Blood cell images segmentation and enumeration based on circle detection algorithm

• Main Authors: Mohammed, Sura Thaar, Shujaa, Mohamed Ibrahim, Zghair, Entidhar Mhawes, Fadel, Ahmed Abbas
• Format: Conference Proceeding
• Language: English
• Subjects: Abnormalities; Algorithms; Automation; Blood; Counting; Developing countries; Diagnosis; Digital imaging; Enumeration; Erythrocytes; Image acquisition; Image processing; Image segmentation; LDCs; Leukemia; Medical imaging
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0236877

Blood cell count is crucial to medical diagnosis. Numerous disorders in the human body are caused by changes in the blood cell count. There are several methods for counting blood cells, including automatic and conventional methods. Under a microscope, the traditional hand-counting method takes a lot of time and produces unreliable findings. Even with hardware solutions like the Automated Hematology Counter, developing nations are unable to set up such prohibitively expensive equipment in each hospital laboratory nationwide. In order to address this issue and offer a software-based, affordable, and efficient replacement for blood cell identification and assessment, this research offers a preliminary analysis of digital image processing-based automated blood cell counting. The patient’s diagnosis and the identification of abnormalities, such as leukemia, can subsequently be made using the RBC (red blood cell) and WBC (white blood cell) counts of blood cells. A few methods for pre- and post-processing have been applied to the blood cell picture for this reason arranged to provide a considerably fresher and clearer image. Lastly, picture processing includes cell counting algorithms as well as image acquisition, pre-processing, segmentation, and post-processing.