Morphology of erythrocyte membrane has been recognized as an alternative biomarker of several patho-physiological states. Numerous attempts have been made to upgrade the existing method of primitive manual counting, particularly exploring the light scattering properties of erythrocyte. All the techniques are at best semi-empirical and heavily rely on the effectiveness of the statistical correlations. Precisely, this is due to the lack of a non-empirical scale of the so-called "morphological scores". In this article, fractal dimension of erythrocyte membrane has been used to formulate a suitable scoring scale. Subsequently, the rapid experimental output of flow-cytometry has been functionally related to the mean morphological quantifier of the whole cell population via an optimum neural network model (R2 = 0.98). Moreover, the fractal dimension has been further demonstrated to be an important parameter in early detection of an abnormal patho-physiological state, even without any noticeable poikilocytic transformation in micrometric domain.
Keywords: Erythrocyte morphology; Fractal dimension; Membrane roughness; Morphological scale; Neural network model.