Abstract
Determination of the WBC count of the body necessitates the detection of white blood cells (leukocytes). During an annual physical checkup, generally doctors prescribe for a complete blood count report. WBC count is required to determine the existence of disease for symptom like body aches, chills, fever, headaches, and many more. The existence of autoimmune diseases, immune deficiencies, blood disorders, and hidden infections within human body can also be alerted by the report of WBC count. The usefulness of chemotherapy or radiation treatment, especially for cancer patients, is also monitored by this report. This paper introduces an automated system to detect the white blood cell from the microscopic image of human blood sample using several image processing techniques.
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References
Mohamed, M., Far, B., Guaily, A.: An efficient technique for white blood cells nuclei automatic segmentation. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 220–225 (2012)
Kaur, R., Kaur, H.: Comparative analysis of white blood cell by different segmentation methods using knowledge based learning. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 3(9), 11720–11728 (2014)
Marzuki, N.I.C., Mahmood, N.H.H., Razak, M.A.A.: Segmentation of white blood cell nucleus using active contour. J. Teknologi 74(6), 115–118 (2015)
Bhamare, M.G., Patil, D.S.: Automatic blood cell analysis by using digital image processing: a preliminary study. Int. J. Eng. Res. Technol. 2(9), 3137–3141 (2013)
Pavithra, S., Bagyamani, J.: White blood cell analysis using watershed and circular hough transform technique. Int. J. Comput. Intell. Inform. 5(2), 114–123 (2015)
Prabakaran, M.K., Khan, F.I., Abrar, N.M., Abbas, F.M., Khrshid, S.S.: A smart sensing and quantification of platelets, red blood cells (RBC), white blood cells (WBC) and classification of WBC’s using microscopic blood image. Int. J. Appl. Med. Sci. Res. 1(1), 1–9 (2015)
Ravikumar, S., Shanmugam, A.: WBC image segmentation and classification using RVM. Appl. Math. Sci. 8(45), 2227–2237 (2014)
Othman, M.Z., Ali, A.B.: Segmentation and feature extraction of lymphocyte WBC using microscopic images. Int. J. Eng. Res. Technol. 3(12), 696–701 (2014)
Shivhare, S., Shrivastava, R.: Automatic bone marrow white blood cell classification using morphological granulometric feature of nucleus. Int. J. Sci. Technol. Res. 1(4), 125–131 (2012)
Chanda, B., Majumder, D.D.: Digital Image Processing and Analysis. Prentice Hall, India (2009)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, India (2009)
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Banerjee, S., Ghosh, B.R., Giri, S., Ghosh, D. (2018). Automated System for Detection of White Blood Cells in Human Blood Sample. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Computing and Informatics . Smart Innovation, Systems and Technologies, vol 77. Springer, Singapore. https://doi.org/10.1007/978-981-10-5544-7_2
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DOI: https://doi.org/10.1007/978-981-10-5544-7_2
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