Lossless wavelet compression on medical image

Abstract

An increasing number of medical imagery is created directly in digital form. Such as Clinical image Archiving and Communication Systems (PACS), as well as telemedicine networks require the storage and transmission of this huge amount of medical image data. Efficient compression of these data is crucial. Several lossless and lossy techniques for the compression of the data have been proposed. Lossless techniques allow exact reconstruction of the original imagery, while lossy techniques aim to achieve high compression ratios by allowing some acceptable degradation in the image. Lossless compression does not degrade the image, thus facilitating accurate diagnosis, of course at the expense of higher bit rates, i.e. lower compression ratios. Various methods both for lossy (irreversible) and lossless (reversible) image compression are proposed in the literature. The recent advances in the lossy compression techniques include different methods such as vector quantization. Wavelet coding, neural networks, and fractal coding. Although these methods can achieve high compression ratios (of the order 50:1, or even more), they do not allow reconstructing exactly the original version of the input data. Lossless compression techniques permit the perfect reconstruction of the original image, but the achievable compression ratios are only of the order 2:1, up to 4:1. In our paper, we use a kind of lifting scheme to generate truly loss-less non-linear integer-to-integer wavelet transforms. At the same time, we exploit the coding algorithm producing an embedded code has the property that the bits in the bit stream are generated in order of importance, so that all the low rate codes are included at the beginning of the bit stream. Typically, the encoding process stops when the target bit rate is met. Similarly, the decoder can interrupt the decoding process at any point in the bit stream, and still reconstruct the image. Therefore, a compression scheme generating an embedded code can start sending over the network the coarser version of the image first, and continues with the progressive transmission of the refinement details. Experimental results show that our method can get a perfect performance in compression ratio and reconstructive image.