ANALYSIS OF THE COMPUTATIONAL COMPLEXITY OF ALGORITHMS FOR SINGLING OUT AREAS OF INTEREST ON MRI DATA
Today, cell therapy is one of the topical trends in regenerative medicine, and the use of magnetic resonance imaging is one of the main ways to carry out fundamental biomedical research. Analysis of the data of magnetic resonance imaging makes it possible to study areas of interest and the dynamics of their change. The combination of modern methods of magnetic resonance imaging and methods of scientific imaging makes it possible to build maps of the migration and distribution of stem cells after their transplantation, which gives an impetus to understanding the mechanisms of their action on the foci of the disease, for example, in ischemic stroke. However, the large volumes of information received from the tomograph slow down the speed of their interpretation by doctors in clinical and scientific research. To increase the efficiency of informational support of the researcher, methods and software have been developed for the automatic singling and visualization of areas of interest (clusters of mesenchymal stem cells and areas of ischemic lesion) on magnetic resonance imaging images. This paper provides descriptions of the developed algorithms in the pseudocode language. The presented methods and software are intended to be included in the information support system for the work of medical researchers. Estimates of their computational complexity are given. An experimental analysis of the performance of the developed algorithms is carried out.