Image processing for automated erythrocyte classification.

Abstract

Digital image processing and pattern recognition techniques were applied to determine the feasibility of a natural n-space subgrouping of normal and abnormal peripheral blood erythrocytes into well separated categories. The data consisted of 325 digitized red cells from 11 different cell classes. The analysis resulted in five features: (a) size, (b) roundness, (c) spicularity, (d) eccentricity and (e) central gray level distribution. These features separated the data into six distinct condensed subgroups of red cells. Each subgroup consisted of morphologically similar cells: (a) macrocytes, (b) normocytes, (c) schistocytes, acanthocytes and burr cells, (d) microcytes and spherocytes, (e) elliptocytes, sickle cells and pencil forms and (f) target cells. The concept of a quantitative "red cell differential" was introduced, utilizing these subgroup definitions to establish subpopulations of red cells, with quantifiable indices for the diagnosis of anemia, at the specimen level.