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Segmentation and clustering in brain MRI imaging.

Golrokh Mirzaei1, Hojjat Adeli2

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This review covers clustering techniques in brain magnetic resonance imaging (MRI) for disease detection and analysis. It highlights clustering

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Computational Neuroscience

Background:

  • Clustering is crucial for magnetic resonance imaging (MRI) brain analysis.
  • It supports reliable detection and diagnosis of brain diseases.
  • Applications include segmentation and atrophy analysis.

Purpose of the Study:

  • To provide a state-of-the-art review of clustering techniques in brain MRI studies.
  • To summarize the diverse applications of clustering in neuroimaging.
  • To establish a foundation for future research in this domain.

Main Methods:

  • Literature review of scientific publications.
  • Analysis of studies employing clustering algorithms on brain MRI data.
  • Categorization of clustering applications in neuroimaging tasks.

Main Results:

  • Clustering is widely applied in brain MRI for segmentation of tissues like grey matter, white matter, and cerebrospinal fluid.
  • Clustering techniques are instrumental in identifying and quantifying brain atrophy.
  • The review synthesizes current methodologies and their impact on clinical applications.

Conclusions:

  • Clustering is an indispensable tool in brain MRI analysis.
  • Its application enhances the accuracy of disease detection, diagnosis, and treatment monitoring.
  • Further research into advanced clustering methods promises improved neuroimaging insights.