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Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction

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Abstract

To address the low compression efficiency of lossless compression and the low image quality of general near-lossless compression, a novel near-lossless compression algorithm based on adaptive spatial prediction is proposed for medical sequence images for possible diagnostic use in this paper. The proposed method employs adaptive block size-based spatial prediction to predict blocks directly in the spatial domain and Lossless Hadamard Transform before quantization to improve the quality of reconstructed images. The block-based prediction breaks the pixel neighborhood constraint and takes full advantage of the local spatial correlations found in medical images. The adaptive block size guarantees a more rational division of images and the improved use of the local structure. The results indicate that the proposed algorithm can efficiently compress medical images and produces a better peak signal-to-noise ratio (PSNR) under the same pre-defined distortion than other near-lossless methods.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (grant nos. 61574102, 61404094, and 61204096), the Fundamental Research Fund for the Central Universities, Wuhan University (grant no 2042014kf0238), and the Hubei Province Science & Technology Pillar Program (grant no. 2015CFB536).

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Authors and Affiliations

  1. School of Physics and Technology, Wuhan University, Wuhan, Hubei, China, 430072

    Xiaoying Song, Qijun Huang, Sheng Chang, Jin He & Hao Wang

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  1. Xiaoying Song
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  2. Qijun Huang
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  3. Sheng Chang
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  4. Jin He
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  5. Hao Wang
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Correspondence to Qijun Huang.

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Song, X., Huang, Q., Chang, S. et al. Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction. J Digit Imaging 29, 706–715 (2016). https://doi.org/10.1007/s10278-016-9892-y

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  • DOI: https://doi.org/10.1007/s10278-016-9892-y

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