International Journal of Advanced and Applied Sciences

Int. j. adv. appl. sci.

EISSN: 2313-3724

Print ISSN: 2313-626X

Volume 4, Issue 8  (August 2017), Pages:  74-78

Title: Detection of acute lymphoblastic leukemia using microscopic images of blood

Author(s):  Raju Bhukya *, B. Prasanth, V. Sasank Vihari, Y. Ajay


Department of Computer Science and Engineering, National Institute of Technology, Warangal, Telangana, India

Full Text - PDF          XML


Leukemia is a group of cancers that usually begin in the bone marrow and result in high number of abnormal white blood cells. Detection of leukaemia in early stages is necessary as this can reduce the rate of mortality and may lead to death. In a manual method of leukaemia detection Haematologists analyze the microscopic images and decide the severity. This is lengthy, cost effective and time taking process which depends on person’s expertise and may not lead to standard accuracy. Till date, a number of methods have been proposed for this Leukaemia detection using Image Processing. Unlike the previous methods, which solely depend upon the entire cell, in this paper we proposed a new method to separate the cell Nucleus from Cytoplasm to obtain more features. The proposed method achieves the better accuracy when compared to the other existing methods. 

© 2017 The Authors. Published by IASE.

This is an open access article under the CC BY-NC-ND license (

Keywords: Leukemia, Leukocyte, Microscopic image, Segmentation, Nucleus, Cytoplasm

Article History: Received 19 March 2017, Received in revised form 1 July 2017, Accepted 5 July 2017

Digital Object Identifier:


Bhukya R, Prasanth B, Vihari VS,  and Ajay Y (2017). Detection of acute lymphoblastic leukemia using microscopic images of blood. International Journal of Advanced and Applied Sciences, 4(8): 74-78


  1. Bezdek JC, Ehrlich R, and Full W (1984). FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences, 10(2-3): 191-203.
  2. Fatma M and Sharma J (2014). Leukemia image segmentation using K-means clustering and HSI color image segmentation. International Journal of Computer Applications, 94(12): 6-9.
  3. Khashman A and Abbas HH (2013). Acute lymphoblastic leukemia identification using blood smear images and a neural classifier. In the 12th International Conference on Artificial Neural Networks: Advances in Computational Intelligence (IWANN '13), Puerto de la Cruz, Tenerife, Spain, 2: 80–87.
  4. Kuo BC and Landgrebe DA (2004). Nonparametric weighted feature extraction for classification. IEEE Transactions on Geoscience and Remote Sensing, 42(5): 1096-1105.
  5. Li CH, Kuo BC, and Lin CT (2011). LDA-based clustering algorithm and its application to an unsupervised feature extraction. IEEE Transactions on Fuzzy Systems, 19(1): 152-163.
  6. Majno G and Joris I (2004). Cells, tissues, and disease: principles of general pathology. Oxford University Press, Oxford, UK.
  7. Mohapatra S and Patra D (2010). Automated cell nucleus segmentation and acute leukemia detection in blood microscopic images. In the International Conference on Systems in Medicine and Biology, IEEE, Kharagpur, India.
  8.  Mohapatra S, Patra D, and Satpathy S (2012a). Unsupervised blood microscopic image segmentation and leukemia detection using color based clustering. International Journal of Computer Information Systems and Industrial Management Applications, 4: 477-485.
  9.  Mohapatra S, Patra D, and Satpathy S (2014). An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images. Neural Computing and Applications, 24(7-8): 1887-1904.
  10. Mohapatra S, Patra D, Kumar S, and Satpathi S (2012b). Kernel induced rough c-means clustering for lymphocyte image segmentation. In the 4th International Conference on Intelligent Human Computer Interaction (IHCI), Kharagpur, India, IEEE: 1-6.
  11. Osuna E, Freund R, and Girosi F (1997). An improved training algorithm for support vector machines. In the IEEE Workshop Neural Networks for Signal Processing VII, IEEE, Florida, USA: 276-285.
  12. Patel N and Mishra A (2015). Automated Leukaemia Detection Using Microscopic Images. Procedia Computer Science, 58: 635-642.
  13. Piuri V and Scotti F (2004). Morphological classification of blood leucocytes by microscope images. In the International Conference on Computational Intelligence for Measurement Systems and Applications, IEEE, Boston, USA.
  14. Pui CH and Evans WE (1998). Acute lymphoblastic leukemia. New England Journal of Medicine, 339(9): 605-615.                PMid:9718381
  15. Shirvoikar M and Virani HG (2016). Detection and segmentation of WBC cells using image processing technique. International Journal of Technology and Science, 9(1): 56-58.
  16. Srisukkham W, Lepcha P, Hossain MA, Zhang L, Jiang R, and Lim HN (2013). A mobile enabled intelligent scheme to identify blood cancer for remote areas-cell membrane segmentation using marker controlled watershed segmentation phase. In the 7th International Conference on Software, Knowledge, Intelligent Management and Applications, Chiang Mai, Thailand: 104-114.
  17. Tong S and Chang E (2001). Support vector machine active learning for image retrieval. In the 9th ACM International Conference on Multimedia, ACM, Ottawa, Canada: 107-118.
  18. Tsochantaridis I, Hofmann T, Joachims T, and Altun Y (2004). Support vector machine learning for interdependent and structured output spaces. In the 21st International Conference on Machine learning, ACM, Alberta, Canada: 1-8.
  19. Turgeon ML (2005). Clinical hematology: Theory and procedures. Lippincott Williams and Wilkins, Pennsylvania, USA.
  20. Vaghela HP, Modi H, Pandya M, and Potdar MB (2015). Leukemia detection using digital image processing techniques. Leukemia, 10(1): 43-51.
  21. Wu KL, Yu J, and Yang MS (2005). A novel fuzzy clustering algorithm based on a fuzzy scatter matrix with optimality tests. Pattern Recognition Letters, 26(5): 639-652.
  22. Yeoh EJ, Ross ME, Shurtleff SA, Williams WK, Patel D, Mahfouz R, Behm FG, Raimondi SC, Relling MV, Patel A, and Cheng C (2002). Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell, 1(2): 133-143.