Compression of medical images has always been viewed with skepticism, since the Ioss of information involved is thought to affect diagnostic information. However, recent research indicates that some waveletbased 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 waveletbased 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 Iocations 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. Copyright 9 1998 by W.B. Saunders Company C OMPRESSION OF MEDICAL IMAGES forfast transmission is not accepted universally because of possibility of losing diagnostic information. Recently, however, it has been reported I that up to 30:1 compression is acceptable using waveletbased compression techniques, rather than JPEG compression, by which more than 10:1 compression is not considered acceptable by most radiologists.Wavelet coding 2,3 is a class of subband coding that allows selective coding of subbands significant to visual perception. The encoding process actually determines the distortion introduced by quantization. It is well known from rate distortion theory that rector quanfization yields opfimal distortion. However, implementafion of vector quantization specifically the encoding process is quite involved. On the other hand, the decoding process in vector quantization is relafively simple and can be achieved from look-up tables. Generally, a simple statisfical clustering technique is used for generating the code vectors in vector quanfization. 4,5 We have developed novel clustefing techniques 6,7 for on-line generation of code vectors 8,9 and applied them to quantizafion of wavelet-transformed subimages. The selection of the number of clusters generated is decided by the quantization level to be applied, ie, whether coarse or fine quantization is needed. The code vectors representing the clusters are generated here by an adapfive vector quantization technique, namely AFLC-VQ (adaptive fuzzy leader clustefing-vector quantization), lo AFLC-VQ is based on an integration of self-organizing neural networks with fuzzy membership values of data samples for...

We have developed and tested alternative transfortris, which create different zerotree structures. These new transforrris cati .rigt1ijcicantly ittiprove the pegortnatice of the compression algorithms that use zerotrees without a significant modification of the latter. A new wavelet pyramid that results in an effective non-integer up-and dowtisatriplitrg of the high frequency subbands is presented in this paper. Non-integer subsatripled pyramids cati also be used to decrease data dependency in the cotripression algorithtn, and therefore decrease the execution time in a processor with multiple parallel processitig units.

Most high resolution medical images such as X-ray radiographic images require enormous storage space and considerable time for transmission and viewing. We propose a wavelet design that creates the optimal filter taps for any class of images adaptively for high fidelity image reconstruction using an energy compacted section of the wavelet decomposed original image with considerable reduction in memory requirement as well as in execution, transmission, and viewing time. This optimal filter tap design is based on two-channel perfect reconstruction quadrature mirror filter (PR-QMF) banks using an interior-point-based optimization algorithm. The algorithm finds wavelet filter taps that allows the smallest amount of energy in the detail sections of the wavelet decomposition of an image in real time. Once the filter taps have been created and a one level wavelet transform has been performed, the energy compacted component of the image containing one fourth of the number of elements in the original image, is retained without any significant loss in the information content. This energy compacted section of the image is then used for any chosen advanced compression algorithm. This technique provides a significant reduction in execution time without an appreciable increase in distortion for advanced lossy image compression algorithms.

We have constructed and tested a novel particle sizer that employs particle counting (the time-domain approach) rather than the ensemble-diffraction approach that currently dominates the market in atmospheric-dust particle sizers. The method does not depend on mechanical devices to restrict particles within the sampling volume but instead allows for optical isolation of the sampling volume. This technique is useful for atmospheric-dust measurements for which nonobtrusive measurement is often desirable.

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