Progressive transmission of high-fidelity radiographic images at very low bit rates

Shuyu Yang, Sunanda Mitra

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


Compression of medical images has always been viewed with skepticism since the loss of information involved is thought to affect diagnostic information. Recent reports, however, indicate that some wavelet based compression techniques may not effectively reduce the image quality even when subjected to compression ratios (CRs) up to 30:1. Although generation of minimum distortion at a specific bit rate by vector quantization (VQ) has been theoretically proven from rate distortion theory almost half a century ago, practical implementation of VQ for small sizes and classes of images has been accomplished relatively recently. Many of the earlier algorithms using simple statistical clustering suffer from a number of problems namely lack of convergence, getting trapped in local minima, and inability to handle large datasets. More advanced vector quantization algorithms have eliminated some of the above problems. However, vector quantization of large data sets as encountered in many medical images still remains a challenging problem. We present here an adaptive vector quantization technique including an entropy coding module that is capable of encoding large size radiographic as well as color images with minimum distortion in the decoded images even at CRs above 100:1.

Original languageEnglish
Pages (from-to)1607-1616
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
Issue numberII
StatePublished - 1999
EventProceedings of the 1999 Medical Imaging - Image Processing - San Diego, CA, USA
Duration: Feb 22 1999Feb 25 1999


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