Abstract
White Blood cell (WBC) is one of the most important blood cells in our body. This cell controls human immune system. The high count and low count of WBC reflect different kinds of disease in our body. Therefore, WBC counting is very important to diagnose several diseases. The pathological process of WBC counting is very time consuming, tedious, and prone to human error. Thus, an automated WBC counting system is highly needed to obtain accurate results. WBC segmentation is very important to make an automated WBC counting system. In our work, we are proposing a color-based WBC segmentation algorithm, which segments WBCs from microscopic images of peripheral blood smear in smart phones. Experimental results show the system to be robust and effective for WBC segmentation. The proposed technique will be very helpful to make an automated WBC counting system and could potentially overcome the errors arising due to manual inspection.
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References
Martin S. Blumenreich; “The White Blood Cell and Differential Count”; Chapter 153; Clinical Methods: The History, Physical, and Laboratory Examinations. 3rd edition; Boston: Butterworths; 1990.
Hansen LK1, Grimm RH Jr, Neaton JD. “The relationship of white blood cell count to other cardiovascular risk factors.” Int J Epidemiol. 1990 Dec; 19(4):881–8.
Leyza Baldo Dorini, Rodrigo Minetto and NeucimarJerˆonimoLeite: “Semi-automatic white blood cell segmentation based on multiscale analysis” IEEE Journal Of Biomedical And Health Informatics, Vol. 17, No. 1, January 2013.
W. Shitongand, 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, no. 1, pp. 5–10, Jan. 2006.
J.B. Nemane, V. A. Chakkarwar: “A Novel Method of White Blood Cell Segmentation and Counting”, International Journal on Advanced Computer Engineering and Communication Technology Vol-1 Issue:1: ISSN2278–5140.
H. D. Cheng, X. H. Jiang, Y. Sun, J. Wang. Color image segmentation: advances and prospects. Pattern Recognition, 2001, 34(12):2259–2281.
Jun Duan, Le Yu; “A WBC SEGMENTATION METHORD BASED ON HSI COLOR SPACE”; Proceedings of IEEE IC-BNMT 2011.
Q. Liao and Y. Deng, “An accurate segmentation method for white blood cell images,” in Proc. IEEE Int. Symp. Biomed. Imag., 2002, pp. 245–248.
B. Kumar and T. Sreenivas, “Teager energy based blood cell segmentation,”in Proc. Int. Conf. Digital Signal Process., 2002, vol. 2, pp. 619–622.
Pramit Ghosh, Debotosh Bhattacharjee, Mita Nasipuri, “Blood smear analyzer for white blood cell counting: A hybrid microscopic image analyzing technique”, Applied Soft Computing, Elsevier, January, 2016.
Pramit Ghosh, Debotosh Bhattacharjee, Mita Nasipuri, “An Automatic Non-invasive System for Diagnosis of Tuberculosis”, Applied Computation and Security Systems, Springer India, 2015.
Pramit Ghosh, Debotosh Bhattacharjee, Mita Nasipuri, “Automation in Sputum Microscopy: A Hybrid Intelligent Technique in Diagnostic Device Automation”, Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications, IGI Global, 2015.
Pramit Ghosh, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu, “Computer Intelligence in Healthcare”, Handbook of Research on Computational Intelligence for Engineering, Science, and Business, 2013.
Pramit Ghosh, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu, “Medical Aid for Automatic Detection of Malaria”, arXiv preprint arXiv:1312.0940, 2013.
Pramit Ghosh, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu, “Automatic White Blood Cell Measuring Aid for Medical Diagnosis”, International Conference on Process Automation, Control and Computing (PACC), IEEE, 2011.
Application note “Measuring color using Hunter L, a, b versus CIE 1976 L*a*b*”. www.hunterlab.com (Accessed on 4/9/2015).
Connolly C and Fliess T, “A Study of Efficiency and Accuracy in the Transformation from RGB to CIELAB Color Space”, IEEE Transactions on Image Processing, vol. 6, no. 7, July 1997.
“Mathematical morphology”, Chapter 6; http://www.cs.uu.nl/docs/vakken/ibv/reader/chapter6.pdf (Accessed on 9th September, 2016).
Acknowledgements
The authors are grateful to DBT, Govt. of India (Letter No. BT/PR8456/MED/29/739/2013) for financial support.
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Dey, R., Roy, K., Bhattacharjee, D., Nasipuri, M., Ghosh, P. (2018). A Novel Approach for Segmenting WBCs in Smartphones Using Color-Based Segmentation. In: Mandal, J., Saha, G., Kandar, D., Maji, A. (eds) Proceedings of the International Conference on Computing and Communication Systems. Lecture Notes in Networks and Systems, vol 24. Springer, Singapore. https://doi.org/10.1007/978-981-10-6890-4_27
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DOI: https://doi.org/10.1007/978-981-10-6890-4_27
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