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30 June 1993 Reversible compression of medical images with adaptive context selection
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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.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Keshi Chen and Tenkasi V. Ramabadran "Reversible compression of medical images with adaptive context selection", Proc. SPIE 1897, Medical Imaging 1993: Image Capture, Formatting, and Display, (30 June 1993); https://doi.org/10.1117/12.147000
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