Inverse Moment Research Articles

Solution of Partial Derivative Equations of Poisson and Klein-Gordon with Neumann Conditions as a Generalized Problem of Two-Dimensional Moments

It will be shown that finding solutions from the Poisson and Klein-Gordon equations under Neumann conditions are equivalent to solving an integral equation, which can be treated as a generalized two-dimensional moment problem over a domain that is considered rectangular. The method consists to solve the integral equation numerically using the two-dimensional inverse moments problem techniques. We illustrate the different cases with examples.

Analysis of internet of things malware using image texture features and machine learning techniques

Threats emanating from Internet of Things (IoT) malicious software are fast evolving and complex phenomena. Unlike conventional networks, Internet of things have unique attributes such as heterogeneity of devices, high scalability and diverse architectures that makes its malware analysis complex. In this paper, we propose an approach for analyzing and classifying IoT malware using Haralick image texture features and machine learning classifiers namely K-nearest neighbor(KNN), naïve Bayes (NB) and random forest(RF). A binary file (malicious or benign) is converted to a gray scale image. The gray level co-occurence matrix (GLCM) is computed on each of the extracted image. On the basis of these GLCM parameters, five Haralick features namely angular second moment, entropy, contrast, inverse different moment and correlation are calculated. Finally, these Haralick texture features are used to perform malware classification using random forest, naïve Bayes and K-nearest neighbor. Experimental results shows that our approach obtains 95% accuracy for Random Forest, 89% for naïve Bayes and 80% for K-nearest neighbor classifiers. Overall, use of texture feature can realize a low computational and platform independent classification scheme for IoT malware.

Application of Fractal and Gray-Level Co-Occurrence Matrix Indices to Assess the Forest Dynamics in the Curvature Carpathians—Romania

The mountain ecosystems face significant damage from deforestation and environmental forest changes. We investigated the evolution of tree types of cover areas, deforested areas and total deforested areas from Curvature Carpathians using Gray-Level Co-occurrence Matrix and fractal analysis. The forest dynamics mapping was one of the main objectives of this study and it was carried out using multiple fractal and GLCM indices. We approached the analysis of satellite forest images by calculation of four fractal indices such as Pyramid dimension, Cube Counting Dimension, Fractal Fragmentation-Compaction Index and Tug-of-War lacunarity. We also calculated fractal dimension because it is an index of complexity comparing how the detail in a pattern changes with the scale at which it is measured. Fractal dimension is useful for estimation of irregularity or roughness of fractal and natural objects that do not conform to Euclidian geometry. While the fractal dimension quantifies how much space is occupied, the Tug-of-War lacunarity complements fractal dimension with its ability to quantify how space is occupied. Analysis was further supplemented by the Gray-Level Co-occurrence Matrix analysis because it quantifies spatial probability distributions of gray level values between pixel pairs within an image. The calculated Gray-Level Co-occurrence Matrix features included Angular Second Moment, Contrast, Correlation, Inverse Difference Moment and Entropy. Such comprehensive analysis has the advantage of combining fractal analysis that extracts quantitative information about the morphological complexity of the image with the spatial distribution of the gray pixel intensities as calculated by the co-occurrence features provided by Gray-Level Co-occurrence Matrix. Evolution of deforested areas, expansion of agricultural land and the increased demand for quality timber have affected the forests ecosystems and, the regional sustainable development of local communities.

Influence of Wave Polarization on the Texture Characteristics of Objects in Radar Images

The influence of wave polarization on texture parameters of objects in radar images (RIs) is examined by calculations of the second-order statistics (the fractal dimension and the Haralick textural features) on the basis of polarimetric data acquired with the Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR) system for two wavelengths of the C- and L-ranges. The variance analysis of the samples of fractal dimension values for natural forested and water objects have revealed no influence of polarization on the values before the speckle filtering; however, after speckle filtering, statistically valid differences have been found in the mean values of some pairs of samples. For sample pairs of the copolarizations VV–HH and cross polarizations HV–VH, a random nature of the differences observed in the mean fractal dimension values has been shown. This conclusion is also true for the Haralick textural features of a water object—the contrast, inverse moment, and entropy. Statistically valid differences in the mean values of the Haralick textural features for urban and water objects have been revealed both before and after speckle filtering. For a forested object, variance analysis shows that the polarization-dependent differences in the mean values of the textural feature samples are statistically unreliable.

