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Leukocyte segmentation in peripheral blood images using a novel edge strength cue-based location detection method

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Abstract

The classification of leukocytes in peripheral blood images is an important milestone to be achieved because it can greatly assist pathologists to diagnose diseases such as leukemia, anemia, and other blood disorders. To a certain extent, a good segmentation method for identifying leukocytes from their background is the first step to the efficient functioning of the leukocytes classification system. However, the morphological structure of leukocytes, poor contrast, and the variations in their shape and size lead to the degradation of the segmentation accuracy. In this paper, we propose a new leukocyte segmentation framework that first locates and then segments leukocytes from peripheral blood images. Here, the locations of the leukocytes are first identified using a novel edge strength cue (ESc), and later, the Grabcut model is deployed to obtain the segmentation of the leukocytes. The novelty lies in the way the location of the leukocytes is detected, and this improves the leukocyte segmentation accuracy. The experimental evaluation is performed on ALL-IDB1, Cellavision, and LISC datasets for leukocyte segmentation based on the detection of the ESc location. Experimental results are evaluated using precision, recall, and F-score measures. The proposed method outperforms the state-of-the-art techniques. Additionally, the computation time of the proposed method is analyzed and presented in the study.

Leukocytes Location Detection and Segmentation

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Notes

  1. The n th percentile is the smallest number that is greater than n% of the numbers in a given set.

References

  1. Alexe B, Deselaers T, Ferrari V (2012) Measuring the objectness of image windows. IEEE Trans Pattern Anal Mach Intell 34(11):2189–2202

    Article  Google Scholar 

  2. Alférez S, Merino A, Acevedo A, Puigví L, Rodellar J (2019) Color clustering segmentation framework for image analysis of malignant lymphoid cells in peripheral blood. Med Biol Eng Comput, 1–19

  3. Biswas S, Ghoshal D (2016) Blood cell detection using thresholding estimation based watershed transformation with sobel filter in frequency domain. Procedia Comput Sci 89:651–657

    Article  Google Scholar 

  4. Blake A, Rother C, Brown M, Perez P, Torr P (2004) Interactive image segmentation using an adaptive gmmrf model. In: European conference on computer vision. Springer, Berlin, pp 428–441

  5. Boykov YY, Jolly MP (2001) Interactive graph cuts for optimal boundary & region segmentation of objects in nd images. In: Computer vision, 2001. ICCV 2001. Proceedings. Eighth IEEE international conference on, IEEE, vol 1, pp. 105–112

  6. Cao F, Liu Y, Huang Z, Chu J, Zhao J (2018) Effective segmentations in white blood cell images using 𝜖-svr-based detection method. Neural Comput Appl, 1–14

  7. Cao H, Liu H, Song E (2018) Bone marrow cells detection: a technique for the microscopic image analysis. arXiv:180502058

  8. Chaira T (2014) Accurate segmentation of leukocyte in blood cell images using atanassov’s intuitionistic fuzzy and interval type ii fuzzy set theory. Micron 61:1–8

    Article  Google Scholar 

  9. Duan Y, Wang J, Hu M, Zhou M, Li Q, Sun L, Qiu S, Wang Y (2019) Leukocyte classification based on spatial and spectral features of microscopic hyperspectral images. Optics Laser Technol 112:530–538

    Article  CAS  Google Scholar 

  10. Ferdosi BJ, Nowshin S, Sabera FA (2018) White blood cell detection and segmentation from fluorescent images with an improved algorithm using k-means clustering and morphological operators. In: 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT). IEEE, Piscataway, pp 566–570

  11. 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. J Medical Signals Sens 7(2):92

    Article  Google Scholar 

  12. Gowda JP, Kumar SP (2017) Segmentation of white blood cell using k-means and gram-schmidt orthogonalization. Indian J Sci Technol, 10(6)

  13. Hegde RB, Prasad K, Hebbar H, Singh BMK (2019) Development of a robust algorithm for detection of nuclei of white blood cells in peripheral blood smear images. Multimed Tools Appl, 1–20

  14. Hegde RB, Prasad K, Hebbar H, Singh BMK (2019) Image processing approach for detection of leukocytes in peripheral blood smears. J Med Systems 43(5):114

