Medical ultrasound image compression using contextual vector quantization

With ever increasing use of medical ultrasound (US) images, a challenge exists to deal with storage and transmission of these images while still maintaining high diagnostic quality. In this article, a state-of-the-art context based method is proposed to overcome this challenge called contextual vector quantization (CVQ). In this method, a contextual region is defined as a region containing the most important information and must be encoded without considerable quality loss. Attempts are made to encode this region with high priority and high resolution (low compression ratio and high bit rate) CVQ algorithm; and the background, which has a lower priority, is separately encoded with a low resolution (high compression ratio and low bit rate) version of the CVQ algorithm. Finally both of the encoded contextual region and the encoded background region is merged together to reconstruct the output image. As a result, very good diagnostic image quality with lower image size and enhanced performance parameters including mean square error (MSE), pick signal to noise ratio (PSNR) and coefficient of correlation (CoC) are gained. The experimental results show that the proposed CVQ methodology is superior as compared to other existing methods (general methods such as JPEG and JPEG2K, and ROI based methods such as EBCOT and CSPIHT) in terms of measured performance parameters. This makes CVQ compression method a feasible technique to overcome storage and transmission limitations.