Improved red blood cell counting in thin blood smears

Abstract

Quantification of the extent of malaria parasite infection (parasitaemia) continues to rely on time-consuming manual microscopy of Giemsa-stained blood smears. We present an algorithm that counts red blood cells in thin blood smear images, the first step in the determination of malaria parasitaemia. Morphological methods and iterative thresholding are used for red blood cell segmentation, and boundary curvature calculations and Delaunay triangulation for red blood cell clump splitting. Our results compare well with those of published semi-automated methods, with an absolute error of 2.8% between manual and automatic counting of red blood cells.