Compression of ultrasound images using wavelet based space- frequency partitions

This paper describes the compression of grayscale medical ultrasound images using a new compression technique, space-frequency segmentation. This method finds the rate-distortion optimal representation of an image from a large set of possible space-frequency partitions and quantizer combinations. The method is especially effective when the images to code are statistically inhomogeneous, which is the case for medical ultrasound images. We implemented a real compression algorithm based on this method, and applied the resulting algorithm to representative ultrasound images. The result is an effective technique that performs significantly better than a current leading wavelet transform coding algorithm, Set Partitioning In Hierarchical Trees (SPIHT), using the standard objective PSNR distortion measure. The performance of our space-frequency codec is illustrated, and the space-frequency partitions described. To obtain a qualitative measure of our method’s performance, we describe an expert viewer study, where images compressed using both space-frequency compression and SPIHT were presented to ultrasound radiologists to obtain expert viewer assessment of the differences in quality between images from the two different methods. The expert viewer study showed the improved quality of space-frequency compressed images compared to SPIHT compressed images.