scholarly journals Novel Medical Image Cryptogram Technology Based on Segmentation and DNA Encoding

2021 ◽  
Author(s):  
Hongwei Xie ◽  
Yuzhou Zhang ◽  
Hao Zhang ◽  
Zhenyu Li
Keyword(s):  
Image Segmentation ◽  
Image Encryption ◽  
Medical Image ◽  
Deoxyribonucleic Acid ◽  
Security Analysis ◽  
Hyperchaotic System ◽  
Segmentation Method ◽  
Dna Encoding ◽  
Fuzzy C Means Clustering ◽  
Encryption Method

Abstract This paper proposes a novel medical image encryption method based on fast and robust fuzzy C-means clustering image segmentation method and deoxyribonucleic acid encoding. Firstly, the plain medical image is split to interested pixels and uninterested pixels, respectively. Then, the uninterested 0-value pixels are abandoned to reduce the pixels in encryption. Secondly, for the interested pixels, some low-value pixels are also discarded by image segmentation to further reduce the encryption time. Thirdly, a 4-dimensional hyperchaotic system is utilized to process the main pixels of medical image with deoxyribonucleic acid encoding. Finally, lossless encryption and fast encryption are done for different purposes and security analysis shows that the encryption method is robust and secure to resist various attacks.

scholarly journals Image Encryption Algorithm Based on the H-Fractal and Dynamic Self-Invertible Matrix

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xuncai Zhang ◽  
Lingfei Wang ◽  
Ying Niu ◽  
Guangzhao Cui ◽  
Shengtao Geng
Keyword(s):  
Image Encryption ◽  
Security Analysis ◽  
Encryption Algorithm ◽  
Hyperchaotic System ◽  
Original Image ◽  
Invertible Matrix ◽  
Pseudorandom Sequences ◽  
Pixel Location ◽  
Encryption Method ◽  
Image Encryption Algorithm

In this paper, an image encryption algorithm based on the H-fractal and dynamic self-invertible matrix is proposed. The H-fractal diffusion encryption method is firstly used in this encryption algorithm. This method crosses the pixels at both ends of the H-fractal, and it can enrich the means of pixel diffusion. The encryption algorithm we propose uses the Lorenz hyperchaotic system to generate pseudorandom sequences for pixel location scrambling and self-invertible matrix construction to scramble and diffuse images. To link the cipher image with the original image, the initial values of the Lorenz hyperchaotic system are determined using the original image, and it can enhance the security of the encryption algorithm. The security analysis shows that this algorithm is easy to implement. It has a large key space and strong key sensitivity and can effectively resist plaintext attacks.

scholarly journals Secure Image Encryption Algorithm Based on Hyperchaos and Dynamic DNA Coding

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 772
Author(s):  
Shuqin Zhu ◽  
Congxu Zhu
Keyword(s):  
Image Encryption ◽  
Key Management ◽  
Security Analysis ◽  
Statistical Characteristics ◽  
Hyperchaotic System ◽  
Dna Encoding ◽  
Chosen Plaintext Attack ◽  
Encryption Decryption ◽  
The One ◽  
Differential Attacks

In this paper, we construct a five dimensional continuous hyperchaotic system and propose an image encryption scheme based on the hyperchaotic system, which adopts DNA dynamic coding mechanism and classical scrambling diffusion encryption structure. In the diffusion stage, two rounds of diffusion are adopted and the rules of DNA encoding (DNA decoding) are dynamically changed according to the pixel value of the plaintext image, that is, the rules of DNA encoding (DNA decoding) used to encrypt different images are different, which makes the algorithm can resist chosen-plaintext attack. The encryption (decryption) key is only the initial value of the chaotic system, which overcomes the difficulty of key management in the “one time pad” encryption system. The experimental results and security analysis show that the algorithm has some advantages of large key space, no obvious statistical characteristics of ciphertext, sensitivity to plaintext and key and able to resist differential attacks and chosen plaintext attack. It has good application prospects.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

scholarly journals Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 37
Author(s):  
Isah Charles Saidu ◽  
Lehel Csató
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Segmentation Method ◽  
Semantic Image Segmentation ◽  
Batch Normalization ◽  
Set Up ◽  
Image Datasets

We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.

Interactive Medical Image Segmentation Method with only Positive Label Examples

2014 ◽  
Vol 989-994 ◽  
pp. 1088-1092
Author(s):  
Chen Guang Zhang ◽  
Yan Zhang ◽  
Xia Huan Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Segmentation Method ◽  
High Quality ◽  
Neighbor Graph ◽  
Foreground Region ◽  
Training Examples ◽  
Positive Label ◽  
Negative Training

In this paper, a novel interactive medical image segmentation method called SMOPL is proposed. This method only needs marking some pixels on foreground region for segmentation. To do this, SMOPL characterize the inherent correlations among foreground and background pixels as Hilbert-Schmidt independence. By maximizing the independence and minimizing the smoothness of labels on instance neighbor graph simultaneously, SMOPL gets the sufficiently smooth confidences of both positive and negative classes in absence of negative training examples. Then a image segmentation can be obtained by assigning each pixel to the label for which the greatest confidence is calculated. Experiments on real-world medical images show that SMOPL is robust to get a high-quality segmentation with only positive label examples.

scholarly journals An Image Encryption Method Based on the Feistel Network and Dynamic DNA Encoding

2018 ◽  
Vol 10 (4) ◽  
pp. 1-14 ◽  
Author(s):  
Xuncai Zhang ◽  
Zheng Zhou ◽  
Ying Niu
Keyword(s):  
Image Encryption ◽  
Dna Encoding ◽  
Encryption Method

A Symmetric Image Encryption Scheme Base on Hyperchaotic System

2015 ◽  
Vol 719-720 ◽  
pp. 1030-1037
Author(s):  
Tao Song
Keyword(s):  
Image Encryption ◽  
Security Analysis ◽  
Image Transmission ◽  
Hyperchaotic System ◽  
Dynamical Behaviors ◽  
Key Space ◽  
Increasing Demand ◽  
Low Dimensional ◽  
System Variables ◽  
Hyperchaotic Systems

In recent years, chaos-based image encryption technologies have been widely studied to meet the increasing demand for real-time secure image transmission applications. To overcome the drawbacks of small key space and weak security in many existing schemes based on low-dimensional chaotic maps, this paper suggests a security improved scheme with a permutation-diffusion architecture. In the permutation stage, baker map is employed to shuffle the pixel positions. In the diffusion stage, the value of each pixel is altered by using a key stream derived from hyperchaotic system. Compared with ordinary chaotic systems, hyperchaotic systems, with more complex dynamical behaviors and number of system variables, offer greater potential for secure cryptosystem construction. Extensive security analysis has been performed on the proposed scheme, including the most important ones like key space analysis, statistical analysis and key sensitivity, which has demonstrated the satisfactory security of the proposed scheme.

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