A lossless compression method for multi-component medical images based on big data mining.

Scientific Reports
Gangtao Xin, Pingyi Fan

Abstract

In disease diagnosis, medical image plays an important part. Its lossless compression is pretty critical, which directly determines the requirement of local storage space and communication bandwidth of remote medical systems, so as to help the diagnosis and treatment of patients. There are two extraordinary properties related to medical images: lossless and similarity. How to take advantage of these two properties to reduce the information needed to represent an image is the key point of compression. In this paper, we employ the big data mining to set up the image codebook. That is, to find the basic components of images. We propose a soft compression algorithm for multi-component medical images, which can exactly reflect the fundamental structure of images. A general representation framework for image compression is also put forward and the results indicate that our developed soft compression algorithm can outperform the popular benchmarks PNG and JPEG2000 in terms of compression ratio.

References

Jul 3, 2007·Drug Safety : an International Journal of Medical Toxicology and Drug Experience·David J Hand
Feb 12, 2008·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·D Taubman
Feb 12, 2008·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·M J WeinbergerG Sapiro
Jun 30, 2012·Nature Methods·Vebjorn LjosaAnne E Carpenter
Jan 7, 2016·Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society·Collin PrestonBasar Koc
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Feb 25, 2017·Nature Communications·Atul BhardwajFrank Wuest
Jan 5, 2020·Nature Communications·Elin AlmstedtSven Nelander

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Software Mentioned

JPEG2000
HEVC

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