Abstract
The paper describes the implementation of non-hierarchical methods k-means and fuzzy c-means on nosily images from different medical modalities as computed tomography and magnetic resonance. Modern devices are created on the basis of advanced technology, both during the actual acquisition of the image and subsequently during its processing. The problem is caused by the unexpected disturbance of the image by parasitic noise, which may already occur in the electronics of the device or in dependence on the phenomena caused by the external environment. The testing was carried out on 3 datasets of medical images and the evaluation per individual images was determined based on the correlation factor and the mean quadratic error. The result is evaluation of non-hierarchical clustering techniques for the creation of mathematical models of tissue depending on the noise intensity.
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Acknowledgement
The work and the contributions were supported by the project SV450994/2101 Biomedical Engineering Systems XV’. This study was also supported by the research project The Czech Science Foundation (GACR) 2017 No. 17-03037S Investment evaluation of medical device development run at the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic. This study was supported by the research project The Czech Science Foundation (TACR) ETA No. TL01000302 Medical Devices development as an effective investment for public and private entities.
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Krestanova, A. et al. (2020). Quantitative Analysis and Objective Comparison of Clustering Algorithms for Medical Image Segmentation. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12034. Springer, Cham. https://doi.org/10.1007/978-3-030-42058-1_10
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