Automated Recognitions of White Blood Cells

  • More Kaveri Bajirao Department of IT, MCOERC, Nashik ,Maharashtra Pune University

Abstract

Healthcare services are important part of   cities. Now a day’s healthcare plays important role in the transformation of cities into smart cities. Leucocytes play a vital role in immune system which helps to protect the body against both infectious disease and foreign invaders. Identification of leukocytes in blood smear provides important information to pathologist as well as doctors to analyze and predicts different types of hematic diseases, such as AIDS and blood cancer (Leukemia). Most of the time, the health professional are interested on leucocytes in white blood cells only. Digital image processing procedures can help them in their examine and detecting. For example, disease like cancer and AIDS is detected based on the total count of the WBC in the human body. In the proposed framework, leucocyte is first segmented into two dominant elements, nucleus and cytoplasm, followed by extraction of features. The identification of leucocytes can be done with the help of adoptive gradient vector flow snake algorithm.


How to cite this article:
Bajirao MK, Bhaskar SD, Dnyneshwar KK.
Automated Recognitions of White Blood Cells.
J Adv Res Info Tech Sys Mgmt 2019; 3(2): 4-6

References

[1] Yampri, P., Pintavirooj, C., Daochai, S., Teartulakarn, S., 2006. “White blood cell classification based on the combination of
Eigen cell and parametric feature detection”. In: Proceedings of the First IEEE Conference Industrial Electronics and Applications pp. 1–4.
[2] Dorini LB, Minetto R and Leite NJ (2007) White blood cell segmentation using morphological operators and scale-space analysis. In SIBGRAPI '07: Proceedings of the XX Brazilian Symposium on Computer. Graphics and Image Processing.

[3] Chinwaraphat, S., Sanpanich, A., Pintavirooj, C., Sangworasil, M., Tosranon, P., 2008. A modified fuzzy clustering for white blood cell segmentation. In: Proceedings of the Third International of Symposium on Biomedical Engineering, pp. 356–359.
[4] B. C. Ko, J.-W. Gim, and J.-Y. Nam, ``Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake,'' Micron, vol. 42, no. 7, pp. 695_705, Oct. 2011.
[5] F. I. Sholeh, ``White blood cell segmentation for fresh blood smear images,'' in Proc. Int. Conf. Adv. Computer Sci. Inf. Syst. (ICACSIS), Sep. 2013, pp. 425_429.
[6] S. Nazlibilek, D. Karacor, T. Ercan, M. H. Sazli, O. Kalender, and Y. Ege, ``Automatic segmentation, counting, size determination and classification of white blood cells,'' Measurement, vol. 55, pp. 58_65, Sep. 2014.
[7] J. Prinyakupt and C. Pluempitiwiriyawej, ``Segmentation of white blood cells and comparison of cell morphology by linear and naive Bayes classifiers,'' Biomed. Eng. On Line, vol. 14, no. 1, p. 63, 2015.
[8] S. Ravikumar, ``Image segmentation and classification of white blood cells with the extreme learning machine and the fast relevance vector machine,'' Artif. Cells, Nanomed, Biotechnol., vol. 44, no. 3,pp. 985_989, 2016.
[9] muhammad sajjad “ Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities” Digital Object Identifier 10.1109/ACCESS.2016.2636218
Published
2021-10-02
How to Cite
BAJIRAO, More Kaveri. Automated Recognitions of White Blood Cells. Journal of Advanced Research in Information Technology, Systems and Management, [S.l.], v. 3, n. 2, p. 4-6, oct. 2021. Available at: <http://thejournalshouse.com/index.php/information-tech-systems-mngmt/article/view/423>. Date accessed: 19 may 2024.