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Proceedings Paper

Semiadaptive vector quantization and its application in medical image compression
Author(s): Jian-Hong Hu; Yao Wang; Patrick Cahill
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Paper Abstract

In this paper, we introduce a semi-adaptive vector quantization (SAVQ) method, which is a combination of the traditional VQ scheme using a fixed code book and the locally adaptive VQ (LAVQ) method which dynamically constructs a code book according to the input data stream. The code book in SAVQ consists of two parts: a fixed part that is designed based on certain training signals as in VQ, and an adaptive part that it updated based on the input vectors to be compressed. The proposed method is more effective than VQ and LAVQ for semi-stationary signals that have patterns common over different images as well as features specific to a particular image. Such is the case with medical images, which have similar tissue characteristics over different images, as well as with local variations that are patient and pathology dependent. The SAVQ as well as VQ and LAVQ methods have been applied to multispectral magnetic resonance brain images. The SAVQ has achieved higher compression ratios than the VQ and LAVQ methods over a wide range of reproduction quality, with more significant improvement in the mid to high quality range. Furthermore, under the same quality criterion, SAVQ requires a much smaller code book than VQ, making the former less time and memory demanding. Readings by neuroradiologists have suggested that images produced by SAVQ at compression ratios up to 40 (for MRI data with 3 or 4 images/set, 256 X 256 pixels/image, and 16 bits/pixel) are acceptable for primary reading.

Paper Details

Date Published: 22 October 1993
PDF: 12 pages
Proc. SPIE 2094, Visual Communications and Image Processing '93, (22 October 1993); doi: 10.1117/12.158007
Show Author Affiliations
Jian-Hong Hu, Polytechnic Univ. (United States)
Yao Wang, Polytechnic Univ. (United States)
Patrick Cahill, Polytechnic Univ. and Cornell Univ. Medical College (United States)


Published in SPIE Proceedings Vol. 2094:
Visual Communications and Image Processing '93
Barry G. Haskell; Hsueh-Ming Hang, Editor(s)

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