Box counting dimension of red blood cells samples when filtered with wavelet transform

Abstract

Automatic recognizing of different populations of several millions of red blood cells (RBCs) is a useful tool in Hematology and Clinical Diagnosis. In this work we studied samples of several millions of RBCs: on one hand healthy control RBCs and on the other hand control RBCs incubated with Trichinella spiralis larval parasites. The alteration on the cells membrane with the parasite can be studied with box-counting dimension on both samples. Previously we applied wavelet transform to all the samples in order to improve the results. The procedure to remove noise from an image is based on the decomposition of the observed signal in a set of wavelets and taking threshold values to select the appropriate coefficients through which the signal can be reconstructed. In our work we compared the results obtained when analyzing the raw signals and the ones obtained after applying wavelet transform, and the results were different and more clearly characterized when the signal were treated with wavelet transform. Finally, the present method using wavelet transform is suitable to optimize the characterization of the RBCs damage when incubated with the larval parasites.