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Andronache I, Peptenatu D, Ahammer H, Radulovic M, Djuričić GJ, Jelinek HF, Russo C, Di Ieva A. Fractals in the Neurosciences: A Translational Geographical Approach. ADVANCES IN NEUROBIOLOGY 2024; 36:953-981. [PMID: 38468071 DOI: 10.1007/978-3-031-47606-8_47] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
The chapter presents three new fractal indices (fractal fragmentation index, fractal tentacularity index, and fractal anisotropy index) and normalized Kolmogorov complexity with proven applicability in geographic research, developed by the authors, and the possibility of their future use in neuroscience. The research demonstrates the relevance of fractal analysis in different fields and the basic concepts and principles of fractal geometry being sufficient for the development of models relevant to the studied reality. Also, the research highlighted the need to continue interdisciplinary research based on known fractal indicators, as well as the development of new analysis methods with the translational potential between fields.
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Affiliation(s)
- Ion Andronache
- Research Center for Integrated Analysis and Territorial Management, Faculty of Geography, University of Bucharest, Bucharest, Romania.
| | - Daniel Peptenatu
- Research Center for Integrated Analysis and Territorial Management, Faculty of Geography, University of Bucharest, Bucharest, Romania
| | - Helmut Ahammer
- GSRC, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria
| | - Marko Radulovic
- Department of Experimental Oncology, Institute of Oncology and Radiology of Serbia, Belgrade, Serbia
| | - Goran J Djuričić
- Department of Radiology, Faculty of Medicine, University of Belgrade, University Children's Hospital, Belgrade, Serbia
| | - Herbert F Jelinek
- Department of Medical Sciences and Biotechnology Center, Khalifa University, Abu Dhabi, UAE
| | - Carlo Russo
- Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, NSW, Australia
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Kolmogorov compression complexity may differentiate different schools of Orthodox iconography. Sci Rep 2022; 12:10743. [PMID: 35750777 PMCID: PMC9232591 DOI: 10.1038/s41598-022-12826-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 05/16/2022] [Indexed: 11/12/2022] Open
Abstract
The complexity in the styles of 1200 Byzantine icons painted between 13th and 16th from Greece, Russia and Romania was investigated through the Kolmogorov algorithmic information theory. The aim was to identify specific quantitative patterns which define the key characteristics of the three different painting schools. Our novel approach using the artificial surface images generated with Inverse FFT and the Midpoint Displacement (MD) algorithms, was validated by comparison of results with eight fractal and non-fractal indices. From the analyzes performed, normalized Kolmogorov compression complexity (KC) proved to be the best solution because it had the best complexity pattern differentiations, is not sensitive to the image size and the least affected by noise. We conclude that normalized KC methodology does offer capability to differentiate the icons within a School and amongst the three Schools.
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