scholarly journals A Hybrid Cryptosystem and Watermarking for Secure Medical Image Transmission

2020 ◽  
pp. 1-14
Author(s):  
Oladotun O. Okediran
Keyword(s):  
Communication Networks ◽  
Medical Image ◽  
Medical Information ◽  
Signal To Noise Ratio ◽  
Common Knowledge ◽  
Image Transmission ◽  
Medical Data ◽  
Signal To Noise ◽  
Open Communication ◽  
Noise Ratio

Advances in computing and communication technologies have provided new methods to store and access medical data electronically and distribute them over open communication networks. Today, patients themselves can access their medical information themselves and medical information can be transmitted among medical institutions as well as stakeholders in the health sector.  Accompanying these benefits are concomitant risks for patient medical records in electronic formats and strictly personal medical documentations being transmitted and accessible over open communication channels such as the Internet. Thus it is common knowledge that there should be in place network-level security measures and protocols in medical information systems. Many security schemes that were based on cryptography, watermarking and steganography have been proposed and implemented to secure medical data. However, an apt review of relevant literature revealed that in many implementations robustness against attacks is not guaranteed. Issues bordering on low embedding capacity, low robustness, low imperceptibility and bad trade tradeoff between robustness and capacity are evident in many implementations. In this paper, a hybrid Rivest-Shamir-Adleman (RSA) algorithm, Rivest Cipher 4 (RC4) algorithm and Spread Spectrum techniques were proposed for securing medical image data over open communication networks. The performance of the proposed scheme was evaluated using Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR), Mean Square Error (MSE) and Bit Error Rate (BER). For the five sample medical images used to test the scheme, the BER value is zero while the PNSR and SNR are consistent and they returned desirable high values. The MSE values for the images were low. The average values of the PSNR, SNR and MSE are 51.88 dB, 43.38 dB and 0.113 respectively. Hence, the proposed scheme is utterly revertible, robust and highly imperceptible; the original images can be retrieved by the recipient without any deformation or alteration.

scholarly journals Image multiplexing using residue number system coding over MIMO-OFDM communication system

2019 ◽  
Vol 9 (6) ◽  
pp. 4815
Author(s):  
Mohamed Ibrahim Youssif ◽  
Amr ElSayed Emam ◽  
Mohamed Abd ElGhany
Keyword(s):  
Image Quality ◽  
Communication System ◽  
Signal To Noise Ratio ◽  
Image Transmission ◽  
Number System ◽  
Residue Number System ◽  
Signal To Noise ◽  
Residue Number ◽  
Mimo Ofdm ◽  
Noise Ratio

<p>Image transmission over Orthogonal Frequency-Division Multiplexing (OFDM) communication system is prone to distortion and noise due to the encountered High-Peak-to-Average-Power-Ratio (PAPR) generated from the OFDM block. This paper studies the utilization of Residue Number System (RNS) as a coding scheme for digital image transmission over Multiple-Input-Multiple-Output (MIMO) – OFDM transceiver communication system. The use of the independent parallel feature of RNS, as well as the reduced signal amplitude to convert the input signal to parallel smaller residue signals, enable to reduce the signal PAPR, decreasing the signal distortion and the Bit Error Rate (BER). Consequently, improving the received Signal-to-Noise Ratio (SNR) and enhancing the received image quality. The performance analyzed though BER, and PAPR. Moreover, image quality measurement is achieved through evaluating the Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), and the correlation values between the initial and retrieved images. Simulation results had shown the performance of transmission/reception model with and without RNS coding implementation.</p><p> </p>

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.

scholarly journals A Jointly Optimized VariableM-QAM and Power Allocation Scheme for Image Transmission

2012 ◽  
Vol 2012 ◽  
pp. 1-14
Author(s):  
Mohamed El-Tarhuni ◽  
Mohamed Hassan ◽  
Akram Bin Sediq
Keyword(s):  
Search Algorithm ◽  
Signal To Noise Ratio ◽  
Image Transmission ◽  
Perceptual Quality ◽  
Practical Implementation ◽  
Signal To Noise ◽  
Look Up Table ◽  
Noise Ratio ◽  
Modulation Level

We introduce an improved image transmission scheme over wireless channels with flat Rayleigh fading. The proposed scheme jointly optimizes bit power and modulation level to maximize the peak signal-to-noise ratio (PSNR) of the reconstructed image and hence improves the perceptual quality of the received image. In this optimization process, the significance of bits with regard to the overall quality of the image is exploited. The optimality of the proposed algorithm is demonstrated using the Lagrange method and verified through an iterative offline exhaustive search algorithm. For practical implementation, a look-up table is used at the transmitter for assigning the bit power and modulation level to each bit stream according to the received signal-to-noise ratio (SNR) observed at the receiver. The proposed scheme has low complexity since the look-up table is computed offline, only once, and used for any image which makes it suitable for devices with limited processing capability. Analytical and simulation results show that the proposed scheme with jointly optimized bit power and variable modulation level provides an improvement in PSNR of about 10 to 20 dB over fixed power fixed modulation (16-QAM). A further reduction in complexity is achieved by using the average signal-to-noise ratio rather than the instantaneous SNR in selecting the system parameters.

