scholarly journals BOOST: Medical Image Steganography Using Nuclear Spin Generator

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 501
Author(s):  
Bozhidar Stoyanov ◽  
Borislav Stoyanov
Keyword(s):  
Nuclear Spin ◽  
Experimental Analysis ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Histogram Analysis ◽  
Signal To Noise ◽  
Generator System ◽  
Key Space ◽  
Spin Generator

In this study, we present a medical image stego hiding scheme using a nuclear spin generator system. Detailed theoretical and experimental analysis is provided on the proposed algorithm using histogram analysis, peak signal-to-noise ratio, key space calculation, and statistical package analysis. The provided results show good performance of the brand new medical image steganographic scheme.

scholarly journals Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Fayadh Alenezi ◽  
K. C. Santosh
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Hopfield Neural Network ◽  
Network Stability ◽  
Signal To Noise ◽  
Convergence Problem ◽  
Information Fidelity ◽  
Medical Image Enhancement

One of the major shortcomings of Hopfield neural network (HNN) is that the network may not always converge to a fixed point. HNN, predominantly, is limited to local optimization during training to achieve network stability. In this paper, the convergence problem is addressed using two approaches: (a) by sequencing the activation of a continuous modified HNN (MHNN) based on the geometric correlation of features within various image hyperplanes via pixel gradient vectors and (b) by regulating geometric pixel gradient vectors. These are achieved by regularizing proposed MHNNs under cohomology, which enables them to act as an unconventional filter for pixel spectral sequences. It shifts the focus to both local and global optimizations in order to strengthen feature correlations within each image subspace. As a result, it enhances edges, information content, contrast, and resolution. The proposed algorithm was tested on fifteen different medical images, where evaluations were made based on entropy, visual information fidelity (VIF), weighted peak signal-to-noise ratio (WPSNR), contrast, and homogeneity. Our results confirmed superiority as compared to four existing benchmark enhancement methods.

scholarly journals An Image Steganography Algorithm using Fractional Discrete Wavelet Transform with Advanced Encryption System

2020 ◽  
Vol 9 (5) ◽  
pp. 541-546
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

The research constitutes a distinctive technique of steganography of image. The procedure used for the study is Fractional Random Wavelet Transform (FRWT). The contrast between wavelet transform and the aforementioned FRWT is that it comprises of all the benefits and features of the wavelet transform but with additional highlights like randomness and partial fractional value put up into it. As a consequence of the fractional value and the randomness, the algorithm will give power and a rise in the surveillance layers for steganography. The stegano image will be acquired after administrating the algorithm which contains not only the coated image but also the concealed image. Despite the overlapping of two images, any diminution in the grade of the image is not perceived. Through this steganographic process, we endeavor for expansion in surveillance and magnitude as well. After running the algorithm, various variables like Mean Square Error (MSE) and Peak Signal to Noise ratio (PSNR) are deliberated. Through the intended algorithm, a rise in the power and imperceptibility is perceived and it can also support diverse modification such as scaling, translation and rotation with algorithms which previously prevailed. The irrefutable outcome demonstrated that the algorithm which is being suggested is indeed efficacious.

scholarly journals Digital Image Steganography Using Bit Flipping

2018 ◽  
Vol 18 (1) ◽  
pp. 69-80 ◽  
Author(s):  
Aditya Kumar Sahu ◽  
Gandharba Swain ◽  
E. Suresh Babu
Keyword(s):  
Digital Image ◽  
Quality Index ◽  
Signal To Noise Ratio ◽  
The State ◽  
Image Steganography ◽  
Original Image ◽  
Signal To Noise ◽  
Secret Data ◽  
Hiding Capacity ◽  
State Of Art

Abstract This article proposes bit flipping method to conceal secret data in the original image. Here a block consists of 2 pixels and thereby flipping one or two LSBs of the pixels to hide secret information in it. It exists in two variants. Variant-1 and Variant-2 both use 7th and 8th bit of a pixel to conceal the secret data. Variant-1 hides 3 bits per a pair of pixels and the Variant-2 hides 4 bits per a pair of pixels. Our proposed method notably raises the capacity as well as bits per pixel that can be hidden in the image compared to existing bit flipping method. The image steganographic parameters such as, Peak Signal to Noise Ratio (PSNR), hiding capacity, and the Quality Index (Q.I) of the proposed techniques has been compared with the results of the existing bit flipping technique and some of the state of art article.

Static Thresholded Pulse Coupled Neural Networks in Contourlet Domain — A New Framework for Medical Image Denoising

2020 ◽  
Vol 20 (03) ◽  
pp. 2050025
Author(s):  
S. Shajun Nisha ◽  
S. P. Raja ◽  
A. Kasthuri
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Noise Removal ◽  
Signal To Noise ◽  
Coupled Neural Networks ◽  
Noise Ratio ◽  
Pulse Coupled Neural Networks ◽  
Image Quality Index

Image denoising, a significant research area in the field of medical image processing, makes an effort to recover the original image from its noise corrupted image. The Pulse Coupled Neural Networks (PCNN) works well against denoising a noisy image. Generally, image denoising techniques are directly applied on the pixels. From the literature review, it is reported that denoising after frequency domain transformation is performing better since noise removal is applied over the coefficients. Motivated by this, in this paper, a new technique called the Static Thresholded Pulse Coupled Neural Network (ST-PCNN) is proposed by combining PCNN with traditional filtering or threshold shrinkage technique in Contourlet Transform domain. Four different existing PCNN architectures, such as Neuromime Structure, Intersecting Cortical Model, Unit-Linking Model and Multichannel Model are considered for comparative analysis. The filters such as Wiener, Median, Average, Gaussian and threshold shrinkage techniques such as Sure Shrink, HeurShrink, Neigh Shrink, BayesShrink are used. For noise removal, a mixture of Speckle and Gaussian noise is considered for a CT skull image. A mixture of Rician and Gaussian noise is considered for MRI brain image. A mixture of Speckle and Salt and Pepper noise is considered for a Mammogram image. The Performance Metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Image Quality Index (IQI), Universal Image Quality Index (UQI), Image Enhancement Filter (IEF), Structural Content (SC), Correlation Coefficient (CC), and Weighted Signal-to-Noise Ratio (WSNR) and Visual Signal-to-Noise Ratio (VSNR) are used to evaluate the performance of denoising.

Application of Constraint CMA Blind Equalization Algorithm in the Medical Image Restoration

2012 ◽  
Vol 263-266 ◽  
pp. 2109-2112
Author(s):  
Jin Zhang ◽  
Ya Jie Mao ◽  
Li Yi Zhang ◽  
Yun Shan Sun
Keyword(s):  
Image Restoration ◽  
Cost Function ◽  
Medical Image ◽  
Signal To Noise Ratio ◽  
Blind Equalization ◽  
Optimal Solution ◽  
The Novel ◽  
Constant Modulus ◽  
Signal To Noise ◽  
Equalization Algorithm

A constraint constant module blind equalization algorithm for medical image based on dimension reduction was proposed. The constant modulus cost function applied to medical image was founded. In order to improve the effect of image restoration, a constraint item was introduced to restrict cost function, and it guarantees that the algorithm converge the optimal solution. Compared to the traditional methods, the novel algorithm improves peak signal to noise ratio and restoration effects. Computer simulations demonstrate the effectiveness of the algorithm.

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