A medical image enhancement method using adaptive thresholding in NSCT domain combined unsharp masking

Noises and artifacts are introduced to medical images due to acquisition techniques and systems. This interference leads to low contrast and distortion in images, which not only impacts the effectiveness of the medical image but also seriously affects the clinical diagnoses. This paper proposes an algorithm for medical image enhancement based on the nonsubsampled contourlet transform (NSCT), which combines adaptive threshold and an improved fuzzy set. First, the original image is decomposed into the NSCT domain with a low-frequency subband and several high-frequency subbands. Then, a linear transformation is adopted for the coefficients of the low-frequency component. An adaptive threshold method is used for the removal of high-frequency image noise. Finally, the improved fuzzy set is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experiments and simulation results show that the proposed method is superior to existing methods of image noise removal, improves the contrast of the image significantly, and obtains a better visual effect.

The clinical assistant diagnosis has a high requirement for the visual effect of medical images. However, the low frequency subband coefficients obtained by the NSCT decomposition are not sparse, which is not conducive to maintaining the details of the source image. To solve these problems, a medical image fusion algorithm combined with sparse representation and pulse coupling neural network is proposed. First, the source image is decomposed into low and high frequency subband coefficients by NSCT transform. Secondly, the K singular value decomposition (K-SVD) method is used to train the low frequency subband coefficients to get the overcomplete dictionary D, and the orthogonal matching pursuit (OMP) algorithm is used to sparse the low frequency subband coefficients to complete the fusion of the low frequency subband sparse coefficients. Then, the pulse coupling neural network (PCNN) is excited by the spatial frequency of the high frequency subband coefficients, and the fusion coefficients of the high frequency subband coefficients are selected according to the number of ignition times. Finally, the fusion medical image is reconstructed by NSCT inverter. The experimental results and analysis show that the algorithm of gray and color image fusion is about 34% and 10% higher than the contrast algorithm in the edge information transfer factor QAB/F index, and the performance of the fusion result is better than the existing algorithm.

In this paper, a novel remote sensing image enhancement method is proposed. Firstly, the image is decomposed into a low-frequency component and some high-frequency components with NSCT; Secondly, contrast stretching is applied to improve the contrast of the low-frequency and the adaptive threshold is used to remove noise of the high-frequency; Thirdly, the inverse NSCT is adopted to reconstruct the image, and the unsharp filter is used to enhance the reconstructed image. Experiments show that the proposed method can suppress pseudo-Gibbs phenomena and noise interference of enhanced image.

Wavelet threshold denoising can easily cause edge blur, while total variational denoising is insufficient to achieve image smooth area denoising. Therefore, this paper proposes a new denoising method combining the total variational model and the adaptive wavelet threshold. Firstly, the original noisy image is processed by the total variational model, the improved Prewitt operator is used to extract the edge of the processed image, and the edge denoising image is obtained. Then, the original noisy image is divided into edge and non-edge images by the improved Prewitt operator, and the non-edge image is de-noised by the adaptive wavelet threshold method to obtain the non-edge denoising image. Finally, after denoising, the edge image and non-edge image are superimposed to achieve high-definition restoration of medical images. Experimental results show that the proposed method can effectively denoise medical images and preserve the original edges and details in the images.KeywordsImage denoisingTotal variational modelPrewitt operatorWavelet transform

본 논문에서는 2차원 바코드 영상을 위한 적응적 이진화 방법을 제안한다. 적응형 이진화 방법은 조명의 영향을 최소화하여 이진화를 수행하는 기술이다. 적응적 이진화 방법은 주로 문서 영상에 맞게 발전되어 왔다. 기존 방법들은 적응적 이진화에서 사용되는 박스에 대한 크기 설정 문제를 가지고 있다. 이 문제로 기존 방법들은 이차원 바코드 영상 인식에 적용하기에 부적절하다. 문제점을 극복하기 위해 먼저 박스크기와 기존 방법들의 문제점을 분석하고, 이를 기반으로 적분영상을 사용한 새로운 적응형 이진화 방법을 소개한다. 제안한 방법의 성능 입증을 위해 기존의 방법과 속도, 성능 비교 실험을 수행하였고 실험 결과는 기존 방법보다 우수함을 입증하였다. In this paper, we propose an adaptive thresholding method to binarize two-dimensional barcode images. Adaptive thresholding methods that minimize light effects convert an original image into a binary image. The methods are applied to document image binarization. The methods, however, have problems of determining box size used in adaptive thresholding. thus, they inappropriate to use in recognition of two-dimensional barcode images. To overcome the problem, we analysis the problem and propose a new adaptive threshold method using the integral image. To show the effectiveness of our method, we compared our method with the well-known existing methods in terms of visual quality and processing time. The experimental result indicates that the proposed method is superior to the existing method.

The denoising of medical images degraded by various types of noises is a major problem in medical image processing. A very good tradeoff between detail preservation and noise removal is obtained while implementing with various successive filtering procedures. This paper presents a wavelet-based multiscale products thresholding and spatial based Bilateral Filter scheme for noise removal of magnetic resonance images. In the first stage, the noisy image is passed through Bilateral Filter (BF) and some amount of noise is reduced but the image becomes blurred, hence adaptive wavelet thresholding is applied with multiscale product rule in the second stage. Rather than using Wavelet Transform, Dyadic Wavelet Transform is employed. After apply the Dyadic Wavelet Transform, wavelet interscale dependencies is exploited by multiply the adjacent wavelet subbands to preserve edge structures while suppressing noise. An adaptive threshold is calculated and implemented on the multiscale products rather than on the wavelet coefficients. Hence to identify important features like edges, curves and textures the thresholding is applied to multiscale products. The proposed method for medical image denoising is performed through a recursive application of simple steps and demonstrated on a number of standard medical images. The results obtained with the proposed method outperform other methods both visually and in terms of objective quality peak-signal-to-noise ratio (PSNR).

