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ARTICLE

Blood Group Classification System Based on Image Processing Techniques

S. A. Shaban*, D. L. Elsheweikh
Department of Computer Science, Faculty of Specific Education, Mansoura, Egypt
* Corresponding Author: S. A. Shaban. Email:

Intelligent Automation & Soft Computing 2022, 31(2), 817-834. https://doi.org/10.32604/iasc.2022.019500

Received 15 April 2021; Accepted 23 June 2021; Issue published 22 September 2021

Abstract

The present paper proposes a novel system that automatically classifies the eight different blood groups according to the ABO and Rh blood group systems. The proposed system is developed by applying MATLAB’s image processing techniques on the blood sample images. These images are acquired from the laboratory using the slide test. It utilizes a mean filter for removing noise from blood sample images. In addition, the Contrast Limited Adaptive Histogram Equalization (CLAHE) is used for enhancing the image characteristics analysis. The proposed system also utilizes the automated threshold strategy (Otsu’s approach) for obtaining the blood samples binary images. Since, adding the three antigens (A, B, D) to the blood samples works on clumping the samples or not, and this, in turn, works on the variation number of objects and holes in the different blood groups’ samples. Therefore, the “Bwboundaries” Matlab function is applied for the first time in the proposed system to count the number of objects and holes for blood group classification. The database used in the proposed system consists of 600 images collected from different laboratories. The suggested system has many advantages such as simplicity, speeding up processing time, reducing human interference and errors, decreasing the risk of transfusion reactions, and high accuracy, even in remote places. As well, it can be used by a lab technician or any user who doesn’t have any previous knowledge of the blood group detection technique. In addition to using this system to assist in several applications such as premarital tests, blood transfusion as well as paternity tests.

Keywords

ABO blood system; premarital tests; blood transfusion; paternity tests; image processing technology; mean filter; CLAHE; Otsu’s approach; “Bwboundaries” Matlab function

Cite This Article

S. A. Shaban and D. L. Elsheweikh, "Blood group classification system based on image processing techniques," Intelligent Automation & Soft Computing, vol. 31, no.2, pp. 817–834, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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