Classification of Red Blood Cells in the Kendall Space of Reflection Shapes

The classification of red blood cells (RBCs) or erythrocytes into three categories based on their shape, normal, sickle-shaped, and those with other deformations, has proven to be a crucial tool in diagnosing and managing sickle cell disease (SCD). Manual classification techniques have evolved into automated tools, with numerous classification methods being applied based on different ways of representing the cells. In this work, we propose a novel methodology for representing RBCs, defined by selecting k landmarks along the cell boundaries and characterizing shapes as points in the Kendall space of reflection shapes, Ω2k. Using this representation, we applied an embedding of the Kendall space Ω2k into a Euclidean space, which allowed for the use of machine learning classification algorithms. We also compared our results with those obtained using other classification methods applied to the same dataset in the literature, highlighting the strong performance of our approach in terms of classification accuracy.