Review on Automatic Detection & Counting of RBC’s by Using Image Processing Technique

Abstract

Accurate counting of red blood cells (RBCs) in microscopic images is critical for various medical diagnoses and research purposes. Manual counting methods are labor-intensive, time-consuming, and prone to human error. This project aims to automate the RBC counting process using advanced image processing techniques. The primary objective is to improve the efficiency of the counting process, minimize subjectivity, and increase productivity in medical laboratories. The proposed method uses computer vision algorithms to analyze digital images of blood smears captured under a microscope. A combination of image segmentation and morphological operations is then applied to isolate individual RBCs, overcoming challenges posed by overlapping cells and variations in staining. Keywords: Image processing; Segmentation; Red Blood cells; Hough Transformation; RBC count.