An automated method of differential red blood cell classification with application to the diagnosis of anemia

Abstract

A method of automated red cell analysis suitable for the rapid classification of large numbers of red cells from individual blood specimens has been developed, and preliminarily tested on normal bloods and clinically proven cases of anemias and red cell disorders. According to this method digital image processing techniques provide several features relating to shape and internal central pallor configurations of red cells. These features are used with a fully automated decision logic to rapidly provide a quantitative 'red cell differential' analysis, a report of the percentage subpopulations of recognized categories of red cells. For each subpopulation, measurements of mean cell area, mean cell hemoglobin content and mean cell hemoglobin density are provided. The nine types of red cell disorders studied with this method were: iron deficiency anemia; the anemia of chronic disease, β-thalassemia trait, sickle cell anemia, hemoglobin C disease, intravascular hemolysis, hereditary elliptocytosis, hereditary spherocytosis, and megaloblastic anemia due to folic acid deficiency. Preliminary indications are that the red cell differential is useful in distinguishing between these conditions.