Gray Level Changes Research Articles

Based on PVM image gray level data to analysis the effect of N-vinyl caprolactam on hydrate growth

The usage of hydrate inhibitors is an effective method to solve the problem of flow assurance in deep-water pipelines. However, the mechanism of hydrate formation and transformation in inhibitor-containing systems is not well understood, and the research methodology needs to be innovated. In this study, a possible mechanism of wearing away during hydrate particle transformation was proposed for the first time, based on the hydrate growth process in the system containing the kinetic inhibitor N-vinyl caprolactam monomer. A method to analysed the hydrate growth and transformation dynamics through the change of gray level in the image was proposed. And the hydrate growth process was divided into three different stages based on the trend analysis of the gray level data of the images,trends in the dynamics of hydrate particle growth within the bulk phase corresponding to changes in gray level were verified. On this basis, the effects of stirring rate, subcooling and the monomer concentration of the NVCap on the growth of hydrates were discussed, and it was suggested that there might be differences in the mechanisms exhibited by the inhibitor at different growth stages of hydrates. In this study, the process of analysing the internal growth dynamics of hydrate systems through image gray levels was reported for the first time, and its trend changes corresponding to hydrate growth dynamics were verified, which proved the feasibility of this method, and this study provides an innovative research methodology and idea for the study of the dynamics of hydrate generation and transformation.

Contrast-enhanced CT-based radiomic analysis for determining the response to anti-programmed death-1 therapy in esophageal squamous cell carcinoma patients: A pilot study.

In view of the fact that radiomics features have been reported as predictors of immunotherapy to various cancers, this study aimed to develop a prediction model to determine the response to anti-programmed death-1 (anti-PD-1) therapy in esophageal squamous cell carcinoma (ESCC) patients from contrast-enhanced CT (CECT) radiomics features. Radiomic analysis of images was performed retrospectively for image samples before and after anti-PD-1 treatment, and efficacy analysis was performed for the results of two different time node evaluations. A total of 68 image samples were included in this study. Quantitative radiomic features were extracted from the images, and the least absolute shrinkage and selection operator method was applied to select radiomic features. After obtaining selected features, three classification models were used to establish a radiomics model to predict the ESCC status and efficacy of therapy. A cross-validation strategy utilizing three folds was employed to train and test the model. Performance evaluation of the model was done using the area under the curve (AUC) of receiver operating characteristic, sensitivity, specificity, and precision metric. Wavelet and area of gray level change (log-sigma) were the most significant radiomic features for predicting therapy efficacy. Fifteen radiomic features from the whole tumor and peritumoral regions were selected and comprised of the fusion radiomics score. A radiomics classification was developed with AUC of 0.82 and 0.884 in the before and after-therapy cohorts, respectively. The combined model incorporating radiomic features and clinical CECT predictors helps to predict the response to anti-PD-1therapy in patients with ESCC.

Pixel’s Quantum Image Enhancement Using Quantum Calculus

The current study provides a quantum calculus-based medical image enhancement technique that dynamically chooses the spatial distribution of image pixel intensity values. The technique focuses on boosting the edges and texture of an image while leaving the smooth areas alone. The brain Magnetic Resonance Imaging (MRI) scans are used to visualize the tumors that have spread throughout the brain in order to gain a better understanding of the stage of brain cancer. Accurately detecting brain cancer is a complex challenge that the medical system faces when diagnosing the disease. To solve this issue, this research offers a quantum calculus-based MRI image enhancement as a pre-processing step for brain cancer diagnosis. The proposed image enhancement approach improves images with low gray level changes by estimating the pixel’s quantum probability. The suggested image enhancement technique is demonstrated to be robust and resistant to major quality changes on a variety of MRI scan datasets of variable quality. For MRI scans, the BRISQUE “blind/referenceless image spatial quality evaluator” and the NIQE “natural image quality evaluator” measures were 39.38 and 3.58, respectively. The proposed image enhancement model, according to the data, produces the best image quality ratings, and it may be able to aid medical experts in the diagnosis process. The experimental results were achieved using a publicly available collection of MRI scans.