    Article  Google Scholar 

  15. Ko BC, Gim JW, Nam JY (2011) Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake. Micron 42(7):695–705

    Article  Google Scholar 

  16. Liu Y, Cao F, Zhao J, Chu J (2017) Segmentation of white blood cells image using adaptive location and iteration. IEEE J Biomed Health 21(6):1644–1655. http://www.cellavision.com.Accessed:2018

    Article  Google Scholar 

  17. Liu Z, Liu J, Xiao X, Yuan H, Li X, Chang J, Zheng C (2015) Segmentation of white blood cells through nucleus mark watershed operations and mean shift clustering. Sensors 15(9):22561–22586

    Article  CAS  Google Scholar 

  18. Mishra S, Majhi B, Sa P K (2019) Texture feature based classification on microscopic blood smear for acute lymphoblastic leukemia detection. Biomed Signal Proces 47:303–311

    Article  Google Scholar 

  19. Moshavash Z, Danyali H, Helfroush M S (2018) An automatic and robust decision support system for accurate acute leukemia diagnosis from blood microscopic images. J Digit Imaging, 1–16

  20. Negm AS, Hassan OA, Kandil AH (2017), A decision support system for acute leukaemia classification based on digital microscopic images. Alex Eng J

  21. Orchard MT, Bouman CA (1991) Color quantization of images. IEEE T Signal Proces 39 (12):2677–2690

    Article  Google Scholar 

  22. Rawat J, Singh A, Bhadauria H, Virmani J, Devgun J (2018) Leukocyte classification using adaptive neuro-fuzzy inference system in microscopic blood images. Arab J Sci Eng 43(12):7041–7058

    Article  Google Scholar 

  23. Rezatofighi SH, Soltanian-Zadeh H (2011) Automatic recognition of five types of white blood cells in peripheral blood. Comput Med Imaging Graph 35(4):333–343

    Article  Google Scholar 

  24. Sadeghian F, Seman Z, Ramli AR, Kahar BHA, Saripan MI (2009) A framework for white blood cell segmentation in microscopic blood images using digital image processing. Bio Proced Online 11(1):196

    Article  CAS  Google Scholar 

  25. Safuan SNM, Tomari MRM, Zakaria WNW (2018) White blood cell (wbc) counting analysis in blood smear images using various color segmentation methods. Measurement 116:543–555. https://homes.di.unimi.it/scotti/all/

    Article  Google Scholar 

  26. Sajjad M, Khan S, Jan Z, Muhammad K, Moon H, Kwak J T, Rho S, Baik S W, Mehmood I (2017) Leukocytes classification and segmentation in microscopic blood smear: a resource-aware healthcare service in smart cities. IEEE Access 5:3475–3489

    Article  Google Scholar 

  27. Supriyanti R, Satrio G, Ramadhani Y, Siswandari W (2017) Contour detection of leukocyte cell nucleus using morphological image, vol 824, IOP Publishing, Bristol

  28. Taha AA, Hanbury A (2015) Metrics for evaluating 3d medical image segmentation : analysis, selection, and tool. BMC Med Imaging 15(1):29

    Article  Google Scholar 

  29. Talbot JF, Xu X (2006) Implementing grabcut. Brigham Young University 3

  30. Tareef A, Song Y, Cai W, Wang Y, Feng DD, Chen M (2016) Automatic nuclei and cytoplasm segmentation of leukocytes with color and texture-based image enhancement. In: Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, IEEE, pp 935–938

  31. Wang Q, Chang L, Zhou M, Li Q, Liu H, Guo F (2016) A spectral and morphologic method for white blood cell classification. Opt Laser Technol 84:144–148

    Article  CAS  Google Scholar 

  32. Wu J, Zeng P, Zhou Y, Olivier C (2006) A novel color image segmentation method and its application to white blood cell image analysis

  33. Zheng X, Wang Y, Wang G, Liu J (2018) Fast and robust segmentation of white blood cell images by self-supervised learning. Micron 107:55–71

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by Anna University Chennai granting Anna Centenary Research Fellowship (ACRF) CFR/ACRF/2017/16.

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  1. Department of Computer Science and Engineering, College of Engineering, Guindy, Anna University, Chennai, India

    K. Sudha & P. Geetha

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  1. K. Sudha
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  2. P. Geetha
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Correspondence to K. Sudha.

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Sudha, K., Geetha, P. Leukocyte segmentation in peripheral blood images using a novel edge strength cue-based location detection method. Med Biol Eng Comput 58, 1995–2008 (2020). https://doi.org/10.1007/s11517-020-02204-x

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