Fractal analysis in the quantification of medical imaging associated with multiple sclerosis pathology

Maria-Alexandra Paun1,2, Mihai-Virgil Nichita3, Vladimir-Alexandru Paun4

  • 1Department of Engineering, Swiss Federal Institute of Technology (EPFL), 1015 Lausanne, Vaud, Switzerland.

Abstract

Insights

Fractal analysis of brain MRI and histology images quantifies demyelination in multiple sclerosis (MS). This method offers objective measures for detecting neural disabilities and disease progression in the central nervous system (CNS).

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Biophysics

Background:

  • Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) causing demyelination and neurodegeneration.
  • Inflammation in MS involves glial cells, including microglia and infiltrating macrophages.
  • Neurodegeneration in MS correlates with disease progression and disability.

Purpose of the Study:

  • To apply fractal analysis to quantify fractal dominance in the nervous system hierarchy.
  • To investigate the relationship between self-organized criticality and self-similarity in MS.
  • To measure fractal dimension and lacunarity in brain MRI and histological images of MS lesions.

Main Methods:

  • Fractal analysis was applied to magnetic resonance imaging (MRI) and histological images.
  • Calculated fractal dimension and lacunarity for regions of interest in the brain.
  • Focused on areas with microglial activation and peripheral macrophage infiltration.

Main Results:

  • Histopathological samples of glial cells with erosions showed fractal dimension > 1.89 and lacunarity between 0.050-0.079.
  • Brain MRI images displayed fractal dimension > 1.7 and lacunarity between 0.0286-0.0393.
  • These quantitative measures objectively reflect the demyelinating process in MS.

Conclusions:

  • Fractal analysis provides objective quantitative measures of demyelination in MS.
  • This approach aids in detecting neural disabilities, particularly cortical onset in MS.
  • The findings support the utility of fractal geometry in understanding MS pathology and diagnosis.

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