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28 December 1998 Virtually lossless compression of medical images through classified prediction and context-based arithmetic coding
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Proceedings Volume 3653, Visual Communications and Image Processing '99; (1998) https://doi.org/10.1117/12.334609
Event: Electronic Imaging '99, 1999, San Jose, CA, United States
Abstract
This paper proposes a method to achieve a virtually-lossless compression of medical images. An image is normalized to the standard deviation of its noise, which is adaptively estimated in an unsupervised fashion. The resulting bit map is encoded without any further loss. The compression algorithm is based on a classified linear-regression prediction followed by context-based arithmetic coding of the outcome residuals. Images are partitioned into blocks, e.g., 16 X 16, and a minimum mean square (MMSE) linear predictor is calculated for each block. Given a preset number of classes, a Fuzzy-C-Means algorithm produces an initial guess of classified predictors to be fed to an iterative procedure which classifies pixel blocks simultaneously refining the associated predictors. All the predictors are transmitted along with the label of each block. Coding time are affordable thanks to fast convergence of the iterative algorithms. Decoding is always performed in real time. The compression scheme provides impressive performances, especially when applied to X-ray images.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bruno Aiazzi, Luciano Alparone, Stefano Baronti, and Franco Lotti "Virtually lossless compression of medical images through classified prediction and context-based arithmetic coding", Proc. SPIE 3653, Visual Communications and Image Processing '99, (28 December 1998); https://doi.org/10.1117/12.334609
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