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Lossless medical image compression using geometry-adaptive partitioning and least square-based prediction

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Abstract

To improve the compression rates for lossless compression of medical images, an efficient algorithm, based on irregular segmentation and region-based prediction, is proposed in this paper. Considering that the first step of a region-based compression algorithm is segmentation, this paper proposes a hybrid method by combining geometry-adaptive partitioning and quadtree partitioning to achieve adaptive irregular segmentation for medical images. Then, least square (LS)-based predictors are adaptively designed for each region (regular subblock or irregular subregion). The proposed adaptive algorithm not only exploits spatial correlation between pixels but it utilizes local structure similarity, resulting in efficient compression performance. Experimental results show that the average compression performance of the proposed algorithm is 10.48, 4.86, 3.58, and 0.10% better than that of JPEG 2000, CALIC, EDP, and JPEG-LS, respectively.

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Funding

This work was supported by the National Natural Science Foundation of China (61404094, 61574102, 61471275, 61625305); the Natural Science Foundation of Hubei Province, China (2014CFB694); and the Fundamental Research Fund for the Central Universities, Wuhan University (2042015kf0174).

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Correspondence to Qijun Huang.

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Song, X., Huang, Q., Chang, S. et al. Lossless medical image compression using geometry-adaptive partitioning and least square-based prediction. Med Biol Eng Comput 56, 957–966 (2018). https://doi.org/10.1007/s11517-017-1741-8

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  • DOI: https://doi.org/10.1007/s11517-017-1741-8

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