Case Report Open Access

Survey on Automatic RBC Detection and Counting

Abstract

Blood cell segmentation and identification is very important as blood being health indicator. A person’s health is determined using complete blood count. The contents of the blood in particular the white blood cells, the red blood cells and platelets define the state of health. For detection and treatment of diseases like anemia, leukemia etc. RBC count is required. In laboratory, blood cell counting is done by using hemocytometer and microscope. This method gives inaccurate and unreliable results that depend on physician skill. This task is laborious and time consuming. The aim of this research is to produce a survey on computer vision system that can detect and estimate the number of red blood cells in the blood sample image using image processing algorithms. This paper considers image processing for counting of blood cells. Image processing algorithms involve six major steps: image acquisition, preprocessing, image enhancement, image segmentation, feature extraction and counting algorithm. The objective is to study the different methodologies of RBC counting and identification of research directions.

Amruta Pandit, Shrikrishna Kolhar, Pragati Patil

To read the full article Download Full Article | Visit Full Article