Enhanced Adaptive Threshold Median Filter for Medical Image Filtering

Abstract

In the field of medical image processing, mitigating the impact of noise is of paramount importance. Conventional median filters primarily target the elimination of medical image noise occurring as a single layer, characterized by a constant level of noise gray value. However, these filters encounter challenges when faced with images corrupted by noise that extends beyond a single layer. This study presents the Enhanced Adaptive Threshold Median Filter (EATMF) as a solution to address the aforementioned challenge. The proposed filter combines the Adaptive Median Filter (AMF) with thresholds (ATMF) and incorporates a Laplacian filter. By introducing changes in the thresholds, the EATMF achieves a balance between effectively removing both low and high density noise while preserving image quality. A comparative analysis between the EATMF and the ATMF is presented, accompanied by visual examples that showcase the performance of the newly introduced filter. The results demonstrate that the EATMF outperforms the ATMF in terms of Peak Signal-to- Noise Ratio (PSNR), indicating its superior noise reduction capabilities. This study highlights the significance of the EATMF in medical image processing, particularly in scenarios where images are corrupted by multi-layer noise. The proposed filter offers an enhanced approach to noise reduction, contributing to improved image quality and accuracy in medical diagnostics and analysis