Automated segmentation of erythrocytes from Giemsa-stained thin blood films

Abstract

This paper investigates automated segmentation of malaria parasites in images of Giemsa-stained thin blood film specimens. The Giemsa staining exhibits not only on the malaria parasites, but also platelets and artifacts. We aim to extract erythrocytes both normal and infected cells from other particles and separate overlapping cells. Our approach is compared with manual cell counting and existing program named CELLCOUNTER. Our processing framework provides 97% accuracy, which yields predominant detection more accurate than the CELLCOUNTER. The results also indicate high correlation between our proposed method and the manual cell counting.