Use of graph algorithms in the processing and analysis of images with focus on the biomedical data

Abstract

Image segmentation is a known problem in the fi eld of image processing. A great number of methods based on different approaches to this issue was created. One of these approaches utilizes the fi ndings of the graph theory. METHODS: Our work focuses on segmentation using shortest paths in a graph. Specifi cally, we deal with methods of "Intelligent Scissors," which use Dijkstra's algorithm to fi nd the shortest paths. RESULTS: We created a new software in Microsoft Visual Studio 2013 integrated development environment Visual C++ in the language C++/CLI. We created a format application with a graphical users development environment for system Windows, with using the platform .Net (version 4.5). The program was used for handling and processing the original medical data. CONCLUSION: The major disadvantage of the method of "Intelligent Scissors" is the computational time length of Dijkstra's algorithm. However, after the implementation of a more effi cient priority queue, this problem could be alleviated. The main advantage of this method we see in training that enables to adapt to a particular kind of edge, which we need to segment. The user involvement has a signifi cant infl uence on the process of segmentation, which enormously aids to achieve high-quality results (Fig. 7, Ref. 13). Text in PDF www.elis.sk.

The Concept of Advanced Multi-Sensor Monitoring of Human Stress

“…On the other hand, dry electrodes are more practice from long-term measurements point of view and additionally, they do not degrade over time [ 81 ]. Several publications deal with the minimization of motion artifacts with the help of signal filtering and software applications [ 90 , 91 , 117 , 118 , 119 ]. For example, the study of Posada-Quintero [ 81 ] describes corrections of artifacts using a stationary wavelet transform and filter transformation of the curve.…”