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Toward practical guideline for design of image compression algorithms for biomedical applications

机译:迈向生物医学应用图像压缩算法设计实用指南

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Improvements in medicine and healthcare are accelerating. Information generation, sharing, and expert analysis, play a great role in improving medical sciences. Big data produced by medical procedures in hospitals, laboratories, and research centers needs storage and transmission. Data compression is a critical tool that reduces the burden of storage and transmission. Medical images, in particular, require special consideration in terms of storage and transmissions. Unlike many other types of big data, medical images require lossless storage. Special purpose compression algorithms and codecs could compress variety of such images with superior performance compared to the general purpose lossless algorithms. For the medical images, many lossless algorithms have been proposed so far. A compression algorithm comprises of different stages. Before designing a special purpose compression method we need to know how much each stage contributes to the overall compression performance so we could accordingly invest time and effort in designing different stages. In order to compare and evaluate these multi stage compression techniques and to design more efficient compression methods for big data applications, in this paper the effectiveness of each of these compression stages on the total performance of the algorithm is analyzed. (C) 2016 Elsevier Ltd. All rights reserved.
机译:医学和保健方面的进步正在加速。信息的生成,共享和专家分析在改善医学科学方面发挥着重要作用。医院,实验室和研究中心的医疗程序产生的大数据需要存储和传输。数据压缩是减少存储和传输负担的关键工具。特别是医学图像,在存储和传输方面需要特别考虑。与许多其他类型的大数据不同,医学图像需要无损存储。与通用无损算法相比,专用压缩算法和编解码器可以以优异的性能压缩各种此类图像。迄今为止,对于医学图像,已经提出了许多无损算法。压缩算法包括不同的阶段。在设计专用压缩方法之前,我们需要知道每个阶段对整体压缩性能的贡献,因此我们可以相应地花费时间和精力来设计不同的阶段。为了比较和评估这些多级压缩技术并为大数据应用设计更有效的压缩方法,本文分析了这些压缩级中的每一个对算法总性能的有效性。 (C)2016 Elsevier Ltd.保留所有权利。

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