Detection of acute lymphoblastic leukemia using image segmentation and data mining algorithms

  • Vasundhara AcharyaEmail author
  • Preetham Kumar
Original Article


Blood is composed of white blood cells, red blood cells, and platelets. Segmentation of the blood smear cells and extraction of features of the cells is essential in the field of medicine. Acute lymphoblastic leukemia is a form of blood cancer caused due to the abnormal increase in the production of immature white blood cells in the bone marrow. It mostly affects the children below 5 years and adults above 50 years of age. Due to the late diagnosis and cost of the devices used for the determination, the mortality rate has increased drastically. Flow cytometry technique that performs automated counting fails to identify the abnormal cells. Manual recount performed using hemocytometer are prone to errors and are imprecise. The proposed work aims to survey different computer-aided system techniques used to segment the blood smear image. The primary objective here is to derive knowledge from the different methodologies used for extracting features from white blood cells and develop a system that would accurately segment the blood smear image by overcoming the drawbacks of the previous works. The objective mentioned above is achieved in two ways. Firstly, a novel algorithm is developed to segment the nucleus and cytoplasm of white blood cell. Secondly, a model is built to extract the features and train the model. The different supervised classifiers are compared, and the one with the highest accuracy is used for the classification. Six hundred images are used in the experimentation. InfoGainAttributeEval and the Ranker Search method are used to achieve the feature selection which in turn helps in improvising the classifier performance. The result shows the classification of the acute lymphoblastic leukemia into its three respective categories namely: ALL-L1, ALL-L2, ALL-L3. The model can differentiate between a normal peripheral blood smear and an abnormal blood smear. The extracted feature values of a cancerous cell and a normal cell are also shown. The performance of the model is evaluated using the test images stained with various stains. The proposed algorithm achieved an overall accuracy of 98.6%. The promising results show that it can be used as a diagnostic tool by the pathologists.

Graphical abstract


Acute lymphoblastic leukemia Blood smear cells Flow cytometry Hemocytometer White blood cell 



