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

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...

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Autores principales:	Gangtao Xin, Pingyi Fan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/43858440c508469ba4db39daa515613a
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spelling	oai:doaj.org-article:43858440c508469ba4db39daa515613a2021-12-02T17:30:54ZA lossless compression method for multi-component medical images based on big data mining10.1038/s41598-021-91920-x2045-2322https://doaj.org/article/43858440c508469ba4db39daa515613a2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91920-xhttps://doaj.org/toc/2045-2322Abstract 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.Gangtao XinPingyi FanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gangtao Xin Pingyi Fan A lossless compression method for multi-component medical images based on big data mining
description	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.
format	article
author	Gangtao Xin Pingyi Fan
author_facet	Gangtao Xin Pingyi Fan
author_sort	Gangtao Xin
title	A lossless compression method for multi-component medical images based on big data mining
title_short	A lossless compression method for multi-component medical images based on big data mining
title_full	A lossless compression method for multi-component medical images based on big data mining
title_fullStr	A lossless compression method for multi-component medical images based on big data mining
title_full_unstemmed	A lossless compression method for multi-component medical images based on big data mining
title_sort	lossless compression method for multi-component medical images based on big data mining
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/43858440c508469ba4db39daa515613a
work_keys_str_mv	AT gangtaoxin alosslesscompressionmethodformulticomponentmedicalimagesbasedonbigdatamining AT pingyifan alosslesscompressionmethodformulticomponentmedicalimagesbasedonbigdatamining AT gangtaoxin losslesscompressionmethodformulticomponentmedicalimagesbasedonbigdatamining AT pingyifan losslesscompressionmethodformulticomponentmedicalimagesbasedonbigdatamining
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A lossless compression method for multi-component medical images based on big data mining

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