Local Binary Pattern Regrouping for Rotation Invariant Texture Classification

This paper represent a deep study of the Local Binary Pattern (LBP) method and its variants of patterns regrouping , which is largely used in texture classification as well in other domain. The analysis of LBP’s two hundred fifty-six patterns has led us to propose a new organization of uniform and no uniform patterns into twenty-eight groups; each group assembled a number of patterns varied according to specific terms. The principal idea is to preserve the low complexity of LBP and simultaneously increase the method robustness against quality degradation caused by image operations like rotation, grey level changes, illumination and mirror effects. The experiments are done with the two texture databases Outex and Brodatz; the tests are proving the robustness of Local Binary Pattern Regrouping (LBPG) under circumstances.

Radiographic predictive factors for 10-year survival of removable partial denture abutment teeth: Alveolar bone level and density.

To determine postoperative periodontal and radiographic factors that predict the survival rates of abutments of removable partial dentures (RPDs). Patients who wore RPDs for > 10 years and received supportive periodontal therapy were included. Periodontal examinations and radiographic assessments were conducted on 83 abutment teeth in 35 patients at baseline, and five years after RPD insertion. In addition to conventional factors, such as tooth mobility at 5 years, radiographic factors, such as the crown-root ratio (ΔCR ratio) and gray-level changes reflecting changes in alveolar bone density (ΔABD), were evaluated. The impact of the covariables on the 10-year survival of abutment teeth was estimated using a multivariate Cox regression model, considering multicollinearity. Patients were classified as having A2-B2 (45.7%) and B3-C2 (54.3%) tooth loss, according to the Eichner classification. A probing depth ≥ 4 mm, tooth mobility ≥ grade 1, and CR ratio ≥ 1 were found in 30.1%, 33.7%, and 51.8% of abutment teeth, respectively. The 10-year survival rate of abutment teeth was 86.7%. Multivariate analysis showed that the 10-year survival of abutment teeth was significantly associated with root canal treatment (P = 0.045, hazard ratio [HR] = 1.23), the 5-year ΔCR ratio (P = 0.022, HR = 3.20), and ΔABD on the edentulous side of the abutment teeth (P = 0.047, HR = 1.08). In addition to root canal treatment, changes in the CR ratio and radiographic alveolar bone density at five years predicted the long-term survival rate of RPD abutments.

A Mathematical Model for COVID-19 Image Enhancement based on燤ittag-Leffler-Chebyshev Shift

The lungs CT scan is used to visualize the spread of the disease across the lungs to obtain better knowledge of the state of the COVID-19 infection. Accurately diagnosing of COVID-19 disease is a complex challenge that medical system face during the pandemic time. To address this problem, this paper proposes a COVID-19 image enhancement based on Mittag-Leffler-Chebyshev polynomial as pre-processing step for COVID-19 detection and segmentation. The proposed approach comprises the Mittag-Leffler sum convoluted with Chebyshev polynomial. The idea for using the proposed image enhancement model is that it improves images with low gray-level changes by estimating the probability of each pixel. The proposed image enhancement technique is tested on a variety of lungs computed tomography (CT) scan dataset of varying quality to demonstrate that it is robust and can resist significant quality fluctuations. The blind/referenceless image spatial quality evaluator (BRISQUE), and the natural image quality evaluator (NIQE) measures for CT scans were 38.78, and 7.43 respectively. According to the findings, the proposed image enhancement model produces the best image quality ratings. Overall, this model considerably enhances the details of the given datasets, and it may be able to assist medical professionals in the diagnosing process.

Research on non-contact respiratory rate measurement method based on video information

Traditional methods of non-contact human respiratory rate measurement usually require complex devices or algorithms. Aiming at this problem, a non-contact respiratory rate measurement method based on only the RGB video information was proposed in this paper. The method consisted of four steps. Firstly, spatial filtering was applied to each frame of the input video. Secondly, a gray compensation algorithm was used to compensate for the gray level change caused by the environmental light. Thirdly, the gray levels of each pixel over time were filtered separately by a low-pass filter. Finally, the region of interest was determined based on the filtering results, and the respiration rate of the human is measured. The physical measurement experiments were designed, and the measurement accuracy was compared with that of the biological radar. The error of the proposed method was between - 5.5% and 3% in different detection directions. The results show that the non-contact respiration rate measurement method can effectively measure the human respiration rate.

