Cargando…
Simultaneous encryption and compression of medical images based on optimized tensor compressed sensing with 3D Lorenz
BACKGROUND: The existing techniques for simultaneous encryption and compression of images refer lossy compression. Their reconstruction performances did not meet the accuracy of medical images because most of them have not been applicable to three-dimensional (3D) medical image volumes intrinsically...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5097394/ https://www.ncbi.nlm.nih.gov/pubmed/27814721 http://dx.doi.org/10.1186/s12938-016-0239-1 |
_version_ | 1782465592682348544 |
---|---|
author | Wang, Qingzhu Chen, Xiaoming Wei, Mengying Miao, Zhuang |
author_facet | Wang, Qingzhu Chen, Xiaoming Wei, Mengying Miao, Zhuang |
author_sort | Wang, Qingzhu |
collection | PubMed |
description | BACKGROUND: The existing techniques for simultaneous encryption and compression of images refer lossy compression. Their reconstruction performances did not meet the accuracy of medical images because most of them have not been applicable to three-dimensional (3D) medical image volumes intrinsically represented by tensors. METHODS: We propose a tensor-based algorithm using tensor compressive sensing (TCS) to address these issues. Alternating least squares is further used to optimize the TCS with measurement matrices encrypted by discrete 3D Lorenz. RESULTS: The proposed method preserves the intrinsic structure of tensor-based 3D images and achieves a better balance of compression ratio, decryption accuracy, and security. Furthermore, the characteristic of the tensor product can be used as additional keys to make unauthorized decryption harder. CONCLUSIONS: Numerical simulation results verify the validity and the reliability of this scheme. |
format | Online Article Text |
id | pubmed-5097394 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-50973942016-11-07 Simultaneous encryption and compression of medical images based on optimized tensor compressed sensing with 3D Lorenz Wang, Qingzhu Chen, Xiaoming Wei, Mengying Miao, Zhuang Biomed Eng Online Research BACKGROUND: The existing techniques for simultaneous encryption and compression of images refer lossy compression. Their reconstruction performances did not meet the accuracy of medical images because most of them have not been applicable to three-dimensional (3D) medical image volumes intrinsically represented by tensors. METHODS: We propose a tensor-based algorithm using tensor compressive sensing (TCS) to address these issues. Alternating least squares is further used to optimize the TCS with measurement matrices encrypted by discrete 3D Lorenz. RESULTS: The proposed method preserves the intrinsic structure of tensor-based 3D images and achieves a better balance of compression ratio, decryption accuracy, and security. Furthermore, the characteristic of the tensor product can be used as additional keys to make unauthorized decryption harder. CONCLUSIONS: Numerical simulation results verify the validity and the reliability of this scheme. BioMed Central 2016-11-04 /pmc/articles/PMC5097394/ /pubmed/27814721 http://dx.doi.org/10.1186/s12938-016-0239-1 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Wang, Qingzhu Chen, Xiaoming Wei, Mengying Miao, Zhuang Simultaneous encryption and compression of medical images based on optimized tensor compressed sensing with 3D Lorenz |
title | Simultaneous encryption and compression of medical images based on optimized tensor compressed sensing with 3D Lorenz |
title_full | Simultaneous encryption and compression of medical images based on optimized tensor compressed sensing with 3D Lorenz |
title_fullStr | Simultaneous encryption and compression of medical images based on optimized tensor compressed sensing with 3D Lorenz |
title_full_unstemmed | Simultaneous encryption and compression of medical images based on optimized tensor compressed sensing with 3D Lorenz |
title_short | Simultaneous encryption and compression of medical images based on optimized tensor compressed sensing with 3D Lorenz |
title_sort | simultaneous encryption and compression of medical images based on optimized tensor compressed sensing with 3d lorenz |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5097394/ https://www.ncbi.nlm.nih.gov/pubmed/27814721 http://dx.doi.org/10.1186/s12938-016-0239-1 |
work_keys_str_mv | AT wangqingzhu simultaneousencryptionandcompressionofmedicalimagesbasedonoptimizedtensorcompressedsensingwith3dlorenz AT chenxiaoming simultaneousencryptionandcompressionofmedicalimagesbasedonoptimizedtensorcompressedsensingwith3dlorenz AT weimengying simultaneousencryptionandcompressionofmedicalimagesbasedonoptimizedtensorcompressedsensingwith3dlorenz AT miaozhuang simultaneousencryptionandcompressionofmedicalimagesbasedonoptimizedtensorcompressedsensingwith3dlorenz |