MRI Brain Tumor Image Enhancement Using LMMSE and Segmentation via Fast C-Means

Brain MRI imaging revolutionizes tumor diagnosis, yet noise frequently obscures the images, complicating precise tumor identification and segmentation. This paper presents a comprehensive pipeline for brain MRI enhancement and tumor segmentation. The proposed method integrates Wavelet Packet Transform (WPT) and Linear Minimum Mean Square Error (LMMSE) filtering for effective noise reduction, combined with morphological operations for contrast enhancement. For segmentation, Fast C-Means clustering is employed, with the number of clusters automatically determined from histogram peaks. The tumor cluster is selected based on the highest centroid intensity and further refined by morphological operations to accurately delineate tumor borders. The approach is evaluated on the BraTS 2021 dataset, subject to Rician, Gaussian, and salt-and-pepper noise with intensities from 6% to 14%. Results demonstrate superior noise suppression compared to Denoising Convolutional Neural Networks (DnCNN) and Non-Local Means (NLM), maintaining structural integrity with a Structural Similarity Index (SSIM) of 0.43 for Rician noise at σ = 6%. Segmentation performance remains stable, achieving Dice coefficients above 0.70, precision over 90%, and sensitivity between 75% to 81%, despite challenges posed by higher levels of salt-and-pepper noise. Tumor characteristics such as position and size correspond closely to ground truth, validating the effectiveness of the system in automating tumor delineation and providing reliable diagnostic assistance in neuro-oncology.

@article{Nguyen2025,
  title     = {MRI Brain Tumor Image Enhancement Using LMMSE and Segmentation via Fast C-Means},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Ngan V. T. Nguyen and Tuan V. Huynh and Liet V. Dang},
  doi       = {10.14569/IJACSA.2025.0160581},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160581}
}