Biomedical Image Security Using Matrix Manipulation and DNA Encryption

2019 ◽  
pp. 49-60 ◽  
Author(s):  
Mousomi Roy ◽  
Shouvik Chakraborty ◽  
Kalyani Mali ◽  
Arghasree Banerjee ◽  
Kushankur Ghosh ◽  
...  
Keyword(s):  
Image Security ◽  
Biomedical Image ◽  
Matrix Manipulation

Implementation Color-Images Cryptography Using RSA Algorithm

2017 ◽  
Vol 7 (11) ◽  
pp. 181
Author(s):  
Ali Saleh Al Najjar
Keyword(s):  
Information Security ◽  
Image Encryption ◽  
Color Images ◽  
Public Key ◽  
Open Channels ◽  
Image Security ◽  
Rsa Algorithm ◽  
The Media ◽  
Encrypted Images ◽  
Difficult Issue

Absolute protection is a difficult issue to maintain the confidentiality of images through their transmission over open channels such as internet or networks and is a major concern in the media, so image Cryptography becomes an area of attraction and interest of research in the field of information security. The paper will offer proposed system that provides a special kinds of image Encryption image security, Cryptography using RSA algorithm for encrypted images by HEX function to extract HEX Code and using RSA public key algorithm, to generate cipher image text. This approach provides high security and it will be suitable for secured transmission of images over the networks or Internet.

INTRODUCING TENSOR ANALYSIS FOR BIOMEDICAL IMAGE REGISTRATION

2018 ◽  
Author(s):  
Wilian Fiirst ◽  
José Montero ◽  
ROGER RESMINI ◽  
Anselmo Antunes Montenegro ◽  
Trueman McHenry ◽  
...  
Keyword(s):  
Image Registration ◽  
Tensor Analysis ◽  
Biomedical Image ◽  
Biomedical Image Registration

scholarly journals Biometric Based on Steganography Image Security in Wireless Sensor Networks

2020 ◽  
Vol 167 ◽  
pp. 1291-1299
Author(s):  
P. Karthika ◽  
R. Ganesh Babu ◽  
K. Jayaram
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Image Security

scholarly journals Hyperchaotic Image Encryption Based on Multiple Bit Permutation and Diffusion

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 510
Author(s):  
Taiyong Li ◽  
Duzhong Zhang
Keyword(s):  
Big Data ◽  
Lyapunov Exponents ◽  
Image Encryption ◽  
Hyperchaotic System ◽  
Encryption Scheme ◽  
Image Content ◽  
Image Security ◽  
Level Data ◽  
Different Types ◽  
And Diffusion

Image security is a hot topic in the era of Internet and big data. Hyperchaotic image encryption, which can effectively prevent unauthorized users from accessing image content, has become more and more popular in the community of image security. In general, such approaches conduct encryption on pixel-level, bit-level, DNA-level data or their combinations, lacking diversity of processed data levels and limiting security. This paper proposes a novel hyperchaotic image encryption scheme via multiple bit permutation and diffusion, namely MBPD, to cope with this issue. Specifically, a four-dimensional hyperchaotic system with three positive Lyapunov exponents is firstly proposed. Second, a hyperchaotic sequence is generated from the proposed hyperchaotic system for consequent encryption operations. Third, multiple bit permutation and diffusion (permutation and/or diffusion can be conducted with 1–8 or more bits) determined by the hyperchaotic sequence is designed. Finally, the proposed MBPD is applied to image encryption. We conduct extensive experiments on a couple of public test images to validate the proposed MBPD. The results verify that the MBPD can effectively resist different types of attacks and has better performance than the compared popular encryption methods.

Non-rigid registration of biomedical image for radiotherapy based on adaptive feature density flow

2021 ◽  
Vol 68 ◽  
pp. 102691
Author(s):  
Jinghua Xu ◽  
Mingzhe Tao ◽  
Shuyou Zhang ◽  
Xue Jiang ◽  
Jianrong Tan
Keyword(s):  
Rigid Registration ◽  
Biomedical Image ◽  
Density Flow

scholarly journals Unsupervised content-preserving transformation for optical microscopy

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Xinyang Li ◽  
Guoxun Zhang ◽  
Hui Qiao ◽  
Feng Bao ◽  
Yue Deng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optical Microscopy ◽  
Training Data ◽  
Fluorescence Labeling ◽  
Imaging Data ◽  
Image Transformation ◽  
General Applicability ◽  
Data Annotation ◽  
Biomedical Image ◽  
Wide Range

AbstractThe development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current implementations of deep learning usually operate in a supervised manner, and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general applicability. Here, we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy, even in some cases in which supervised models cannot be applied. Through the introduction of a saliency constraint, the unsupervised model, named Unsupervised content-preserving Transformation for Optical Microscopy (UTOM), can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image content. UTOM shows promising performance in a wide range of biomedical image transformation tasks, including in silico histological staining, fluorescence image restoration, and virtual fluorescence labeling. Quantitative evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and modalities. We anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

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