Reducing noise in medical imaging is essential to improve quality and diagnostic accuracy. Gaussian noise, common in MRI and X-ray images, reduces clarity and obscures pathological structures. Traditional denoising methods often fail to preserve edge details, leading to the loss of important anatomical information. To address this, an Adaptive Edge-Preserving Type-2 Fuzzy Filter is developed, aiming to reduce Gaussian noise while maintaining edge integrity. The proposed method integrates an adaptive mechanism into classical Type-2 Fuzzy Filtering, adjusting filtering parameters dynamically based on edge strength. This enables effective noise removal in both homogeneous and edge-rich regions. The performance of the methods was evaluated on real medical image datasets under varying noise levels and compared with Mean, Median, Fuzzy, and classical Type-2 Fuzzy Filtering methods. Results demonstrate superior performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), providing a robust solution for improving medical image quality.
Aynur Yonar (Mon,) studied this question.
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