DEVELOPMENT OF A SIMPLE METHODOLOGY BASED ON FRACTAL MATHEMATICS FOR SELECTIVE DIAGNOSIS OF RED BLOOD CELLS DISORDERS

ALCIDES J. LEGUTO; JUAN PABLO REBECHI; MANCILLA CANALES MANUEL; BORTOLATO, SANTIAGO; PATRICIA PONCE DE LEÓN; PÉREZ SUSANA; ANA KOROL

Tucuman

Congreso; XXXVI Congreso Argentino de Mecánica Computacional; 2018

AMCA

Automatic characterization of different populations of red blood cells (RBCs) is a useful tool in Hematology and Clinical Diagnosis. In this work we focus on different pathologies that affect hemorheological properties of human blood. Our main aim is to develop an analytical methodology to aid in the diagnosis of those pathologies that undergo specifically by altering RBCs membrane dynamic properties. The alterations on the RBC membranes were studied with box-counting dimension (BCD). BCDs were estimated by a standardized analysis of de noised images of RBC suspensions obtained with an optical microscope and a non-professional digital camera. The systematical de noising was carried out by application of Wavelet transform on the images. BCD is a fractal quantifier that has been proven to depend on the levels of RBC aggregation. Wavelet transform de noising technique implies the decomposition of the image signals in a set of wavelets and the selection of the most significant ones through which the image can be reconstructed. In this work we compared the BCD estimated on RBC suspensions from healthy patients and from those affected by parasitosis (trichinosis and ascariasis), leukemia and iron-deficiency anemia. For samples obtained from patients with parasitosis or iron-deficiency anemia, which had been reported to alter hemorheological properties of blood, estimated BCD main values and variances were significatively different when compared with healthy samples. For samples from patients with leukemia, although visual differences were observed in the obtained photographs, the estimated BCDs were not different to healthy ones. The developed methodology is proven to be an effective and selective tool for clinical diagnosis of hematological relevant diseases. Moreover, the chosen mathematical quantifier allows a user-independent characterization that could potentially be applied to distinguish among different stages of blood diseases in low-resources laboratories.