Background: Modification of erythrocyte morphology is clinically important in hematology and medicine. Its detection is routinely performed by subjective microscopic evaluation, which is difficult and strongly dependent on the operator's expertise. We developed an original automated methodology to analyze erythrocyte cell shape modification to support and improve the operator's capability and expedite measurements.
Methods: We used morphometric parameters derived from optical microscope images elaborated with an image processing software (NIH Scion Image) to construct a new application for statistical multivariate discriminant analysis.
Results: For each cell type the elaboration of the morphometric parameters allowed us to develop a chromogenic index, a dimension index, a biconcavity index, and a density profile. The measurements of these indexes were used to construct a statistical methodology that could discriminate among erythrocyte morphologies according to Bessis. When applied casewise, the model effectively differentiated between discocytes, target cells, ovalocytes, macrocytes, and microcytes, with an agreement of 70% between actual and predicted classifications.
Conclusions: The results clearly demonstrated that a set of opportunely selected morphometric parameters derived from optical microscope images and statistically analyzed can effectively discriminate with a high degree of certainty among different shape modifications that red blood cells can undergo in various in vitro and in vivo conditions. This method represents the first attempt to automate the definition of erythrocyte morphology and may have important applications in cases in which the detection of erythrocyte cell shape changes is crucial.
Copyright 2003 Wiley-Liss, Inc.