Article: Fuzzy-C means segmentation of lymphocytes for the identification of the differential counting of WBC Journal: International Journal of Cloud Computing (IJCC) 2021 Vol.10 No.1/2 pp.26 - 42 Abstract: In the domain of histology, discovering the population of white blood cells (WBC) in blood smears helps to recognise destructive diseases. Standard tests performed in hematopathological laboratories by human experts on the blood samples of precarious cases such as leukaemia are time-consuming processes, less accurate and totally depending upon the expertise of the technicians. In order to get the advantage of faster analysis time and perfect partitioning at clumps, an algorithm is proposed in this paper that automatically identifies the counting of lymphocytes present in peripheral blood smear images containing acute lymphoblastic leukaemia (ALL) that performs lymphocytes segmentation by fuzzy C-means (FCM) clustering. Afterward, neighbouring and touching cells in cell clumps are individuated by the watershed transform (WT), and then morphological operators are applied to bring out the cells into an appropriate format in accordance with feature extraction. The extracted features are thresholded to eliminate the regions other than lymphocytes. The algorithm ensures 98.52% of accuracy in counting lymphocytes by examining 80 blood-smear image samples of the ALL-IDB1 dataset. The research works in showing this kind of improved accuracy facilitates in identifying leukaemia on starting stages for uncomplicated healing. Inderscience Publishers - linking academia, business and industry through research

Title: Fuzzy-C means segmentation of lymphocytes for the identification of the differential counting of WBC

Authors: Duraiswamy Umamaheswari; Shanmugam Geetha

Addresses: Department of Computer Science, Government Arts College, Udumalpet-642126, Tamil Nadu, India ' Department of Computer Science, LRG Government Arts College for Women, Tiruppur-641604, Tamil Nadu, India

Abstract: In the domain of histology, discovering the population of white blood cells (WBC) in blood smears helps to recognise destructive diseases. Standard tests performed in hematopathological laboratories by human experts on the blood samples of precarious cases such as leukaemia are time-consuming processes, less accurate and totally depending upon the expertise of the technicians. In order to get the advantage of faster analysis time and perfect partitioning at clumps, an algorithm is proposed in this paper that automatically identifies the counting of lymphocytes present in peripheral blood smear images containing acute lymphoblastic leukaemia (ALL) that performs lymphocytes segmentation by fuzzy C-means (FCM) clustering. Afterward, neighbouring and touching cells in cell clumps are individuated by the watershed transform (WT), and then morphological operators are applied to bring out the cells into an appropriate format in accordance with feature extraction. The extracted features are thresholded to eliminate the regions other than lymphocytes. The algorithm ensures 98.52% of accuracy in counting lymphocytes by examining 80 blood-smear image samples of the ALL-IDB1 dataset. The research works in showing this kind of improved accuracy facilitates in identifying leukaemia on starting stages for uncomplicated healing.

Keywords: fuzzy C-means; FCM; medical image processing; morphology; segmentation; watershed; WBC count; leukaemia.

DOI: 10.1504/IJCC.2021.113974

International Journal of Cloud Computing, 2021 Vol.10 No.1/2, pp.26 - 42

Received: 22 May 2019
Accepted: 20 Jul 2019
Published online: 06 Apr 2021 *

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Title: Fuzzy-C means segmentation of lymphocytes for the identification of the differential counting of WBC

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