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Enhanced Spatial Fuzzy C-Means Algorithm for Brain Tissue Segmentation in T1 Images.

Bahram Jafrasteh1, Manuel Lubián-Gutiérrez2,3, Simón Pedro Lubián-López2,3

  • 1Biomedical Research and Innovation Institute of Cádiz (INiBICA) Research Unit, Puerta del Mar University Hospital, Cádiz, 11008, Spain. jafrasteh.bahram@inibica.es.

Neuroinformatics
|April 24, 2024
PubMed
Summary

We developed an Enhanced Spatial Fuzzy C-means (esFCM) algorithm for accurate brain tissue segmentation in 3D MRI scans. This method significantly improves segmentation accuracy, especially in noisy or unevenly lit images.

Keywords:
3D T1 MRI imagesBrain tissue segmentationEnhanced spatial fuzzy C-means (esFCM) algorithmMagnetic resonance imaging (MRI)Structural similarity index (SSIM)

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Area of Science:

  • Neurology
  • Medical Imaging
  • Computer Vision

Background:

  • Accurate brain tissue segmentation in Magnetic Resonance Imaging (MRI) is vital for diagnosing neurological disorders.
  • Existing segmentation methods face challenges with image noise and intensity variations.

Purpose of the Study:

  • To introduce and evaluate an Enhanced Spatial Fuzzy C-means (esFCM) algorithm for segmenting White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF) in 3D T1 MRI scans.
  • To improve the accuracy and robustness of brain MRI segmentation.

Main Methods:

  • The esFCM algorithm utilizes a weighted least square approach with the Structural Similarity Index (SSIM) for bias field correction.
  • It incorporates neighborhood information from the previous iteration's membership function to enhance adaptability.
  • Segmentation accuracy was compared against Fuzzy C-means variants, Gaussian Mixture Model (GMM), FSL, and ANTs using multiple datasets and metrics.

Main Results:

  • The esFCM algorithm demonstrated superior segmentation performance compared to other methods.
  • Performance improvements were particularly notable in MRI datasets with added noise and bias fields.
  • The method effectively handled intensity irregularities and complex image structures.

Conclusions:

  • The proposed esFCM algorithm offers enhanced accuracy and robustness for 3D brain MRI segmentation.
  • It presents a significant advancement for clinical applications requiring precise tissue segmentation.
  • esFCM shows considerable potential for improving the diagnosis of brain injuries and neurodegenerative diseases.