Use of Picture Information Measure using Color Features for Image Retrieval in CBIR

Content-Based Image Retrieval systems backups the image retrieval process using the primary characteristics of image like colour, shape, texture and spatial locations clubbed with the semantic approaches for better efficiency and performance. Various information measures have been proposed in order to increase the level of Retrieval. A method of picture information measures based upon the concept of the minimum number of gray level changes to convert a picture into one with a desired histogram is presented. In search of finding a new perspective an integrated approach of Picture Information Measure (PIM) employed with the primitive visual feature color. The retrieval results obtained by applying color histogram (CH) on PIM (PIM of Red Green and Blue and there integrated variation) + Color Moment to a 1000 image database demonstrated significant improvement in retrieval effectively.

Optimization of Edge Extraction Algorithm for Objects in Complex Background

In the field of artificial intelligence, machine vision is expected because of its low cost and easy popularization. Feature extraction is one of the core steps of machine vision. When we use Canny algorithm, which extracts edges by gray level change, we cannot directly separate the object from the background. The edge information of the object will also be lost when the Gaussian filter smoothes the image. The research proposes an improved edge extraction algorithm based on Canny algorithm. We use the image difference method and object envelopment method to separate the object from the background, so the data to be processed is greatly reduced and the speed and accuracy of the algorithm are improved. Then we use bilateral filtering instead of Gaussian filtering, which increases the weight of gray difference on the basis of Gaussian filtering, so we can to retain more edge information. Finally, we improve the gradient operator, by determining the gradient operator coefficient according to the inverse ratio of the Euclidean distance between the pixel and the center pixel. The average performance of the new gradient operator in the center of the picture is better than that of Sobel operator. Experimental simulation shows that the algorithm has good detection accuracy. Compared with traditional algorithms, the improved algorithm is not affected by complex backgrounds, and reduce the influence of light. The improved algorithm has clear edges in the center area of the image.

SAR Image Recording Strategy to Improve Characteristics

The low information consistency and serious interference caused by synthetic aperture radar (SAR) imaging characteristics and target attributes affect the accuracy of SAR image information description, which reduces the performance of SAR image registration. In this work, the polar speeded up robust features (PSURF) descriptor is proposed for SAR image registration. In PSURF, according to the advantages of pole space in the relationship between pixels, a quantitative model of information change based on pole space is established to improve the integrity, consistency, and separability of image features. Firstly, the support region is transformed into polar coordinates. Then, the image gray level changes in the radial and angular directions are quantified with different scales. Finally, the PSURF descriptor based on the scale of keypoints is constructed covering the omnidirectional space.

A Framework for Multimedia Event Classification With Convoluted Texture Feature

Background:The proposed work uses two approaches as its background. They are (i) LBP approach (ii) Kirsch compass mask.:Texture classification plays a vital role in object discrimination from the original image. LBP is majorly used for classifying texture. Many filtering based methods co-occurrence matrix method, etc., were used, but due to the computational efficiency and invariance to monotonic grey level changes, LBP is adopted majorly. Second, as Edge plays a vital role in discriminating the object visually, Kirsch compass mask was applied to obtain maximum edge strength in 8 compass directions which has the advantage of changing the mask according to users own requirement than any other compass mask.Objective:The objective of our work was to extract better features and model a classifier for the Multimedia Event Detection task.Methods:The proposed work consists of two steps for feature extraction. Initially, an LBP based approach is used for object discrimination, later, convolution is used for object magnitude determination using Kirsch Compass mask. Eigenvalue decomposition is adopted for feature representation. Finally, a classifier is modelled using a chi-square kernel for the event classification task.Results:The proposed event detection work is experimented using Columbia Consumer Video (CCV) dataset. It contains 20 event based videos. The proposed work is evaluated with other existing works using mean Average Precision (mAP). Several experiments have been carried out to evaluate our work, they are LBP vs. non-LBP approach, Kirsch vs. Robinson compass mask, Kirsch masks angle wise analysis, comparison of above approaches are performed in a modeled classifier. Two approaches are used to compare the proposed work with other existing works.They are (i) Non Clustered Events (events were considered individually and one versus one strategy was followed) (ii) Clustered Events (some events were clustered and followed one vs. all strategy and remaining events were non-clustered).Conclusion:In the proposed work, a method for event detection is described. Feature extraction is performed using LBP based approach and Kirsch compass mask for convolution. For event detection, a classifier model is generated using the chi-square kernel. The accuracy of event classification is further increased using clustered events approach. The proposed work is compared with various state- of- the- art methods and proved that the proposed work obtained outstanding performance.