Relations for Moments of Dual Generalized Order Statistics for a New Inverse Kumaraswamy Distribution

The inverse Kumaraswamy distribution has been presented as a new distribution. The recurrence relation for moments of dual generalized order statistics has been presented for the new inverse Kumaraswamy distribution. These include the recurrence relations for single, inverse, product and ratio moments of dual generalized order statistics for the new inverse Kumaraswamy distribution. Special cases of the recurrence relations have also been obtained.

Computer vision for purity, phenol, and pH detection of Luwak Coffee Green Bean

Computer vision as a non-invasive bio-sensing method provided opportunity to detect purity, total phenol, and pH in Luwak coffee green bean. This study aimed to obtain the best Artificial Neural Network (ANN) model to detect the percentage of purity, total phenol, and pH on Luwak coffee green bean by using color features (red-green-blue, gray, hue-saturation-value, hue-saturation-lightness, L*a*b*), and Haralick textural features with color co-occurrence matrix including entropy, energy, contrast, homogeneity, sum mean, variance, correlation, maximum probability, inverse difference moment, and cluster tendency. The best ANN structure was (5 inputs; 30 nodes in hidden layer 1; 40 nodes in hidden layer 2; and 3 outputs) which had training mean square error (MSE) of 0.0085 and validation MSE of 0.0442.

Prediction of VMAT delivery accuracy with textural features calculated from fluence maps

BackgroundComprehensively textural feature performance test from volumetric modulated arc therapy (VMAT) fluences to predict plan delivery accuracy.MethodsA total of 240 VMAT plans for various treatment sites were analyzed, with Trilogy and TrueBeam STx systems. Fluence maps superposed fluences at each control point per plan. The textural features were the angular second moment (ASM), inverse difference moment (IDM), contrast, variance, correlation, and entropy, calculated from fluence maps using three displacement distances. Correlation analysis of textural feature performance as predictors of VMAT delivery accuracy used global gamma passing rates with MapCHECK2 and ArcCHECK dosimeters, and mechanical delivery errors calculated from machine log files.ResultsSpearman’s rank correlation coefficients (r) of the ASM (d = 10) to the gamma passing rates with 1%/2 mm using the MapCHECK2 were 0.358 and 0.519, respectively (p < 0.001). For the ArcCHECK, they were 0.273 (p = 0.001) and 0.259 (p = 0.009), respectively. The r-values of the ASM (d = 10) to the Trilogy and TrueBeam STx MLC errors were − 0.843 and − 0.859, respectively (p < 0.001), and those to the MU delivery errors were − 0.482 and − 0.589, respectively (p < 0.001). The ASM (d = 10) showed better performance in predicting VMAT delivery accuracy.ConclusionsThe ASM (d = 10) calculated from VMAT plan fluence maps were strongly correlated with global gamma passing rates and MLC delivery errors, and can predict VMAT delivery accuracy.

Predictive value of 18F-FDG PET conventional parameters and radiomics features for invasive breast cancer patients with uncertain HER2 expression by immunohistochemistry