Medical image enhancement is an effective tool to improve visual quality of digital medical images. In this paper, we propose a new unsharp masking scheme for medical image enhancement. It embeds the PLIP multiplication into the unsharp masking framework. Experimental results demonstrate that the proposed method can effectively enhance the overall contrast and edges of medical images while suppressing background noise.

Fault detection in a grid-connected photovoltaic system using adaptive thresholding method

Introduction: Studies on the security and interoperability of Internet of medical things (IoMT)in health are still scarce. The most important effects of the IoMT on health care are the ability to exchange information and reduce hospitalization and health care costs. The main challenges of the IoMT in healthcare are security and protection of privacy, among which encryption of images is very important in the field of communication and security. The main purpose of this study was to design a secure channel for encrypting and sending medical data. Methods: This study presents a new method for encrypting medical images to preserve patient information by synchronizing two chaotic systems. The use of chaotic signals as carriers of medical images greatly enhances security and greatly reduces the possibility of detection. The idea of synchronization of chaotic systems was presented in multi states with fixed time delay. In this idea, with the help of Lyapunov’s theory of stability, a controller was designed to establish the stability of the closed-loop system. Then, according to the synchronization idea and its error, a chaotic masking method was proposed to encrypt patient images. The simulation and accuracy of the results of the presented method were investigated in MATLAB software version 2020.Results: The simulations were performed on medical color images and CT, MRI, and X-ray images. Encrypted and recovered images were obtained by the proposed system. To evaluate the performance of the proposed method, various criteria including image histogram, signal-to-noise rate, correlation, and entropy of information were evaluated. The results showed the excellent image encrypting efficiency of the proposed method.Conclusion: Using the proposed method, medical images were encrypted based on a secure protocol, and then with satisfactory quality, images were recovered after transmission.

Medical X-ray image is used as the important information and basis for the clinical diagnosis, while its quality digressed because of the interferences caused by the human body structure, equipments, and environmental factors. This paper verifies that the X-ray image degradation caused by the X-ray scattering which is similar as the haze scattering. Dark channel prior is widely used on haze removing, we applied the dark channel prior method in medical X-ray image enhancement. The experimental results show that the method can effectively increase the image contrast, highlight the details.

For the fusion of traditional medical image fusion effect is poor, a pseudo gibbs phenomenon is complex and PCNN parameters Settings and so on, proposed a based on the next sampling shear wave transform (NSST) and particle swarm optimization algorithm (PSO), the standard differential evolution algorithm (DE) combining optimization pulse coupled neural network (PCNN) parameters of medical image fusion method. Source image in NSST domain is decomposed into the same size high frequency sub-bands of k and a low frequency subband, combines PSO and DE using spatial frequency (SF) as the fitness function of the optimization algorithm to improve the PCNN, search for the optimal parameters for fusion of the high frequency subband coefficients, low-frequency subband coefficients of energy weighted average method is adopted to improve the fusion, Finally, NSST inverse transformation is used to obtain the final fusion image. The fusion effect of medical images was evaluated and analyzed according to subjective and objective evaluation indexes. Experimental results show that this algorithm is better than other algorithms in objective evaluation index and has better fusion effect.

This paper presents a robust adaptive moving human detection and recognition method in videos. The human detection method consists of modified moving average background model with supportive secondary model and an adaptive threshold selection model based on Gaussian distribution. The moving average background model is used for background modeling and the background subtraction system is used to provide foreground image through difference image between current image and background model. The adaptive threshold method is used to simultaneously update the system to environment changes. The modified human model consists of five parts with robust features to facilitate human recognition process. For recognition purpose Support Vector Machine has been used as classifier. Experimental results show the effectiveness of proposed system.

There are various mathematical optimization problems that can be effectively solved by meta-heuristic algorithms. The improvement of these algorithms is that they carry out iterative search processes which resourcefully act upon exploration and exploitation in spatial domain containing global and local optima. An innovative robust Cuckoo Optimization Algorithm (COA) with adaptive thresholding is proposed to solve the problem of detection and estimation of surface defects on metal coating surfaces. The proposed method is developed through implementing changes to COA and improved the performance. For improving capability of local search as well to keep the global search effect, the enhanced methods such as level set is associated with the proposed method. Also, the method adapts dynamic step size, adaptively changing with the search process for improving the rate of convergence and the ability of local search. The algorithm performance is scrutinized from the experimental analysis and results. Also, the segmentation effectiveness is further enhanced by adapting suitable methods for preprocessing and post processing. The comparison and analysis of the results accomplished with the proposed method and results of earlier methods shows superior performance of the proposed method.

Medical image enhancement is to process the image so that the result is more suitable than the original image for analysis and diagnosis. The information of image is acquired by the observer's eyes, so in the image processing, the human visual characteristics (HVC) as an important factor should be well thought of. In the paper, we study HVC from testing the distinguishability of gray scale and the sensitivity to the structure of image, analyze the physiological character and psychological characters of human vision, and summarize the laws of HVC. A new medical image enhancement method is presented based on HVC. It divides the image into the smooth areas and detail areas, and uses different processing methods for the areas according to HVC. Experiment results demonstrate that the method enhances the edge detail information, and improves the image quality, thereby increases the veracity of diagnoses, makes the treatment approach more exact.

Chapter 14 - Despeckling of ultrasound images based on the multiresolution approach and Gaussianization transform