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 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 die 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.
Medical image compression lossless image compression Wavelet transforms.
Yuehua Wan Shiming Ji Qiaoling Yuan Yi Xie
The MOE Key Laboratory of Mechanical Manufacture and Automation Zhejiang University of Technology Ha Institute of computer and information engineering Zhejiang Gongshang University Hangzhou, 310035, Ch
国际会议
Firth IEEE International Conference on Cognitive Informatics(第五届认知信息国际会议)
北京
英文
214-217
2006-07-17（万方平台首次上网日期，不代表论文的发表时间）