Objective To explore the predictive value of 18F-fluorodeoxyglucose (FDG) PET conventional parameters and radiomics features for human epidermal growth factor receptor 2 (HER2) expression which was uncertain by immunohistochemistry (IHC) detection in invasive breast cancer. Methods From April 2012 to December 2017, 76 patients (all were females, age: (50.8±10.9) years) with invasive breast cancer and with uncertain HER2 expression by IHC in Tianjin Medical University Cancer Institute and Hospital were enrolled retrospectively. The 18F-FDG PET/CT images before treatment were reviewed and the expression of HER2 were confirmed by fluorescence in situ hybridization (FISH). The tumor lesions were manually outlined, and the radiomics features from PET images were extracted. Wilcoxon test was used to determine whether there was difference in PET conventional metabolic parameters (maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG)) and radiomics features between HER2-negative and HER2-positive groups. The receiver operating characteristic (ROC) curve analysis was used to compare the predictive efficacy of PET radiomics features for HER2 expression. Results There were 41 HER2-positive patients and 35 HER2-negative patients. No significant differences in PET conventional metabolic parameters between different HER2 expression groups were observed (U values: from -1.598 to 1.551, all P>0.05). A total of 38 PET radiomics features were extracted, and there were significant differences in gray mean, correlation, contrast, inertia, and inverse different moments between 2 groups(U values: from -2.413 to 2.527, all P<0.05). The area under the curve of the above 5 parameters for prediction of HER2 expression were 0.643, 0.638, 0.647, 0.644 and 0.643, respectively, and the contrast was the best parameter. Conclusions PET radiomics features can effectively identify HER2 expression in patients with invasive breast cancer to some extent, and the contrast may be the best. Conventional metabolic parameters have limited predictive value. Key words: Breast neoplasms; Receptor, epidermal growth factor; Immunohistochemistry; Positron-emission tomography; Tomography, X-ray computed; Deoxyglucose; Radiomics

A Prognostic Model Integrating PET-Derived Quantitative Parameters and Image Texture Analyses Using Radiomics in a Large Prospective Phase III Trial, GOYA

A Prognostic Model Integrating PET-Derived Quantitative Parameters and Image Texture Analyses Using Radiomics in a Large Prospective Phase III Trial, GOYA

Efficient Evaluation of Molecular Electrostatic Potential in Large Systems

An algorithm for the efficient computation of molecular electrostatic potential is reported. It is based on the partition/expansion of density into (pseudo) atomic fragments with the method of Deformed Atoms in Molecules, which allows to compute the potential as a sum of atomic contributions. These contributions are expressed as a series of irregular spherical harmonics times effective multipole moments and inverse multipole moments, including short-range terms. The problem is split into two steps. The first one consists of the partition/expansion of density accompanied by the computation of multipole moments, and its cost depends on the size of the basis set used in the computation of electron density within the Linear Combination of Atomic Orbitals framework. The second one is the actual computation of the electrostatic potential from the quantities calculated in the first step, and its cost depends on the number of computation points. For a precision in the electrostatic potential of six decimal figures, the algorithm leads to a dramatic reduction of the computation time with respect to the calculation from electron density matrix and integrals involving basis set functions.

Prediction of white matter hyperintensities progression based on radiomics of whole-brain MRI: a study of risk factors

