan, Y., Takagi, H., Shi, Y. (eds.) ICSI 2017, Part I. LNCS, vol. 10385, pp. 501–508. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61824-1_55

    CrossRef  Google Scholar 

  26. Tuba, E., Tuba, M., Simian, D.: Wireless sensor network coverage problem using modified fireworks algorithm. In: 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 696–701. IEEE (2016)

    Google Scholar 

  27. Tuba, E., Jovanovic, R., Hrosik, R.C., Alihodzic, A., Tuba, M.: Web intelligence data clustering by bare bone fireworks algorithm combined with k-means. In: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics, p. 7. ACM (2018)

    Google Scholar 

  28. Zheng, S., Janecek, A., Tan, Y.: Enhanced fireworks algorithm. In: IEEE Congress on Evolutionary Computation, pp. 2069–2077. IEEE, June 2013

    Google Scholar 

  29. Tuba, E., Ribic, I., Capor-Hrosik, R., Tuba, M.: Support vector machine optimized by elephant herding algorithm for erythemato-squamous diseases detection. Procedia Comput. Sci. 122, 916–923 (2017)

    CrossRef  Google Scholar 

  30. Mishra, S., Sharma, L., Majhi, B., Sa, P.K.: Microscopic image classification using DCT for the detection of Acute Lymphoblastic Leukemia (ALL). In: Raman, B., Kumar, S., Roy, P.P., Sen, D. (eds.) Proceedings of International Conference on Computer Vision and Image Processing. AISC, vol. 459, pp. 171–180. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-2104-6_16

    CrossRef  Google Scholar 

  31. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Milan Tuba .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Tuba, E., Strumberger, I., Bacanin, N., Zivkovic, D., Tuba, M. (2019). Acute Lymphoblastic Leukemia Cell Detection in Microscopic Digital Images Based on Shape and Texture Features. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11656. Springer, Cham. https://doi.org/10.1007/978-3-030-26354-6_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-26354-6_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26353-9

  • Online ISBN: 978-3-030-26354-6

  • eBook Packages: Computer ScienceComputer Science (R0)