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Proceedings Paper

Reversible compression of medical images with adaptive context selection
Author(s): Keshi Chen; Tenkasi V. Ramabadran
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Paper Abstract

An improved version of an efficient method for the reversible compression of digitized medical images is described. The modifications made in this version are aimed at reducing some of the limitations imposed by the original method. As in the original method, the improved method uses the well-known linear prediction technique to decorrelate a given image. A statistical source model with multiple contexts is employed to model the sequence of decorrelated image pixels. The selection of contexts for the source model is based on the horizontal and vertical components of the gradient in the given image as well as the predicted gray-level value of a pixel. The selection procedure is however entirely adaptive in the improved method, whereas it is only partially adaptive in the original method. The source model statistics are also calculated adaptively. The decorrelated image pixels are encoded using the appropriate contextual statistics with the arithmetic coding technique. Experiments on three groups of medical images show that the improved method achieves satisfactory compression performance.

Paper Details

Date Published: 30 June 1993
PDF: 8 pages
Proc. SPIE 1897, Medical Imaging 1993: Image Capture, Formatting, and Display, (30 June 1993); doi: 10.1117/12.147000
Show Author Affiliations
Keshi Chen, Iowa State Univ. (United States)
Tenkasi V. Ramabadran, Iowa State Univ. (United States)


Published in SPIE Proceedings Vol. 1897:
Medical Imaging 1993: Image Capture, Formatting, and Display
Yongmin Kim, Editor(s)

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