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PubMed Publications| Keyword search (4,164 papers available) |  |
"Lahmiri S" Authored Publications:
| Title: | Fractals in Neuroimaging | ||||
| Authors: | Lahmiri S, Boukadoum M, Di Ieva A | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/38468046/ | ||||
| DOI: | 10.1007/978-3-031-47606-8_22 | ||||
| Publication: | Advances in neurobiology | ||||
| Keywords: | Classification; Computed tomography; Detrended fluctuation analysis; Fractal dimension; Hurst exponent; Magnetic resonance imaging; Neuroimaging; Statistical tests; | ||||
| PMID: | 38468046 | Category: | Date Added: | 2024-03-12 | |
| Dept Affiliation: |
JMSB
1 Department of Supply Chain & Business Technology Management, John Molson School of Business, Concordia University, Montreal, Canada. 2 RESMIQ, Labo microPro, Université du Québec à Montréal (UQAM), Montreal, Canada. 3 Computational NeuroSurgery (CNS) Lab & Macquarie Neurosurgery, Macquarie Medical School, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, NSW, Australia. antonio.diieva@mq.edu.au. |
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Description: |
Several natural phenomena can be described by studying their statistical scaling patterns, hence leading to simple geometrical interpretation. In this regard, fractal geometry is a powerful tool to describe the irregular or fragmented shape of natural features, using spatial or time-domain statistical scaling laws (power-law behavior) to characterize real-world physical systems. This chapter presents some works on the usefulness of fractal features, mainly the fractal dimension and the related Hurst exponent, in the characterization and identification of pathologies and radiological features in neuroimaging, mainly, magnetic resonance imaging. |
