Enhancement of Discrete Wavelet Transform Algorithm Applied in Medical Image Compression

Abstract

The Discrete Wavelet Transform (DWT) is a widely adopted technique in medical image compression for its ability to capture spatial and frequency characteristics. Clinical diagnosis may be impacted by distortions and the loss of important details caused by DWT's inability to preserve phase information, which is crucial for keeping the alignment of edges and textures in images. By incorporating a trained Autoencoder to learn and preserve essential image features for improved reconstruction, this limitation is addressed effectively. The Autoencoder comprises an encoder with convolutional layers using non-separable filters to enforce orthogonality, and a decoder with trans-posed convolutions for image reconstruction. JPEG2000 was employed as the compression technique, with the proposed method achieving a similar compression ratio to traditional DWT, indicating no compromise in efficiency. The results show that the enhanced DWT with autoencoder significantly out-performs the traditional DWT method, achieving up to 61.90% improvement in Peak Signal-to-Noise Ratio (PSNR), thereby reducing distortions and preserving critical image details more effectively. This improvement is crucial for maintaining the integrity and diagnostic quality of medical images, ensuring that essential features are accurately represented.