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Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts.

Yanqiu Zeng1, Baocan Zhang1, Wei Zhao1

  • 1Chengyi University College, Jimei University, Xiamen, China.

Computational and Mathematical Methods in Medicine
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This study introduces a hybrid algorithm to remove Gaussian noise from magnetic resonance (MR) images. The novel method enhances image quality and reliability by combining morphological component analysis with advanced filtering techniques.

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Area of Science:

  • Medical Imaging
  • Signal Processing
  • Computational Science

Background:

  • Gaussian noise in magnetic resonance (MR) images degrades image quality and reliability.
  • Noise originates from random thermal motion of electronic components in imaging devices.

Purpose of the Study:

  • To develop a hybrid denoising algorithm for MR images.
  • To improve the quality and reliability of MR images affected by Gaussian noise.

Main Methods:

  • Decomposition of noisy MR images into cartoon, texture, and residual components using Morphological Component Analysis (MCA).
  • Denoising of each component using Wiener filter (cartoon), wavelet hard threshold (texture), and wavelet soft threshold (residual).
  • Reconstruction of the denoised MR image by combining the processed components.

Main Results:

  • The proposed hybrid algorithm significantly outperforms individual denoising methods.
  • Demonstrated improvements in Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR).

Conclusions:

  • The hybrid denoising approach effectively reduces Gaussian noise in MR images.
  • This method offers superior performance for MR image quality enhancement compared to existing techniques.