Analysis of Distance Transforms for Watershed Segmentation on Chronic Leukaemia Images


  • T. Ahmad Aris Faculty of Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, 02100 Padang Besar, Perlis, Malaysia
  • A. S. Abdul Nasir Faculty of Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, 02100 Padang Besar, Perlis, Malaysia
  • W. A. Mustafa Faculty of Engineering Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, 02100 Padang Besar, Perlis, Malaysia


Chronic Leukaemia, Distance Transform, Otsu’s Thresholding, Watershed Segmentation,


Leukaemia is a blood cancer that contributes to the increase in the world mortality rates per year. Leukaemia can be divided into two major types which are acute and chronic leukaemia. This disease is caused by the excessive production of abnormal white blood cells (WBCs); hence these cells play a major role in the screening and diagnosis of leukaemia disease. Leukaemia screening requires the complete blood count process. However, due to the cells complex nature in chronic leukaemia which is overlapped, it would be difficult to obtain the accurate number of the WBCs for the screening process. Therefore, this paper proposes an automated WBCs counting with analysis of watershed segmentation for the screening of chronic leukaemia images. The segmentation approach consists of a few steps; (1) colour conversion, (2) image segmentation, (3) noise removal and (4) separation of overlapping WBCs. In this paper, three different distance transforms for watershed segmentation known as Euclidean, city block and chessboard have been analysed in order to find the best approach which is capable of separating the overlapping WBCs. The experimental results show that segmentation using watershed based on Euclidean has successfully segmented 50 blood images with average counting accuracy of 99.81%, as compared to the city block (91.09%) and chessboard (98.78%). Thus, the proposed procedures with watershed segmentation provide an efficient alternative in enhancing the accuracy of the WBCs count for leukaemia screening.


Z. A. Omar, Z. M. Ali, and N. S. I. Tamin, Malaysian Cancer Statistics: Data and Figure Peninsular Malaysia 2006. Malaysia: National Cancer Registry, 2006.

G.C.C. Lim, “Overview of cancer in Malaysia,” Japanese Journal of Clinical Oncology, vol. 32, pp. 37–42, Feb. 2002.

M. Ab Azizah, I.T. Nor Saleha, A. Noor Hashimah, Z.A. Asmah, W. Mastulu, Malaysian National Cancer Registry Report 2007-2011, 2016.

G.P.M. Priyankara, O.W. Seneviratne, R.K.O.H. Silva, W.V.D. Soysa, and C.R.D. Silva, An Extensible Computer Vision Application for Blood Cell Recognition and Analysis. Sri Lanka: University of Moratuwa, 2006.

C. Reta, L. Altamirano, J.A. Gonzalez, R. Diaz, and J.S. Guichard, “Segmentation of bone marrow cell images for morphological classification of acute leukaemia,” in 2010 Proceedings of the TwentyThird International Florida Artificial Intelligence Research Society Conference, pp. 86–91.

A.S. Abdul Nasir, M.Y. Mashor, and H. Rosline, “Unsupervised colour segmentation of white blood cell for acute leukaemia images,” in 2011 IEEE International Conference on Imaging Systems and Techniques, pp. 142–145.

E.A. Mohammed, B.H. Far, C. Naugler, and M.M. Mohamed, “Application of support vector machine and k-means clustering algorithms for robust chronic lymphocytic leukaemia color cell segmentation,” 2013 IEEE 15th International Conference on e-Health Networking, Applications & Services, pp. 622–626.

S. Agaian, M. Madhukar, and A.T. Chronopoulos, “Automated screening system for acute myelogenous leukaemia detection in blood microscopic images,” IEEE Systems Journal, vol. 8, pp. 995–1004, Sep. 2014.

C. Di Ruberto, A. Loddo, and L. Putzu, “A leucocytes count system from blood smear images,” Machine Vision and Applications, vol. 27, pp. 1151–1160, Nov. 2016.

N.H. Harun, M.Y. Mashor, H.N. Lim, and R. Hassan, “Automated white blood cells counting system for acute leukaemia based on blood images,”Jurnal Teknologi, vol. 78, pp. 91–98, Jan. 2016.

S. K. Nayak and N. Sampathila, “Development of a protocol for screening leukaemia from the microscopic images acquired from blood smear,”IJRET: International Journal of Research in Engineering and Technology, vol. 5, pp. 8–11, 2016.

S.D. Devi, R. Sharada, R. Shankari, T. Tamilarasi, and G. Priya, “Automatic Diagnosis of Acute Lymphoblastic Leukemia Using Duplex Method,”International Journal of Healthcare Sciences, vol. 5, pp. 14–21, 2017.

A. Khashman and E. Al-zgoul, “Image segmentation of blood cells in leukaemia patients,”Recent Advances in Computer Engineering and Applications, pp. 104–109, Jan. 2010.

N. Patel, A. Mishra, “Automated leukaemia detection using microscopic images,”Procedia Computer Science, vol. 58, pp. 635– 642, 2015.

S. Sivakumar and S. Ramesh, “Automatic white blood cell segmentation using k means clustering,”International Journal of Science and Engineering Research, vol. 3, 2015.

W. Shitong and W. Min, “A new detection algorithm (NDA) based on fuzzy cellular neural networks for white blood cell detection,”IEEE Trans. Inf. Technol. Biomed, vol. 10, pp. 5–10, 2006.

M. Ghosh, D. Das, C. Chakraborty, and A.K. Ray, “Automated leukocyte recognition using fuzzy divergence,”Micron, vol. 41, pp. 840–846, 2010.

S. Mishra, B. Majhi, P. K. Sa, and L. Sharma, “Gray level cooccurrence matrix and random forest based acute lymphoblastic leukaemia detection,”Biomedical Signal Processing and Control, vol. 33, pp. 272–280, Mar. 2017.

S. Arslan, E. Ozyurek, and C. Gunduz‐Demir,“A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images,”Cytometry Part A, vol. 85, pp. 480– 490, Jun. 2014.

Q.C.Q. Chen, X.Y.X. Yang, and E.M. Petriu, “Watershed segmentation for binary images with different distance transforms,” in Proceedings of the 3rd IEEE International Workshop on Haptic, Audio and Visual Environments and Their Applications, pp. 111–116.

N. Theera-Umpon and P.D. Gader, “Training neural networks to count white blood cells via a minimum counting error objective function,” in 2000. Proceedings. 15th International Conference on Pattern Recognition, pp. 299–302.




How to Cite

Ahmad Aris, T., Abdul Nasir, A. S., & Mustafa, W. A. (2018). Analysis of Distance Transforms for Watershed Segmentation on Chronic Leukaemia Images. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-16), 51–56. Retrieved from