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 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.
School of Information Engineering, Northeast Dianli University, Jilin, 132012, China. 150681573@qq.com.
Qingzhu
Q
Wang
School of Information Engineering, Northeast Dianli University, Jilin, 132012, China.
Xiaoming
X
Chen
School of Information Engineering, Northeast Dianli University, Jilin, 132012, China.
Mengying
M
Wei
Department of Neurosurgery, China-Japan Union Hospital of Jilin University, Changchun, China.
Zhuang
Z
Miao
D016494:Computer Security; D044962:Data Compression / Q000379:methods*; D021621:Imaging, Three-Dimensional; D008168:Lung / Q000000981:diagnostic imaging; D013997:Time Factors; D014057:Tomography, X-Ray Computed
3D Lorenz; Encryption and compression; Hither order singular value decomposition; Medical images