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Automatic brain MR image denoising based on texture feature-based artificial neural networks.

Yu-Ning Chang1, Herng-Hua Chang1

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Image Processing

Background:

  • Noise significantly degrades brain magnetic resonance (MR) image quality, impacting analysis tasks like segmentation and classification.
  • Manual parameter tuning for existing denoising algorithms is time-consuming and sensitive to image-specific features.
  • Automating denoising parameter selection is crucial for efficient and reliable MR image analysis.

Purpose of the Study:

  • To develop an automated system for denoising brain MR images by predicting optimal filtering parameters.
  • To leverage artificial intelligence and texture analysis for a robust and efficient denoising solution.

Main Methods:

  • Extracted 83 image attributes from four categories: basic statistics, Gray-Level Co-occurrence Matrix (GLCM), Gray-Level Run-Length Matrix (GLRLM), and Tamura texture features.
  • Utilized paired-samples t-test for feature discrimination ranking and Sequential Forward Selection (SFS) for optimal feature selection.
  • Integrated selected features into a backpropagation neural network to create a predictive parameter model for automated denoising.

Main Results:

  • The proposed AI framework accurately predicted bilateral filtering parameters for MR image denoising.
  • The automated system effectively removed noise from various brain MR images.
  • The AI-driven approach outperformed manual parameter tuning, yielding superior denoised results and significant time savings.

Conclusions:

  • The developed artificial neural network-based system successfully automates brain MR image denoising.
  • This approach offers an efficient and effective solution for improving MR image quality in clinical and research settings.
  • Automated parameter prediction enhances the reliability and speed of MR image analysis workflows.