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<p>Brain Magnetic Resonance image diagnostics employs image processing, but aberrations such as Intensity Inhomogeneity (IIH) distort the image, making diagnosis difficult. Clinical diagnostic methods must address IIH discrepancies in brain MR scans, which occur often. Accurate brain MR image processing is difficult but required for clinical diagnosis. In this study, we introduced a more energy-efficient intensity inhomogeneity correction (IIC) method that makes use of the Modified Energy-based Generative Adversarial Network. This method uses reconstruction error in the discriminator architecture to save energy by altering the cost function. The generator’s performance is also improved by this reconstruction error. As the reconstruction error decreases, the discriminator collects latent information from real images to enhance output. To prevent mode collapse, the model has a drawing away term (PT). The generator design is improved by using skip connections and information modules that collect features at various scales. The suggested method beats state-of-the-art methods in metrics such as Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Multi-Scale Structural Similarity Index (MSSSIM), Mean Squared Error (MSE), and Root Mean Square Error (RMSE).</p>

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Modified energy-based GAN for intensity in homogeneity correction in brain MR images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月21日

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  • Yepuganti Karuna,
  • Neelam Syamala,
  • C. V. Ravikumar,
  • Pooja Thakur,
  • Saritha Saladi

摘要

Brain Magnetic Resonance image diagnostics employs image processing, but aberrations such as Intensity Inhomogeneity (IIH) distort the image, making diagnosis difficult. Clinical diagnostic methods must address IIH discrepancies in brain MR scans, which occur often. Accurate brain MR image processing is difficult but required for clinical diagnosis. In this study, we introduced a more energy-efficient intensity inhomogeneity correction (IIC) method that makes use of the Modified Energy-based Generative Adversarial Network. This method uses reconstruction error in the discriminator architecture to save energy by altering the cost function. The generator’s performance is also improved by this reconstruction error. As the reconstruction error decreases, the discriminator collects latent information from real images to enhance output. To prevent mode collapse, the model has a drawing away term (PT). The generator design is improved by using skip connections and information modules that collect features at various scales. The suggested method beats state-of-the-art methods in metrics such as Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Multi-Scale Structural Similarity Index (MSSSIM), Mean Squared Error (MSE), and Root Mean Square Error (RMSE).