Multivariate Statistical Modeling for Medical Image Compression Using Wavelet Transforms

With the promotion and application of digital imaging technology in the medical domain, the amount of medical image has grown rapidly. Recently variety of medical image compression methods using wavelet transforms have been proposed by many researchers. In this paper, we derive the general estimation rule in the wavelet domain to obtain the compression coefficients from the medical image based on the multivariate statistical theory. Multivariate model makes it possible to exploit the dependency between the estimated wavelet coefficients and their neighbours or other coefficients in different subbands. According to the multivariate model, a powerful compression scheme by means of wavelet transforms is presented. The experiments are done on the medical images including computed tomography (CT) and magnetic resonance imaging. The results show that under common objective conditions, our compression algorithm can achieve high subjective quality compressed image. Among the existing image compression methods using the same type of wavelet (Daubechies 8) filter, our results produce the highest peaksignal-to-noise ratio