Design and Implementation of Image Diagnosis Technique Using Matlab and Wavelet Transform

Image diagnosis has become a cornerstone of modern medical practice, where precision and clarity are vital for accurate decision-making. This study presents the design and implementation of an image diagnosis technique that integrates MATLAB and wavelet transform to enhance diagnostic image quality. The proposed method emphasizes preprocessing, wavelet-based decomposition, denoising through adaptive thresholding, and feature extraction. Quantitative evaluations using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) demonstrate superior performance in noise reduction and detail preservation across multiple imaging modalities including X-ray, MRI, CT, and ultrasound. Compared to traditional filtering methods, the technique shows significant improvements in visual quality and computational efficiency. These results highlight the method’s potential for integration into automated and real-time diagnostic systems. Future enhancements may involve the incorporation of machine learning for intelligent image analysis and decision support.