A microstructural model of Red Blood Cell (RBC) behaviour was proposed. The erythrocyte is treated as a viscoelastic object, which is denoted by a network of virtual particles connected by elastic springs and dampers (Kelvin-Voigt model). The RBC is submerged in plasma modelled by lattice Boltzmann fluid. Fluid – structure interactions are taken into account. The simulations of RBC behaviour during flow in a microchannel and wall impact were performed. The results of RBC deformation during the flow are in good agreement with experimental data. The calculations of erythrocyte disaggregation from the capillary surface show the impact of RBC structure stiffness on the process.
In this paper the authors propose a decision support system for automatic blood smear analysis based on microscopic images. The images are pre-processed in order to remove irrelevant elements and to enhance the most important ones – the healthy blood cells (erythrocytes) and the pathologic ones (echinocytes). The separated blood cells are analysed in terms of their most important features by the eigenfaces method. The features are the basis for designing the neural network classifier, learned to distinguish between erythrocytes and echinocytes. As the result, the proposed system is able to analyse the smear blood images in a fully automatic way and to deliver information on the number and statistics of the red blood cells, both healthy and pathologic. The system was examined in two case studies, involving the canine and human blood, and then consulted with the experienced medicine specialists. The accuracy of classification of red blood cells into erythrocytes and echinocytes reaches 96%.