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Context Dependent Fuzzy Associated Statistical Model for Intensity Inhomogeneity Correction From Magnetic Resonance

Badri Narayan Subudhi1, T Veerakumar2, S Esakkirajan3

  • 11Department of Electrical EngineeringIndian Institute of Technology JammuJammu181221India.

IEEE Journal of Translational Engineering in Health and Medicine
|July 9, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for correcting intensity inhomogeneity in magnetic resonance images (MRIs) by combining correction and segmentation. The novel approach utilizes a fuzzy set statistical model and variable neighborhood searching for improved accuracy.

Keywords:
Markov random fieldfuzzy clusteringintensity inhomogeneitymaximum a posteriori probability

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Computational Biology

Background:

  • Magnetic Resonance Imaging (MRI) is susceptible to intensity inhomogeneity, which can degrade image quality and affect subsequent analysis.
  • Existing methods for intensity inhomogeneity correction often struggle with local optima and fail to integrate segmentation effectively.

Purpose of the Study:

  • To propose a novel context-dependent fuzzy set associated statistical model for simultaneous intensity inhomogeneity correction and segmentation of MRI data.
  • To enhance the Maximum A Posteriori (MAP) estimation by incorporating a Variable Neighborhood Searching (VNS) global convergence criterion.

Main Methods:

  • A novel statistical model integrating fuzzy set theory and Markov Random Fields (MRF) to capture spatio-contextual information in MRIs.
  • Combined intensity inhomogeneity correction and image segmentation as a single task using MAP estimation.
  • Application of Variable Neighborhood Searching (VNS) for iterative global convergence in MRF-MAP estimation to overcome local optima.

Main Results:

  • The proposed technique effectively corrects intensity inhomogeneity in various MRI datasets.
  • Simultaneous correction and segmentation demonstrate superior performance compared to existing state-of-the-art methods.
  • The VNS-based approach successfully avoids local optima, leading to more robust and accurate results.

Conclusions:

  • The developed context-dependent fuzzy set associated statistical model offers an effective solution for MRI intensity inhomogeneity correction and segmentation.
  • The integration of VNS with MRF-MAP estimation significantly improves the reliability and accuracy of the proposed technique.
  • This novel approach holds promise for enhancing the quality and utility of MRI data in clinical and research applications.