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Proceedings Paper

Visualization and volumetric compression
Author(s): Kelby K. Chan; Christina C. Lau; Keh-Shih Chuang; Craig A. Morioka
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Paper Abstract

We performed volume compression on CT and MR data sets, each consisting of 256 X 256 X 64 or 32 images, using three-dimensional (3D) DCT followed by quantization, adaptive bit-allocation, and Huffman encoding. Cuberille based surface rendering and oblique angle slicing was performed on the reconstructed compression data using a multi-stream vector processor. For CT images 3D-DCT was found to be successful in exploiting the additional degree of voxel correlations between image frames, resulting in compression efficiency greater than 2D-DCT of individual images. During rendering operations, a substantial amount of thresholding, resampling, and filtering operations are performed on the data. At compression ratios in the range 6 - 15:1, 3D compression was not found to have any adverse visual impact on rendered output. Of these two methods, oblique angle slicing, which involves the fewest operations was found to be the most demanding of small compression errors. We conclude that 3D-DCT compression is a viable technique for efficiently reducing the size of data volumes which must be analyzed with various rendering methods.

Paper Details

Date Published: 1 May 1991
PDF: 6 pages
Proc. SPIE 1444, Medical Imaging V: Image Capture, Formatting, and Display, (1 May 1991); doi: 10.1117/12.45176
Show Author Affiliations
Kelby K. Chan, UCLA School of Medicine (United States)
Christina C. Lau, UCLA School of Medicine (United States)
Keh-Shih Chuang, UCLA School of Medicine (United States)
Craig A. Morioka, UCLA School of Medicine (United States)

Published in SPIE Proceedings Vol. 1444:
Medical Imaging V: Image Capture, Formatting, and Display
Yongmin Kim, Editor(s)

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