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.

Research on Realization of Medical Image Restoration Based on Blind Equalization Algorithm

2020 ◽  
Vol 10 (4) ◽  
pp. 809-813
Author(s):  
Ting Han ◽  
Ruo-Han Zhao ◽  
Mo Dong
Keyword(s):  
Image Restoration ◽  
Medical Image ◽  
Medical Images ◽  
Blind Equalization ◽  
Diagnostic Errors ◽  
Degradation Process ◽  
Restoration Algorithm ◽  
Equalization Algorithm ◽  
Improvement Effect ◽  
Blind Restoration

In order to study the realization of medical image restoration, this study mainly adopts blind equalization algorithm to analyze medical images, and observes the improvement effect of blind equalization technology on medical images. In the process of medical image formation, it is unavoidable to be affected by point spread function, which leads to image degradation and brings great difficulties to diagnosis, and the results of degradation are often unpredictable. The results show that the blind restoration algorithm can restore the image when the degradation process of the medical image is uncertain, which makes the medical image clearer and more accurate, brings great convenience to the diagnosis, and also reduces the diagnostic errors caused by the unclear image.

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 HPLC Estimation of New Impurity Methyl Ezetimibe in Ezetimibe Drug

2020 ◽  
Vol 32 (6) ◽  
pp. 1309-1313
Author(s):  
Duggirala Parvatha Venkata Vardhani Devi ◽  
Kapavarapu Maruthi Venkata Narayanarao ◽  
Pulipaka Shyamala ◽  
Rallabhandi Murali Krishna ◽  
Komali Siva Prasad
Keyword(s):  
Limit Of Detection ◽  
Signal To Noise Ratio ◽  
Gradient Elution ◽  
Hplc Method ◽  
The Novel ◽  
Signal To Noise ◽  
Drug Substances ◽  
Chromatographic Detection ◽  
Noise Ratio ◽  
Gradient Elution Mode

A new gradient elution mode HPLC method was developed and validated to detect and monitor the novel impurity namely methyl ezitimibe in ezetimibe drug substances. Chromatographic detection and analysis of methyl ezetimibe was performed on XBridge C18 column with mobile phase consisting of 0.02 M phosphate buffer (pH 5) and acetonitrile with 1 mL/min flow rate in gradient elution mode. Methyl ezetimibe was detected and monitored at 248 nm. The calibration curve was linear over range of 0.015 to 0.219% concentration. The limit of detection and quantification were computed as 0.005% (signal to noise ratio 3.60) and 0.015% (signal to noise ratio 15.96), respectively. The precision was 0.97% (%RSD) and accuracy was 93.2 to 98.2% (recovery). The developed method was proved suitable to detect and monitor methyl ezetimibe impurity in ezetimibe drug substances.

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 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.

Image Restoration via Group l2,1 Norm-Based Structural Sparse Representation

2017 ◽  
Vol 32 (04) ◽  
pp. 1854008 ◽  
Author(s):  
Kai Song Zhang ◽  
Luo Zhong ◽  
Xuan Ya Zhang
Keyword(s):  
Image Restoration ◽  
Sparse Representation ◽  
Sparse Coding ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Coefficient Matrix ◽  
Optimization Approach ◽  
Signal To Noise ◽  
Nonlinear Features ◽  
Characteristics Of Image

Sparse representation has recently been extensively studied in the field of image restoration. Many sparsity-based approaches enforce sparse coding on patches with certain constraints. However, extracting structural information is a challenging task in the field image restoration. Motivated by the fact that structured sparse representation (SSR) method can capture the inner characteristics of image structures, which helps in finding sparse representations of nonlinear features or patterns, we propose the SSR approach for image restoration. Specifically, a generalized model is developed using structured restraint, namely, the group [Formula: see text]-norm of the coefficient matrix is introduced in the traditional sparse representation with respect to minimizing the differences within classes and maximizing the differences between classes for sparse representation, and its applications with image restoration are also explored. The sparse coefficients of SSR are obtained through iterative optimization approach. Experimental results have shown that the proposed SSR technique can significantly deliver the reconstructed images with high quality, which manifest the effectiveness of our approach in both peak signal-to-noise ratio performance and visual perception.

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