  1. 1.
    Karen Seiter (2016) ALL types. In:Acute Lymphoblastic Leukemia Staging. Available via Accessed 16th Nov,2017Google Scholar
  2. 2.
    Afshar S, Abdolrahmani F, Tanha FV, Seif MZ, Taheri K (2011) Recognition and prediction of leukemia with artificial neural network (ANN). Med J Islam Repub Iran 25(1):35–39Google Scholar
  3. 3.
    Alomari YM, Azma RZ, Abdullah SNHS, Omar K (2014) Automatic detection and quantification of WBCs and RBCs using iterative structured circle detection algorithm. Computational and Mathematical Methods in Medicine 2014:1–14. CrossRefGoogle Scholar
  4. 4.
    Belekar SJ, Chougule SR (2015) WBC segmentation using morphological operation and SMMT operator-a review. International Journal of Innovative Research in Computer and Communication Engineering 3(1):434–440Google Scholar
  5. 5.
    Bhagvathi SL, Thomas NS (2016) An automatic system for detecting and counting RBC and WBC using fuzzy logic. ARPN-JEAS 11(11):6891–6894Google Scholar
  6. 6.
    Bhamare, Miss. Madhuri G. and D.S.Patil (2013) Automatic blood cell analysis by using digital image processing: a preliminary study .Int J Eng Res Technol 2(9):3137–3141.Google Scholar
  7. 7.
    Erosion operation (2017) Accessed 16th November, 2017
  8. 8.
    Joshi MD, Karode AH, Suralkar SR (2013) White blood cells segmentation and classification to detect acute leukemia. International journal of emerging trends Technology in Computer. Science 2(3):147–151Google Scholar
  9. 9.
    Marzuki NIC, Mahmood NH, Razak MAA (2015) Segmentation of white blood cell nucleus using active contour. J Teknol 74(6):115–118. Google Scholar
  10. 10.
    Mishra, S. J., & Deshmukh,A.P. (2014) Detection of Leukemia in Human Blood Sample based on Microscopic Images. International Journal of Advanced Research in Electronics and Communication Engineering, 1(3) :10-14. Google Scholar
  11. 11.
    Mulik V, Bhilare PM, Alhat S (2016) Analysis of acute lymphoblastic leukemia cells using digital image processing. International Journal for Scientific Research and Development 4(2):70–72Google Scholar
  12. 12.
    Nasir A, Mustafa N, Nasir NFM (2009) Application of thresholding technique in determining ratio of blood cells for leukemia detection. In Proceedings of the international conference on man-machine systems (ICoMMS 2009), 1–6Google Scholar
  13. 13.
    Putzu L, Ruberto CD (2013) White blood cells identification and counting from microscopic blood image. International journal of medical, health, biomedical, bioengineeringand Pharm Eng 7(1):20–27Google Scholar
  14. 14.
    Sadeghian F, Ramli AR, Seman Z, Khahar BHA, Saripan MI (2009) A Framework for White Blood Cell Segmentation in Microscopic Blood Images Using Digital Image Processing. Biological Procedures Online 11(1):196–206. CrossRefGoogle Scholar
  15. 15.
    Thanh TTP, Pham GN, Park JH , Moon KS, Lee SH, Kwon KR (2017) Acute leukemia classification using convolution neural network in clinical decision support system. In Proc Of Computer Science & Information Technology, 49–53.
  16. 16.
    Vaghela HP, Pandya M, Modi H, Potdar MB (2015) Leukemia detection using digital image processing techniques. International Journal of Applied Information Systems 10(1):43–51CrossRefGoogle Scholar
  17. 17.
    Wang M, Zhou X, Li F, Huckins J, King RW, Wong STC Novel cell segmentation and online learning algorithms for cell phase identification in automated time-lapse microscopy. In 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro 65–68.
  18. 18.
    Wang Q, Wang J, Zhou M, Li Q, Wang Y (2017) Spectral-spatial feature-based neural network method for acute lymphoblastic leukemia cell identification via microscopic hyperspectral imaging technology. Biomedical Optics Express 8(6):3019–3028Google Scholar
  19. 19.
    Saeedizadeh Z, Dehnavi A, A.M., Rabbani H (2015) Automatic recognition of myeloma cells in microscopic images using bottleneck algorithm, modified watershed and SVM classifier. J Microsc 261(1):46–56. CrossRefGoogle Scholar
  20. 20.
    Jiang K, Jiang QX, Xiong Y (2003) A novel white blood cell segmentation scheme using scale-space filtering and watershed clustering. Mach Learning Cybernetics 5:2820–2825Google Scholar
  21. 21.
    Kumar BR, Joseph DK, Sreenivas TV( 2002) Teager energy based blood cell segmentation. In proceedings of the 14th international conference on digital signal processing, Santorini, Greece, 619–622Google Scholar
  22. 22.
    Alreza ZKK, Karimian A (2016) A. Design a new algorithm to count white blood cells for classification leukemic blood image using machine vision system. In Proceedings of the 6th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran, 251–256Google Scholar
  23. 23.
    Reta C, Gonzalez JA, Diaz R, Guichard JS (2011) Leukocytes segmentation using Markov random fields. Adv Exp Med Biol 696:345–353CrossRefGoogle Scholar
  24. 24.
    Madhloom HT, Kareem SA, Ariffin H (2011) An image processing application for the localization and segmentation of lymphoblast cell using peripheral blood images. J Med Syst 36:2149–2158CrossRefGoogle Scholar