Anti-Jamming Ability Research on COFDM Modulation of UAV Image Transmission

2014 ◽  
Vol 1079-1080 ◽  
pp. 614-617
Author(s):  
Zhuang Wu ◽  
Xiao Xin Ma ◽  
Lan Zhang
Keyword(s):  
Phase Noise ◽  
Signal To Noise Ratio ◽  
Image Transmission ◽  
Frequency Offset ◽  
Signal To Noise ◽  
Orthogonal Property ◽  
Noise Ratio ◽  
Uav Image ◽  
Inter Symbol Interference

Thefrequency offset and phase noise will make the orthogonal property between eachsub carrier deterioration. Only1% frequency offset will cause 30dB signal-to-noise ratio decrease. In order to researchanti-jamming ability on COFDM modulation, we use the PXW-100S type codedmodulation module and the PXW-500S type DVB-T receiving board for unmannedaerial vehicle(UAV) platform, through the elimination of inter symbol interference(ISI) and inter carrier interference(ICI).

scholarly journals Can Signal-to-Noise Ratio Perform as a Baseline Indicator for Medical Image Quality Assessment

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 11534-11543 ◽  
Author(s):  
Zhicheng Zhang ◽  
Guangzhe Dai ◽  
Xiaokun Liang ◽  
Shaode Yu ◽  
Leida Li ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Medical Image ◽  
Image Quality Assessment ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Multiscale Transform and Shrinkage Thresholding Techniques for Medical Image Denoising – Performance Evaluation

2020 ◽  
Vol 20 (3) ◽  
pp. 130-146
Author(s):  
S. Shajun Nisha ◽  
S. P. Raja
Keyword(s):  
Image Denoising ◽  
Medical Image ◽  
Performance Metrics ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Magnetic Resonance Imaging Mri ◽  
Qualitative Performance

AbstractDue to sparsity and multiresolution properties, Mutiscale transforms are gaining popularity in the field of medical image denoising. This paper empirically evaluates different Mutiscale transform approaches such as Wavelet, Bandelet, Ridgelet, Contourlet, and Curvelet for image denoising. The image to be denoised first undergoes decomposition and then the thresholding is applied to its coefficients. This paper also deals with basic shrinkage thresholding techniques such Visushrink, Sureshrink, Neighshrink, Bayeshrink, Normalshrink and Neighsureshrink to determine the best one for image denoising. Experimental results on several test images were taken on Magnetic Resonance Imaging (MRI), X-RAY and Computed Tomography (CT). Qualitative performance metrics like Peak Signal to Noise Ratio (PSNR), Weighted Signal to Noise Ratio (WSNR), Structural Similarity Index (SSIM), and Correlation Coefficient (CC) were computed. The results shows that Contourlet based Medical image denoising methods are achieving significant improvement in association with Neighsureshrink thresholding technique.

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Experiments in Bright-Field Imaging with Hollow-Cone Illumination

1981 ◽  
Vol 39 ◽  
pp. 226-227
Author(s):  
W. Kunath ◽  
K. Weiss ◽  
E. Zeitler
Keyword(s):  
Signal To Noise Ratio ◽  
Carbon Support ◽  
Electronic System ◽  
Optic Axis ◽  
Hollow Cone ◽  
Signal To Noise ◽  
Bright Field ◽  
Noise Ratio ◽  
Single Field ◽  
Field Imaging

Bright-field images taken with axial illumination show spurious high contrast patterns which obscure details smaller than 15 ° Hollow-cone illumination (HCI), however, reduces this disturbing granulation by statistical superposition and thus improves the signal-to-noise ratio. In this presentation we report on experiments aimed at selecting the proper amount of tilt and defocus for improvement of the signal-to-noise ratio by means of direct observation of the electron images on a TV monitor.Hollow-cone illumination is implemented in our microscope (single field condenser objective, Cs = .5 mm) by an electronic system which rotates the tilted beam about the optic axis. At low rates of revolution (one turn per second or so) a circular motion of the usual granulation in the image of a carbon support film can be observed on the TV monitor. The size of the granular structures and the radius of their orbits depend on both the conical tilt and defocus.

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

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