Automatic White Blood Cells Counting Using OPENCV

Abstract

Counting the number of white blood cells (WBCs) is a crucial procedure in medical laboratories for diagnosing various diseases. However, manual counting can be time-consuming and susceptible to errors. To overcome this, a research study has proposed an automated approach for WBC counting in sampled images using OpenCV, an open-source computer vision library. The authors developed an algorithm that segments the WBCs from the background by utilizing preprocessing techniques, followed by edge detection (canny edge detection) to identify the cells' boundaries. The number of cells is counted by implementing a simple circular Hough transform method. For this, the authors approached and collected datasets from ALL-IDB team for sampled images to test the proposed method. The proposed method has achieved high accuracy rates and outperformed manual counting in terms of speed and efficiency. The developed approach has the potential to be integrated into existing medical laboratory workflows, automating the WBC counting process and improving the diagnosis and treatment of various diseases.