Objective To explore the risk factors of predicting white matter hyperintensities progression based on radiomics of MRI of whole-brain white matter. Methods The imaging and clinical data of 152 patients with white matter hyperintensities admitted to Zhejiang People′s Hospital from March 2014 to October 2018 were retrospectively analyzed. The whole brain white matter on baseline T1WI images of each patient were segmented by SPM12 software package, and images of white matter were imported into AK software for texture feature extraction and dimensionality reduction. At last, least absolute shrinkage and selection operator(LASSO) was used to calculate the score of radiomics signature of each patient. According to the improved Fazekas scale, patients with WMH progression were divided into three groups: any white matter hyperintensities (AWMH), periventricular white matter hyperintensities (PWMH) and deep white matter hyperintensities (DWMH). Statistical differences of clinical factors and radiomics signature between WMH progression subgroups and non-progression subgroups were compared with independent sample t test or Mann-Whitney U test, and Univariate logistic regression was used to select independent clinical risk factors and multivariate logistic regression along with imaging omics tags were used to construct predictive models, which was evaluated by ROC curve. And the correlation between the clinical indicators of independent risk factors and the texture feature of radiomics signature was analyzed. Results The efficacy of the model for the detection for AWMH progression, PWMH progression and DWMH progression was 0.818,0.778 and 0.824, respectively. Age is an independent risk factor for AWMH progression and DWMH progression[OR(95%CI), 4.776(2.152-10.601) vs. 3.851(1.101-8.245); P<0.05]. BMI is an independent risk factor for DWMH[OR(95%CI), 3.004(1.204-7.370); P<0.05], and hyperlipidemia is an independent risk factor for AWMH and PWMH[OR(95%CI), 4.062(1.834-8.998) vs. 3.549(1.666-7.563); P<0.05]. In AWMH subgroup, Surface Area was negatively correlated with age and low density lipoprotein(LDL) (r=-0.401, -0.312), and Inverse Difference Moment_ALLDirection_offset 1_SD was negatively correlated with age(r=-0.412). In PWMH subgroup, Compactness 1 was negatively correlated with LDL(r=-0.198), and Inverse Difference Moment_angle 0_offset 7 was positively correlated with LDL(r=0.252). In DWMH subgroup, LongRunEmphasis_ALLDirection_offset 7 was negatively correlated with age(r=-0.322), and GLCMEntropy_angle0_offset 4 was negatively correlated with age(r=-0.278). GLCMEntropy_AllDirection_offset4 was negatively correlated with body mass index(r=-0.514). Conclusion Radiomics based on whole-brain white matter MR imaging can predict WMH progression and identify the risk factors in high-risk populations, thus providing early additional information to conventional magnetic resonance imaging to predict WMH progression. Key words: Radiomics; White matter hyperintensities; Risk factors; Magnetic resonance imaging

Texture Features of Magnetic Resonance Images: an Early Marker of Post-stroke Cognitive Impairment.

Stroke is frequently associated with delayed, long-term cognitive impairment (CI) and dementia. Recent research has focused on identifying early predictive markers of CI occurrence. We carried out a texture analysis of magnetic resonance (MR) images to identify predictive markers of CI occurrence based on a combination of preclinical and clinical data. Seventy-two-hour post-stroke T1W MR images of 160 consecutive patients were examined, including 75 patients with confirmed CI at the 6-month post-stroke neuropsychological examination. Texture features were measured in the hippocampus and entorhinal cortex and compared between patients with CI and those without. A correlation study determined their association with MoCA and MMSE clinical scores. Significant features were then combined with the classical prognostic factors, age and gender, to build a machine learning algorithm as a predictive model for CI occurrence. A middle cerebral artery transient occlusion model was used. Texture features were compared in the hippocampus of sham and lesioned rats and were correlated with histologically assessed neural loss. In clinical studies, two texture features, kurtosis and inverse difference moment, differed significantly between patients with and without CI and were significantly correlated with MoCA and MMSE scores. The prediction model had an accuracy of 88 ± 3%. The preclinical model revealed a significant correlation between texture features and neural density in the hippocampus contralateral to the ischemic area. These preliminary results suggest that texture features of MR images are representative of neural alteration and could be a part of a screening strategy for the early prediction of post-stroke CI.

Delay-Based Reservoir Computing Using Multimode Semiconductor Lasers: Exploiting the Rich Carrier Dynamics

Delay-based reservoir computing is a neuromorphiccomputing technique employing a single nonlinear node, in this case a semiconductor laser subjected to a delayed optical self-feedback. This implementation of reservoir computing, while being versatile and highly performant, is limited in processing speed by the delay length. In an effort to reduce the required delay line length, while maintaining the same strong performance, we investigate if it is possible to distribute the processing power over more than one longitudinal mode. We therefore have numerically analysed a rate equation model of a semiconductor laser with two longitudinal modes, delayed optical feedback and optical injection. Our modelling takes into account the carrier inversion moments that represent carrier gratings formed in the cavity during operation. This allows us to study the importance of modal interaction via carrier gratings and how this effects the computational power. We show that the setup with a dual-mode laser and an optimized reservoir parameter space, can reduce the delay-line fourfold in comparison with a single-mode laser setup.

