Enhancing Medical Image Reclamation for Chest Samples Using B-Coefficients, DT-CWT and EPS Algorithm

This paper introduces a novel approach for medical image reclamation, specifically focusing on enhancing chest image resolution. The proposed method introduces the Dual-Tree Complex Wavelet Transform (DT-CWT) with Edge Preservation Smoothing (EPS) filters to balance visual clarity. The resulting Image Reclamation system maintains high-quality results while preserving image edges. Performance validation using established metrics like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Root Mean Square Error (RMSE), and entropy demonstrates substantial improvements: PSNR of 31, SSIM of 0.99, RMSE of 8.25, and entropy of 1.03. Furthermore, the algorithm extracts features from the enhanced chest image through symlet transform, allowing for Bhattacharya coefficient computation and unique bin analysis to enhance image retrieval. Experimental results show efficiency gains, increasing the top 5 matching images’ retrieval score from 320 to 512. This approach promises to enhance medical image reclamation in emergency settings, facilitating quicker and more accurate diagnoses and treatments for acute chest injuries. Ultimately, this work can potentially save lives, reduce complications, and improve patient outcomes in chest trauma emergencies.