Life detection and non-contact respiratory rate measurement in cluttered environments

A method is proposed in this paper for life detection and non-contact respiratory rate measurement in cluttered environments. Only an RGB video of the detection area is required. In the method, spatial filtering is firstly applied to each frame of the video for image denoising. Gray level compensation follows to compensate for the change of gray level caused by the environment light. Thirdly, the gray levels of each pixel over time are filtered separately by a low-pass filter. At last, the human is located and the respiratory rate is measured. Tests on a self-made dataset show that an accuracy of 76.7% is achieved by the proposed method, which is better than that of the Convolutional Neural Networks (30%) and the histogram of oriented gradients (3.3%).

USING 4-[18F]-ADAM MEASURE SEROTONIN TRANSPORTER VARIATIONS WITH HAMD SCALE RELATED TO MAJOR DEPRESSIVE DISORDER IN CT/PET THREE-DIMENSIONAL IMAGE

The medical image can be divided into two major classes: anatomical image and functional image. Magnetic resonance image (MRI) and computed tomography (CT) belong to the anatomical image, which can show an outline of an organ and a tissue clearly. The functional image can show the image of the organ and the tissue metabolism situation. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) are also part of the functional image. In this paper, we use 4-[[Formula: see text]F]-ADAM to measure serotonin transporter changes and find the relation with major depressive disorder in CT/PET three-dimensional image. In clinical diagnosis, based on the video imaging methods can be divided into the anatomy and function of these two categories. Therefore, the combination of anatomical and functional imaging can provide more useful information. In the study, we use the CT image to get the reference position and combine it with the isotope metabolic situation from PET. We use the fused image to construct 3D image and count serotonin transporter with different times, and measure the variance about serotonin transporter changes in the organization of the metabolism and distribution. We use the variance to find the relation and compare the score using Hamilton Depression Rating Scale by experienced psychiatrists. After the test cases, we measure the serotonin transporter distribution of depressed patients can do a preliminary classification. We can get the gray-level value changes in major depressive disorder patients at different times. The advantage of this method is to offer more information to diagnose or analyze the difference between the brain organs, such as pons, midbrain, thalamus, corpus striatum, and prefrontal cortex.

The gains of a 4-week cognitive training are not modulated by novelty.

Cognitive training should not only improve performance of the trained task, but also untrained abilities. Exposure to novelty can improve subsequent memory performance, suggesting that novelty exposure might be a critical factor to promote the effects of cognitive training. Therefore, we combined a 4‐week working memory training with novelty exposure. Neuropsychological tests and MRI data were acquired before and after training to analyze behavior and changes in gray matter volume, myelination, and iron levels. In total, 83 healthy older humans participated in one of three groups: Two groups completed a 4‐week computerized cognitive training of a two‐back working memory task, either in combination with novel or with familiarized nature movies. A third group did not receive any training. As expected, both training groups showed improvements in task specific working memory performance and reaction times. However, there were no transfer or novelty effects on fluid intelligence, verbal memory, digit‐span, and executive functions. At the neural level, no significant micro‐ or macrostructural changes emerged in either group. Our findings suggest that working memory training in healthy older adults is associated with task‐specific improvements, but these gains do not transfer to other cognitive domains, and it does not lead to structural brain changes.

Position-based intensity change model for illumination variation in digital image correlation

The illumination variation exerts a significant influence on the accuracy and efficiency of digital image correlation (DIC) technology. However, the existing intensity change models assume that all the pixels in the reference subset have the same gray-level intensity change parameters, which cannot accurately reflect the effect of illumination variations. We pay attention to the position effect on intensity variations and propose a position-based intensity change model to study the illumination variation in DIC. The proposed model combines the gray-level intensities, the illumination variation, and the position parameters of a reference subset. The forward additive Gauss–Newton algorithm with a first-order shape function is applied to evaluate the feasibility and effectiveness. Simulation and experimental results verify that the proposed model has higher accuracy in different illumination conditions as compared with the existing second-order intensity change model.

A Research for Evaluation of Life Span of a Tire through Image Processing Technique.

