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Proceedings Paper

Medical image compression with structure-preserving adaptive quantization
Author(s): Chang Wen Chen; Ya-Qin Zhang; Jiebo Luo; Kevin J. Parker
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Paper Abstract

We present in this paper a study of medical image compression based on an adaptive quantization scheme capable of preserving clinically useful structures appeared in the given images. We believe that how accurate can a compression algorithm preserve these structures is a good measure of image quality after compression since many image-based diagnoses are based on the position and appearance of certain structures. With wavelet decomposition, we are able to investigate the image features at different scale levels that correspond to certain characteristics of biomedical structures contained in the medical images. An adaptive quantization algorithm based on clustering with spatial constraints is then applied to the high frequency subbands. The adaptive quantization enables us to selectively preserve the image features at various scales so that desired details of clinically useful structure are preserved during the process of compression, even at a low bit rate. Preliminary results based on real medical images suggest that this clustering-based adaptive quantization, combined with wavelet decomposition, is very promising for medical image compression with structure-preserving capability.

Paper Details

Date Published: 21 April 1995
PDF: 12 pages
Proc. SPIE 2501, Visual Communications and Image Processing '95, (21 April 1995); doi: 10.1117/12.206631
Show Author Affiliations
Chang Wen Chen, Univ. of Rochester (United States)
Ya-Qin Zhang, David Sarnoff Research Ctr. (United States)
Jiebo Luo, Univ. of Rochester (United States)
Kevin J. Parker, Univ. of Rochester (United States)

Published in SPIE Proceedings Vol. 2501:
Visual Communications and Image Processing '95
Lance T. Wu, Editor(s)

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