Analisa Kehandalan Ekstraksi Fitur Ordo Kedua terhadap Ordo Pertama untuk Identifikasi Ciri Berbasis Tekstur Warna

The feature extraction method is done by first converting RGB images into grayscale images. Based on the grayscale image, two different processing methods are carried out, namely first order feature extraction and second order feature extraction. The extraction features of the first order used 5 characteristic parameters, namely Mean, Variance, Skewness, Kurtosis, and Entropy, while the extraction features of the second order used 6 characteristic parameters, namely Angular Second Moment, Contrast, Correlation, Standard Deviation, Inverse Difference Moment and Homogenity. The 11 characteristic parameters will then be classified using the LVQ artificial neural network method to find the final weight used as the reference weight for characteristics identification based on color texture. In this research, 30 image samples were used (15 image samples in category A and 15 image samples in category B) which were divided into 16 image samples for training and 14 image samples for testing. The results of the research analysis show that the second order feature extraction method is more reliable than the first order feature extraction method.

Single-pixel compressive imaging based on the transformation of discrete orthogonal Krawtchouk moments.

A single-pixel compressive imaging technique that uses differential modulation based on the transformation of discrete orthogonal Krawtchouk moments is proposed. In this method, two sets of Krawtchouk basis patterns are used to differentially modulate the light source, then the Krawtchouk moments of the target object are acquired from the light intensities measured by a single-pixel detector. The target image is reconstructed by applying an inverse Krawtchouk moment transform represented in the matrix form. The proposed technique is verified by both computational simulations and laboratory experiments. The results show that this technique can retrieve an image from compressive measurements and the real-time reconstruction. The background noise can be removed by the differential measurement to realize the excellent image quality. Moreover, the proposed technique is especially suitable for the single-pixel imaging application that requires the extraction of the characteristics at the region-of-interest.

Muscle Synergies Modify Optimization Estimates of Joint Stiffness During Walking.

Because of its simplicity, static optimization (SO) is frequently used to resolve the muscle redundancy problem (i.e., more muscles than degrees-of-freedom (DOF) in the human musculoskeletal system). However, SO minimizes antagonistic co-activation and likely joint stiffness as well, which may not be physiologically realistic since the body modulates joint stiffness during movements such as walking. Knowledge of joint stiffness is limited due to the difficulty of measuring it experimentally, leading researchers to estimate it using computational models. This study explores how imposing a synergy structure on the muscle activations estimated by optimization (termed "synergy optimization," or SynO) affects calculated lower body joint stiffnesses during walking. By limiting the achievable muscle activations and coupling all time frames together, a synergy structure provides a potential mechanism for reducing indeterminacy and improving physiological co-activation but at the cost of a larger optimization problem. To compare joint stiffnesses produced by SynO (2-6 synergies) and SO, we used both approaches to estimate lower body muscle activations and forces for sample experimental overground walking data obtained from the first knee grand challenge competition. Both optimizations used a custom Hill-type muscle model that permitted analytic calculation of individual muscle contributions to the stiffness of spanned joints. Both approaches reproduced inverse dynamic joint moments well over the entire gait cycle, though SynO with only two synergies exhibited the largest errors. Maximum and mean joint stiffnesses for hip and knee flexion in particular decreased as the number of synergies increased from 2 to 6, with SO producing the lowest joint stiffness values. Our results suggest that SynO increases joint stiffness by increasing muscle co-activation, and furthermore, that walking with a reduced number of synergies may result in increased joint stiffness and perhaps stability.

Bayesian Model Selection Approach to Multiple Change-Points Detection with Non-Local Prior Distributions

We propose a Bayesian model selection (BMS) boundary detection procedure using non-local prior distributions for a sequence of data with multiple systematic mean changes. By using the non-local priors in the BMS framework, the BMS method can effectively suppress the non-boundary spike points with large instantaneous changes. Further, we speedup the algorithm by reducing the multiple change points to a series of single change point detection problems. We establish the consistency of the estimated number and locations of the change points under various prior distributions. From both theoretical and numerical perspectives, we show that the non-local inverse moment prior leads to the fastest convergence rate in identifying the true change points on the boundaries. Extensive simulation studies are conducted to compare the BMS with existing methods, and our method is illustrated with application to the magnetic resonance imaging guided radiation therapy data.

