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Microscopic image analysis and counting of red blood cells and white blood cells in a urine sample

Abstract:

Traditional counting of red blood cells (RBC) and white blood cells (WBC) in a urine sample is done manually by a medical technologist. However, this makes the blood cell count subjective to the skill of the laboratory technician and will take much longer time in doing the task. This paper proposes the use of image processing in counting the WBCs and RBCs in a urine sample through use of Canny Edge Detection and Circular Hough Transform algorithm. The process consists of two (2) main parts. First is the Canny Edge Detection and the final part is Circular Hough Transform algorithms. It shows that the proposed system has a percentage accuracy of at least 93.229% in reference to the actual RBC and WBC count result.