Efficient retrieval of digital medical images over the Internet for worldwide users in a platform independent manner is becoming essential in clinical research and education now-a-days. Due to the large size of most x-ray images, traditionally the images are subsampled for viewing in a smaller size and reduced quality. We are presenting a new and efficient way of encoding and decoding large X-ray images for Internet users without any appreciable reduction in image quality and no reduction in size by a high fidelity vector quantizer in the wavelet domain. As opposed to a 16:1 reduction in size by subsampling, this new technique decreases the file size to over 100:1 with insignificant loss and no reduction in size in the decoded image viewed by the web users anywhere. In addition to traditional vector quantization in the wavelet domain, a new scheme for generating the vectors from images will be used. In the proposed scheme, wavelet transform will be obtained by a lifting scheme and the vectors will be gen erated over a multiscale wavelet domain by relating wavelet coefficients from coarser to finer resolution. Such a multiresolution tree for scalar quantization is well known but it has never been used in vector quantization.
|Number of pages||7|
|Journal||Proceedings of the IEEE Symposium on Computer-Based Medical Systems|
|State||Published - 2001|
|Event||14th IEEE Symposium on Computer-Based Medical Systems - Bethesda, MD, United States|
Duration: Jul 26 2001 → Jul 27 2001