Efficient Web-based retrieval of radiographic images using a new multi-scale tree vector quantization in the wavelet domain

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 nowadays. Due to the large size of most X-ray images, traditionally the images are sub-sampled for viewing at a smaller size and with reduced quality. We present 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 sub-sampling, 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 Web users anywhere. In addition to traditional vector quantization in the wavelet domain, a new scheme for generating the vectors from images is used. In the proposed scheme, a wavelet transform is obtained by a lifting scheme, and the vectors are generated over a multi-scale wavelet domain by relating wavelet coefficients from coarser to finer resolution. Such a multi-resoultion tree for scalar quantization is well-known but it has never been used in vector quantization.

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