  25. 25.
    Houby EMFE (2018) Framework of computer aided diagnosis Systems for Cancer Classification Based on medical images. J Med Syst 42:157–167CrossRefGoogle Scholar
  26. 26.
    Theera-Umpon N (2005) Patch-based white blood cell nucleus segmentation using fuzzy clustering. ECTI Transactions on Electrical Eng., Electronics, and Communications 3(1):15–19Google Scholar
  27. 27.
    Mohammed EA, Mohamed MMA, Naugler Christopher, Far BH (2013) Chronic lymphocytic leukemia cell segmentation from microscopic blood images using watershed algorithm and optimal thresholding. In Proc. of the 26th IEEE Can Con El Comp En(CCECE):1–5Google Scholar
  28. 28.
    Esti Suryani and Wiharto Wiharto and Nizomjon Polvonov (2015) Identification and Counting White Blood Cells and Red Blood Cells using Image Processing Case Study of Leukemia. International Journal of Computer Science & Network Solutions 2(6):35–49.Google Scholar
  29. 29.
     Khobragade S, Mor DD, Patil CY (2015) Detection of leukemia in microscopic white blood cell images. In Proc. of the 2015 international conference on information processing (ICIP) (ICIP):435–440.
  30. 30.
    Cuevas E, Díaz M, Manzanares M, Zaldivar D, Perez-Cisnero M (2013) An improved computer vision method for white blood cells detection. Computational and Mathematical Methods in Medicine:1–14Google Scholar
  31. 31.
    Ahasan R, Ratul AU, Bakibillah ASM White Blood Cells Nucleus Segmentation from Microscopic Images of strained peripheral blood film during Leukemia and Normal Condition. In Proc. of the 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV): 361–366.
  32. 32.
    Guo N, Zeng L, Wu Q (2007) A method based on multispectral imaging technique for white blood cell segmentation. Comput Biol Med 37:70–76CrossRefGoogle Scholar
  33. 33.
    Sinha N, Ramakrishnan AG (2002) Blood cell segmentation using EM algorithm. In proc. Third Indian conference on computer vision. Graphics & Image Processing (ICVGIP):1–6.
  34. 34.
    Wang M, Chu R (2009) A novel white blood cell detection method based on boundary support vectors. In proc. of the 2009 IEEE international conference on systems. Man and Cybernetics:2595–2598Google Scholar
  35. 35.
    Nivaldo Medeiros(n.d.) Blood Smear Database. In: Atlas.Available via Accessed 16th November, 2017. Accessed 16 Nov 2017
  36. 36.
    Labati RD, Piuri V, Scotti F (2011) ALL -IDB: the acute lymphoblastic leukemia image database for image processing. In Proc. of the 2011 IEEE international conference on image processing 2045–2048.
  37. 37.
    C.I.E primaries (n.d.) Accessed 16th November, 2017
  38. 38.
    Area open function (2015) Accessed 16th November, 2017
  39. 39.
    Border clear (2015) Accessed 16th November, 2017
  40. 40.
  41. 41.
    Gonzalez, R. C., R. E. Woods, and S. L. Eddins (2004) Digital Image Processing Using MATLAB. New Jersey, Pearson Prentice HallGoogle Scholar
  42. 42.
    Measurement properties (2017) Accessed: 16th November, 2017
  43. 43.
    Label the components (2015) Accessed: 16th November, 2017
  44. 44.
    Filling holes (2015) Accessed16th November, 2017
  45. 45.
    Extended maxima (2012) Accessed 16th November, 2017
  46. 46.
    Impose minima (2003) Accessed: 16th November, 2017
  47. 47.
    Subrajeet Mohapatra (2013) Hematological image analysis for acute lymphoblastic leukemia detection and classification. Doctoral Dissertation, National Institute of Technology RourkelaGoogle Scholar
  48. 48.
    Histogram equalization (2015). Histeq function. In: Histogram Equalization. Available via . Accessed: 16th Nov, 2017 Accessed: 16 Nov 2017
  49. 49.
  50. 50.
    Almuallim H, Dietterich TG (1991) Learning with many irrelevant features. In: Proc. Ninth National conference on Artificial intelligence (AAAI-91), Anaheim, CA 2: 547–552Google Scholar
  51. 51.
    Kira K, Rendell LA. (1992) The feature selection problem: traditional methods and a newalgorithm. In: Proc. AAAI-92, San Jose, CA 122–126Google Scholar
  52. 52.
    MyHematalogy(2017) Leishman Stain . In : MyHematology. Available via . Accessed: 16th Nov, 2017Google Scholar
  53. 53.
    Wright Stain Method Technical Data Sheet (2005) Wright Stain . In: Technical Data Sheets. Available via. Accessed 16 Nov 2017
  54. 54.
    ALL-IDB (2011) Accessed 16th January,2019
  55. 55.
    Attribute-Relation File Format (ARFF) (2008) ARFF. In: Attribute-Relation File Format (ARFF). Available via. Accessed 16 Nov 2017
  56. 56.
    Mohammed EA, Mohamed MM, Far BH, Naugler C (2014) Peripheral blood smear image analysis: a comprehensive review. Journal of Pathology Informatics 5(1):9. CrossRefGoogle Scholar
  57. 57.
    Adjust operation (2017) Accessed:16th November, 2017

Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Manipal Institute of Technology (MIT)Manipal Academy of Higher Education(MAHE)ManipalIndia
  2. 2.Department of I&CT, Manipal Institute of TechnologyManipal Academy of Higher EducationManipalIndia

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