Application of image analysis technique to determine cleaning of ohmic heating system for milk.

Cleaning of equipment is one of the major areas of concern in food industry. Image analysis technique was used to assess the cleaning effectiveness and optimize the CIP protocol for ohmic heating setup. Process parameters selected for optimization of cleaning were caustic concentration (1.0, 1.5, 2.0 and 2.5%), caustic temperature (70, 75, 80 and 85°C), acid concentration (0.00, 0.25, 0.5 and 0.75%), and acid temperature (40, 50, 60 and 70°C). Time for caustic treatment was varied from 5 to 20min at an interval of 5min, while time acid treatment was kept at a constant of 10min. Taguchi orthogonal array design was used generate different combinations of acid and alkali concentration and temperature. Images of ohmic heating plates were taken before and after the cleaning procedure. MATLAB program was developed to analyze and extract Gray-Level Co-occurrence (GLCM) matrix properties from the image. Optimized combination was selected based on the highest value of desirability factor among all the experimental set of trials. Treatment with 1.5% caustic concentration at 70°C for 5min followed by 0.5% nitric acid concentration at 60°C was found optimum effective CIP of the heating plates used in ohmic heating setup. GLCM properties correlation, cluster prominence, cluster shade, entropy, homogeneity and inverse difference moment normalized were found suitable for analysis of cleaning effectiveness and optimization of the CIP protocol.

Study of Squeeze Effect and Worm of Rollers Affecting the Inverse Bending on Straighten Steel Pipes

In the manufacturing process of steel pipes, bending or torsion deformation is most usual happened by the variation of temperature, external extension or compression, inner residual stress. The straighten quality is thus out of standard. Tilt roller straightens machine is thus become required equipment in the straightening process. Rollers can generate vertical and radial bending continuously in order to achieve enough straightness. These two direction bending moments have to good match each other. Quality problems, such as surface wear scar, sink in or shorten the length, can thus be avoided. Curve geometry of rollers, squeezing effect and straighten inverse bending moment calculation were considered in the study. Optimal bending stress cannot be lower than 70 % yielding stress and contacting length between roller and pipe cannot be lower than 65% curve length of the roller. The worm of rollers affect contact situation and the tilt angle of roller have to rearrange.

Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis

PurposeGlioblastomas (GBM) and metastases are the most frequent malignant brain tumors in the adult population. Their presentation on conventional MRI is quite similar, but treatment strategy and prognosis are substantially different. Even with advanced MR techniques, in some cases diagnostic uncertainty remains. The main objective of this study was to determine whether fractal, texture, or both MR image analyses could aid in differentiating glioblastoma from solitary brain metastasis. MethodIn a retrospective study of 55 patients (30 glioblastomas and 25 solitary metastases) who underwent T2W/SWI/CET1 MRI, quantitative parameters of fractal and texture analysis were estimated, using box-counting and gray level co-occurrence matrix (GLCM) methods. ResultsAll five GLCM parameters obtained from T2W images showed significant difference between glioblastomas and solitary metastases, as well as on CET1 images except correlation (SCOR), contrary to SWI images which showed different values of two parameters (angular second moment-SASM and contrast-SCON). Only three fractal features (binary box dimension-Dbin, normalized box dimension-Dnorm and lacunarity-λ) measured on T2W and Dnorm measured on CET1 images significantly differed GBMs from solitary metastases. The highest sensitivity and specificity were obtained from inverse difference moment (SIDM) on T2W and SIDM on CET1 images, respectively. Combination of several GLCM parameters yielded better results. The processing of T2W images provided the most significantly different parameters between the groups, followed by CET1 and SWI images. ConclusionsComputational-aided quantitative image analysis may potentially improve diagnostic accuracy. According to our results texture features are more significant than fractal-based features in differentiation glioblastoma from solitary metastasis.