The tire component of a car is the only one which is in direct contact with the road. They become defective because of factors like driver’s driving, wear out, pressure, overheating, wheel alignment. When the tire is worn, the threads shallow up and can cause the failure of the tires hence reducing safety. A new tire has the capacity of absorbing the heat whereas; the old tire does not absorb the heat and hence is prone to damages. Gray level changes in the image of the tire before and after its surface deformation are found. These changes predict the life of the tire, "more the gray level changes lesser is the life span of the tire and vice-versa". These gray level changes can be found out by using "SVD" The surface deformation of a tire is found by using SVD (Single value decomposition) with PCA(Principal component analysis) algorithm. PCA is used for the sake of Dimensionality Reduction. The principal components depending upon the variance of each training samples and the test samples help in the analysis of its life span. This paper proposes a robust image processing technique developed using the MatLab platform for nondestructive testing of a tire

Speckle Noise Removal in Image-based Detection of Refractive Index Changes in Porous Silicon Microarrays

Based on porous silicon (PSi) microarray images, we propose a new method called the phagocytosis algorithm (PGY) for removing the influence of speckle noise on image gray values. In a theoretical analysis, speckle noise of different intensities is added to images, and a suitable denoising method is developed to restore the image gray level. This method can be used to reduce the influence of speckle noise on the gray values of PSi microarray images to improve the accuracy of detection and increase detection sensitivity. In experiments, the method is applied to detect refractive index changes in PSi microcavity images, and a good linear relationship between the gray level change and the refractive index change is obtained. In addition, the algorithm is applied to a PSi microarray image, and good results are obtained.

A fully automatic computer-aided diagnosis system for hepatocellular carcinoma using convolutional neural networks

The cancer of liver, which is the leading cause of cancer death, is commonly diagnosed by comparing the changes of gray level of liver tissue in the different phases of the patient's CT images. To aid the doctor in reducing misdiagnosis or missed diagnosis, a fully automatic computer-aided diagnosis (CAD) system is proposed to diagnose hepatocellular carcinoma (HCC) using convolutional neural network (CNN) classifier. The automatic segmentation and classification are two core technologies of the proposed CAD system, which are both realized based on CNN. The segmentation of liver and tumor is implemented by a fully convolutional networks (FCN) based on a fine tuning VGG-16 model with two additional ‘skip structures’ using a weighted loss function which helps to solve the problem of inaccurate tumor segmentation caused by the inevitably unbalanced training data. HCC classification is implemented by a 9-layer CNN classifier, whose input is a 4-channel image data constructed by combining the segmentation result of FCN with the original CT image. A total of 165 venous phase CT images including 46 diffuse tumors, 43 nodular tumors, and 76 massive tumors are used to evaluate the performance of the proposed CAD system. The classification accuracy of CNN classifier for diffuse, nodular and massive tumors are 98.4%, 99.7% and 98.7% respectively, which are significantly improved in contrast with the traditional feature-based ANN and SVM classifiers. The proposed CAD system, which is unaffected by the difference of preprocessing method and feature type, is proved satisfactory and feasible by the test set.

Local binary pattern-based discriminant graph construction for dimensionality reduction with application to face recognition

Graph construction has attracted increasing interest in recent years due to its key role in many dimensionality reduction (DR) algorithms. On the other hand, our previous study shows that the Local-Binary-Pattern Image (LBPI) representation is a more powerful discriminant and is invariant to monotonic gray level changes. Here, we attempt to construct a discriminant graph for DR in the LBPI representation space. We call the graph the Local-Binary-Image Discriminant (LBID) graph and further incorporate the LBID graph into the Locality Preserving Projection (LPP) to develop an enhanced algorithm - Local Binary Image Discriminant Preserving Projection (LBIDPP). Meanwhile, we also construct a Local-Binary-Histogram (LBH) graph in LBP histogram space and obtain the Local Binary Histogram Preserving Projection (LBHPP) algorithm and compare these to the LBID graph and LBIDPP. It is worth noting that LBIDPP is not a simple combination of the two feature extractions LBP and LPP, i.e., LBP + LPP. LBIDPP inherits the attractive properties of the LBP and LPP. The experiments on face recognition validate the effectiveness and feasibility of the LBID graph and LBIDPP.

Comparison of Selected Shading Correction Methods

Abstract Shade effect is a defect of the images very often invisible for human vision perception but may cause difficulties in proper image processing and object detection especially if the aim of the task is to proceed detection and quantitative analysis of the objects. There are several methods in image processing systems or presented in the literature, however some of them introduce unexpected changes in the images, what may interfere the final quantitative analysis. In order to solve this problem, authors proposed a new method for shade correction, which is based on simulation of the image background based on analytical methods which return pixel values representing smooth grey level changes. Comparison of the effects of correction by applying standard methods and the method proposed are presented.