A Comparative Study of White Blood cells Segmentation using Otsu Threshold and Watershed Transformation

  • Nancy Salem Department of Biomedical Engineering, Faculty of Engineering, Helwan University, Egypt;
  • Noorhan M Sobhy Department of Biomedical Engineering, Faculty of Engineering, Helwan University, Egypt
  • Mohamed El Dosoky Department of Biomedical Engineering, Faculty of Engineering, Helwan University, Egypt
Keywords: White blood cells, Leukaemia, segmentation, Otsu threshold, watershed, feature extraction,

Abstract

The aim of white blood cells (WBC) segmentation is to separate leukocytes from other different components in the blood peripheral image. In this paper, a method to segment white blood cells from microscopic images is proposed. The proposed method consists of three stages; Pre-processing, segmentation, and finally post-processing. In the pre-processing step; the color correction is used to enhance the image. In the segmentation step; two techniques have been used which are Otsu threshold and watershed marker-controlled followed by feature extraction. Shape features are used to differentiate between single and grouped cells. Artifacts are removed in the post-processing step. Experimental results show that the accuracy is 99.3% and 93.3% for the watershed based and the Otsu threshold based methods respectively. Experiments demonstrate that watershed marker-controlled outperforms Otsu threshold in the segmentation of WBC.

References

(1) M. D. Joshi, A. H. Karode, and S. R. Suralkar “Detection of Acute Leukemia using White Blood Cells Segmentation based on Blood Sample,” International Journal of Electronics and Communication Engineering & Technology (IJECET), vol. 4, pp. 148-153, 2013.

(2) S. G. Deore and N. Nemade, “Image Analysis Framework for Automatic Extraction of the Progress of an Infection,” International Journal of Advanced Research in Computer Science and Software

Engineering, vol. 3, pp. 703-707, 2013.

(3) K. A. ElDahshan, M. I. Youssef, E. H. Masameer, and M. A. Hassan, “Comparison of Segmentation Framework on Digital Microscope Images for Acute Lymphoblastic Leukemia Diagnosis using RGB and HSV Color Spaces,” Jouranl Of Biomedical Engineering and Medical Imaging(JBMEI), vol. 2, no. 2, pp. 26-34, 2015.

(4) S. Mohapatra, D. Patra, and S. Satpathy, “Unsupervised Blood Microscopic Image Segmentation and Leukemia Detection using Color based Clustering,” International Journal of Computer Information Systems and Industrial Management Applications., vol. 4, pp. 477-485,

(5) A. Khashman and E. Al-Zgoul, “Image Segmentation of Blood Cells in Leukemia Patients,” Recent Advances in Computer Engineering and Applications, pp.104-109, 2010.

(6) R. Patil, M. Sohani, and S. Bojewar, “Acute Leukemia blast counting using RGB, HSI color spaces,” Proceedings on International Conference in Computational Intelligence (ICCIA 2012), pp. 11- 14, 2012.

(7) N. Raja Rajeswari.V Ramesh, “Contrast stretching enhancement technique for Acute Leukaemia Images,” Publications of Problems & Application In Engineering Research, vol. 4, pp. 190-194 , 2013.

(8) L. Putzu and C. Di Ruberto, “White blood cells identification and counting from microscopic blood image,” World Academy of Science, Engineering and Technology, vol. 73, pp. 363–370, 2013.

(9) H. P. Vaghela, H. Modi, M. Pandya, and M. B. Potdar, “Leukemia Detection using Digital Image Processing Techniques,” International Journal of Applied Information Systems (IJAIS), vol. 10, pp. 43-51, 2015.

(10) Z. Liu, J. Liu, X. Xiao, H. Yuan, X. Li, J. Chang, and Z. Chengyun, “Segmentation of White Blood Cells through Nucleus Mark Watershed Operations and Mean Shift Clustering,” sensors, pp. 22561-22586, 2015.

(11) N. I. Che Marzukia, N. H. Mahmoodb, and M. A. Abdul Razakb, “Segmentation of White Blood Cell Nucleus Using Active Contour,” Jurnal Teknologi, vol. 74, no. 6, pp. 115-118, 2015.

(12) S. Jagadeesh, E. Nagabhooshanam, S. Venkatachalam, “Image Processing Based Approch to Cancer Cell Prediction in Blood Samples,” International Journal of Technology and Engineering Sciences, vol. 1, pp. 1-4, 2013.

(13) F. A. Ajala, O. D. Fenwa, and M. A. Aku., “A comparative Analysis of Watershed and Edge based Segmentation of Red Blood Cells,” International Journal of Medicine and Biomedical Research, vol. 4, issue 1, pp. 1-7, 2015.

(14) C. Reta, L. Altamirano, J. A. Gonzalez, R. Diaz-Hernandez, H. Peregrina, I. Olmos, J. E. Alonso, and Ruben Lobato, “Segmentation and Classification of Bone Marrow Cells Images Using Contextual Information for Medical Diagnosis of Acute Leukemias,” PLOS ONE, vol. 10, no. 6, 2015.

(15) N. Abbas and D. Mohamad, “Occluded Red Blood Cells Splitting via Boundaries Analysis and lines drawing in Microcopic Thin Blood Smear Digital Images,” VFAST Transactions on Software Engineering, vol. 5, no. 1, pp. 10-17, 2014.

(16) A. S. Abdul Nasir, N. Mustafa, and N. F. Mohd Nasir, “Application of Thresholding Technique in Determining Ratio of Blood Cells for Leukemia Detection,” in Proc. of the International Conference on Man-Machine Systems (ICoMMS), October 11-13, 2009.

(17) R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed., Upper Saddle River, New Jersey, Pearson Prentice Hall, 2008.

(18) Q. Wu, Microscope Image Processing, Elsevier, 2008.

(19) 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,” International Society for Advancement of Cytometry, pp. 480-490, 2014.

(20) R. D. Labatir, V. Piuriw, and F. Scotti, “ALL-IDB: The Acute Lymphoblastic Leukaemia Image database for Image Processing,” in Proc. of the IEEE International Conference on Image Processing (ICIP), September 11 - 14, 2011.

Published
2016-07-24
How to Cite
Salem, N., Sobhy, N. M., & Dosoky, M. E. (2016). A Comparative Study of White Blood cells Segmentation using Otsu Threshold and Watershed Transformation. Journal of Biomedical Engineering and Medical Imaging, 3(3), 15. https://doi.org/10.14738/jbemi.33.2078