Abstract:

Medical image analysis is an important task in diagnosing various diseases, among which the study of megaloblastic anemia stands out resulting in improving the publish health. The peculiarity of processing the corresponding images is the selection of the edge for all objects of interest, including the details of the structure of megaloblastic anemia cells. It is shown that for these purposes it is advisable to use the ideology of wavelets. Based on this, the paper considers the issues of the influence of the wavelet type on the selection of the edge in images with megaloblastic anemia cells with changed contrast in the RGB and HSV color spaces. The wavelets considered in this paper include gaus1, haar, db2, and bior1.1. For the purpose of comparing the results, such quality assessments as niqe, brisque, and entropy are used, as well as a visual comparison of the obtained results. It is shown that for selecting the edge and potential areas of interest in images with megaloblastic anemia cells, it is advisable to use the RGB space. It is also noted that the gaus1 wavelet allows for efficient allocation of potential areas of interest, while the haar and bior1.1 wavelets allow for allocation of the edges of objects of interest. The results are presented in the form of various figures and tables.