Models of images of human biological fluids facies

• Published in: Procedia computer science Vol. 192; pp. 4055 - 4062
• Main Authors: Krasheninnikov, Victor R., Malenova, Olga E., Subbotin, Alexey U., Trubnikova, Larisa I., Albutova, Marina L.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2021
• Subjects: autoregressive model; biological fluid; circular images; correlation function; facies; marker; medical diagnostic; marker; autoregressive model; medical diagnostic; facies; circular images; biological fluid; correlation function
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2021.09.180

The precise diagnostics of different diseases is very important for its treatment. It is particularly important to differentiate the disease on the early stages when the pathological alterations have not yet caused great harm to the whole organism, since this allows using greater number of therapies and increase the recovery probability. One of the methods of early diagnostics is based on examination of human biological liquids (blood, tears, cervical mucus, urine, etc,). A small drop of liquid is drawn on an object-plate and dried out slowly, thus a thin dry film (facies) remains. In the process of fluid crystallization there appear characteristic patterns (markers) in the facies. Each marker is a highly definite sign of some pathology even at an early stage of a disease development. When mass population health examination is carried out, it is necessary to analyze a large number of images This is the reason the problem of algorithm and software development for automated processing of images is rather urgent nowadays. Mathematical models of facies images are needed to formalize the tasks of image processing and synthesis of algorithms. The facies has a round shape, as it is obtained from a drop of liquid. Facies images have a radial-ring structure due to the peculiarities of the drying and crystallization of the contained fractions. These facies features should be taken into account in their imaging models. In this paper, autoregressive models of circular images are used to represent facies images. These models use grids defined on concentric circles or Archimedes’ spirals. The radial-ring structure of facies image correlations is naturally taken into account in these models. The paper contains examples of model identification, that is, estimation of the values ​​of its parameters at which the correlation properties of the model are closest to the characteristics of a given facies. It is shown that the classification of the facies type (corresponding to the general condition of the patient) is possible according to these estimates.