Fractal Dimension of Silhouette Magnetic Resonance Brain Images as a Measure of Age-Associated Changes in Cerebral Hemispheres

Authors : Nataliia MARYENKO, Oleksandr STEPANENKO

Pages : 27-37

Doi:10.18678/dtfd.1180625

View : 17 | Download : 14

Publication Date : 2023-04-30

Article Type : Research Paper

Abstract :Aim: The aim of the present study was to characterize age-associated changes in the spatial configuration of cerebral hemispheres insert ignore into journalissuearticles values(including changes in spatial complexity and space-filling capacity); using fractal analysis of silhouette magnetic resonance brain images. Material and Methods: Magnetic resonance brain images of 100 insert ignore into journalissuearticles values(44 male, 56 female); participants aged between 18-86 years were studied. Five magnetic resonance images were selected from the magnetic resonance imaging dataset of each brain, including four tomographic sections in the coronal plane and one in the axial plane. Fractal dimension values of the cerebral hemispheres silhouettes were measured using the two-dimensional box-counting algorithm. Morphometric parameters based on Euclidean geometry insert ignore into journalissuearticles values(perimeter, area, and their derivative values); were determined as well. Results: The average fractal dimension value of the five studied tomographic sections was 1.878±0.0009, the average value of four coronal sections was 1.868±0.0010. It was shown that fractal dimension values of cerebral silhouettes for all studied tomographic sections and four coronal sections significantly decrease with age insert ignore into journalissuearticles values(r=-0.512, p<0.001 and r=-0.491, p<0.001, respectively);. The difference in the character of age-related changes in males and females was not statistically significant. Based on the age and the fractal dimension values of the studied sample, the confidence intervals of the fractal dimension values of cerebral hemispheres silhouettes were determined, which can be used as norm criteria in clinical neuroimaging. Conclusion: The fractal analysis and obtained data can be used in neuroimaging for assessing the degree of age-related cerebral atrophy and for differentiating between normal aging and neurodegenerative diseases.
Keywords : Fraktal, beyin, yaşlanma, atrofi, nörogörüntüleme