Mitra S, Yang S, Kustov V
Department of Electrical Engineering, Texas Tech University, Lubbock 79409-3102, USA.
J Digit Imaging. 1998 Nov;11(4 Suppl 2):24-30. doi: 10.1007/BF03168174.
Compression of medical images has always been viewed with skepticism, since the loss of information involved is thought to affect diagnostic information. However, recent research indicates that some wavelet-based compression techniques may not effectively reduce the image quality, even when subjected to compression ratios up to 30:1. The performance of a recently designed wavelet-based adaptive vector quantization is compared with a well-known wavelet-based scalar quantization technique to demonstrate the superiority of the former technique at compression ratios higher than 30:1. The use of higher compression with high fidelity of the reconstructed images allows fast transmission of images over the Internet for prompt inspection by radiologists at remote locations in an emergency situation, while higher quality images follow in a progressive manner if desired. Such fast and progressive transmission can also be used for downloading large data sets such as the Visible Human at a quality desired by the users for research or education. This new adaptive vector quantization uses a neural networks-based clustering technique for efficient quantization of the wavelet-decomposed subimages, yielding minimal distortion in the reconstructed images undergoing high compression. Results of compression up to 100:1 are shown for 24-bit color and 8-bit monochrome medical images.
医学图像压缩一直受到质疑，因为人们认为其中涉及的信息丢失会影响诊断信息。然而，最近的研究表明，一些基于小波的压缩技术可能无法有效降低图像质量，即使在压缩比高达30:1的情况下也是如此。将一种新设计的基于小波的自适应矢量量化的性能与一种著名的基于小波的标量量化技术进行比较，以证明前一种技术在压缩比高于30:1时的优越性。在重建图像具有高保真度的情况下使用更高的压缩比，能够在紧急情况下通过互联网快速传输图像，以便远程地点的放射科医生进行即时检查，同时如果需要，更高质量的图像可以逐步传输。这种快速和渐进式传输还可用于以用户期望的质量下载大型数据集，如可视人数据集，用于研究或教育。这种新的自适应矢量量化使用基于神经网络的聚类技术对小波分解后的子图像进行有效量化，在进行高压缩的重建图像中产生最小的失真。文中展示了24位彩色和8位单色医学图像高达100:1的压缩结果。
