Bi-Level algorithm for the segmentation and counting of Leukocytes and Erythrocytes

Duraiswamy Umamaheswari; Shanmugam Geetha

doi:10.17485/IJST/v13i45.328

Article

Bi-Level algorithm for the segmentation and counting of Leukocytes and Erythrocytes

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v13i45.328

Year: 2020, Volume: 13, Issue: 45, Pages: 4541-4554

Original Article

Bi-Level algorithm for the segmentation and counting of Leukocytes and Erythrocytes

Duraiswamy Umamaheswari^1*, Shanmugam Geetha²

¹Research Scholar, Department of Computer Science, Government Arts College, Udumalpet, 642126, Tamil Nadu, India. Tel.: +91-984372443
²Assistant Professor, Department of Computer Science, LRG Government Arts College for Women, Tiruppur, 641604, Tamil Nadu, India

*Corresponding Author
Tel: +91-984372443
Email: [email protected]

Received Date:22 April 2020, Accepted Date:19 June 2020, Published Date:19 December 2020

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background/Objectives: To present an accurate quantitative approach based two-phase algorithm to count both the leukocytes and erythrocytes for identifying the severity of leukaemia in the human body. Methods/Statistical analysis: The algorithm is having two-phases with the first phase meant for recognizing and counting the leukocytes using the thresholding based segmentation technique that focuses on the intensity values of pixels of the greyscale blood smear images; whereas the second phase recognizes the erythrocytes by their circular shape using Circular Hough Transform (CHT) method. The system experiments with 26 stained blood smear images from the ALL-IDB1 benchmark dataset. Findings: The first phase of the algorithm achieves 99.41 per cent overall accuracy in leukocytes detection and in the second phase 99.76 per cent overall accuracy is attained in erythrocytes detection. Novelty/Applications: This proposal applies Circular Hough Transform in detecting the erythrocytes by adjusting the radius of the circle according to the magnification rate of the sample image.

Keywords: Circular Hough transform; cell count; image processing; Leukaemia; Leukocytes; Erythrocytes

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Copyright

© 2020 Umamaheswari & Geetha.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)