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Fractals in Neuroimaging.

Salim Lahmiri1, Mounir Boukadoum2, Antonio Di Ieva3

  • 1Department of Supply Chain & Business Technology Management, John Molson School of Business, Concordia University, Montreal, Canada.

Advances in Neurobiology
|March 12, 2024
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Summary

Fractal geometry, using fractal dimension and Hurst exponent, helps identify brain pathologies in magnetic resonance imaging by analyzing statistical scaling patterns of natural phenomena.

Keywords:
ClassificationComputed tomographyDetrended fluctuation analysisFractal dimensionHurst exponentMagnetic resonance imagingNeuroimagingStatistical tests

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Area of Science:

  • Medical Imaging
  • Fractal Geometry
  • Statistical Analysis

Background:

  • Natural phenomena exhibit statistical scaling patterns, interpretable through fractal geometry.
  • Fractal geometry characterizes irregular shapes using power-law behavior in spatial or time domains.
  • Fractal features like fractal dimension and Hurst exponent are valuable for analyzing complex systems.

Purpose of the Study:

  • To present works demonstrating the utility of fractal features in neuroimaging.
  • To highlight the application of fractal dimension and Hurst exponent in characterizing pathologies.
  • To showcase the identification of radiological features using fractal analysis in magnetic resonance imaging.

Main Methods:

  • Utilizing fractal geometry principles to analyze neuroimaging data.
  • Calculating fractal dimension to quantify image complexity.
  • Applying the Hurst exponent to assess statistical scaling properties in magnetic resonance imaging (MRI).

Main Results:

  • Fractal features effectively characterize irregular structures in neuroimaging.
  • Fractal dimension and Hurst exponent aid in distinguishing between normal and pathological tissues.
  • These fractal metrics show promise in identifying specific radiological features in the brain.

Conclusions:

  • Fractal geometry offers a powerful framework for analyzing neuroimaging data.
  • Fractal dimension and Hurst exponent are valuable quantitative tools for neuroimaging pathology identification.
  • The application of fractal analysis in MRI can enhance the characterization of neurological conditions.