Three-dimensional Histogram Research Articles

Three-dimensional iodine mapping quantified by dual-energy CT for predicting programmed death-ligand 1 expression in invasive pulmonary adenocarcinoma

We examined the association between texture features using three-dimensional (3D) io-dine density histogram on delayed phase of dual-energy CT (DECT) and expression of programmed death-ligand 1 (PD-L1) using immunostaining methods in non-small cell lung cancer. Consecutive 37 patients were scanned by DECT. Unenhanced and enhanced (3 min delay) images were obtained. 3D texture analysis was performed for each nodule to obtain 7 features (max, min, median, mean, standard deviation, skewness, and kurtosis) from iodine density mapping and extracellular volume (ECV). A pathologist evaluated a tumor proportion score (TPS, %) using PD-L1 immunostaining: PD-L1 high (TPS ≥ 50%) and low or negative expression (TPS < 50%). Associations between PD-L1 expression and each 8 parameter were evaluated using logistic regression analysis. The multivariate logistic regression analysis revealed that skewness and ECV were independent indicators associated with high PD-L1 expression (skewness: odds ratio [OR] 7.1 [95% CI 1.1, 45.6], p = 0.039; ECV: OR 6.6 [95% CI 1.1, 38.4], p = 0.037). In the receiver-operating characteristic analysis, the area under the curve of the combination of skewness and ECV was 0.83 (95% CI 0.67, 0.93) with sensitivity of 64% and specificity of 96%. Skewness from 3D iodine density histogram and ECV on dual energy CT were significant factors for predicting PD-L1 expression.

Exploring the effect of eccentricity on geometry nuclear collisions by Glauber Monte Carlo simulation

This paper will analyze different properties of a nuclear collision and their correlation with the geometry of collision using the Glauber Monte-Carlo based calculations. Among all properties, the eccentricity is the main focus of this research. In order to investigate its impact on collision parameters, three-dimensional histograms are created, using given collision parameters, under different categories of numbers of participant nucleons. Comparisons between the histograms allow the exploration of connections between this property and the geometries of lead nuclear collisions, namely, the ellipticity of the collision of the two lead nuclei as eccentricity increases from 0 to 1. Our result is intuitive. The ellipticity of the collision has a positive correlation with the eccentricity, as when the difference between the number of participant nucleons on the L1 and L2 axes increases with eccentricity. The number of nucleons participating in the collision also increases with eccentricity. These are all coherent with past studies conducted in this field.

Evaluación del modelo de elevación digital de la misión topográfica de radar en transbordador (SRTM) en la macrocuenca del Papaloapan, Mexico, usando LiDAR como referencia

The Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM) is evaluated using a LiDAR DEM from INEGI as benchmark in the Papaloapan Basin (~58 000 km2) in Mexico. Three representative regions are selected: 1) a hilly region with strong slopes and elevations over 3 000 m; 2) a transitional region with relatively smoother slopes and elevations around 1 000 m, and 3) a floodplain with flat terrain and elevations below 100 m. The straight comparison of both datasets shows very similar elevation values at the hilly and transitional regions. However, in the floodplain, the relationship has a parabolic shape, and errors are relatively higher, in terms of the elevation range. This is probably due to systematic errors in SRTM being very close to the actual low elevations. Maps of errors suggest strong association with water bodies and the aspect. For example, in the transitional region, most negative errors are found on slopes facing east, while positive errors are found on slopes facing west. Three-dimensional histograms of errors vs. topographic features (elevation, slope, and aspect) are estimated. The histograms suggest a systematic error, which means SRTM could be improved with a simple calibration at least in these cases. Evaluations of public DEMs from different sources in Mexico are considered necessary for the identification of their strengths and weaknesses. We believe these evaluations might provide the grounds for the creation of improved MEDs in the future either by either a simple calibration or through composite MEDs from multiple sources.

Physical property characterization of rocks in the Bayan Obo REE-Nb-Fe deposit, China

Bayan Obo ore deposit is the world’s largest rare-earth element (REE) resource, the second largest niobium (Nb) resource, and also a significant iron (Fe) resource in China. Evaluating resource potential for the deposit has become a focus of global interest. Rock-physical properties bridge geophysical exploration and geologic modeling; variation in these parameters is necessary for successful geophysical application. REE, Nb, iron, and potassium are mainly hosted in dolomite and slate of the Bayan Obo Group, and REE mineralization is genetically associated with carbonatite. Three physical properties (resistivity, polarizability, and magnetic susceptibility [MS]) of iron ore, slate, dolomite, and carbonatite dike outcrop samples at Bayan Obo are measured and statistically analyzed using 3D reconstruction, 1D/2D/3D kernel density estimation, scatterplot matrix, 3D histogram, and Pearson- and maximum information coefficient-based correlation analysis. It is evident that iron ore, iron-mineralized fine-grained dolomite, and iron-mineralized slate are mainly of low resistivity, and iron ore and iron-mineralized fine-grained dolomite have high MS. MS favorably distinguishes iron ore from slate; MS and resistivity distinguish between iron-mineralized fine-grained dolomite and carbonatite dikes. The physical properties and whole-rock geochemistry (major and trace elements) jointly demonstrate that MS of iron ore, slate, and dolomite is positively correlated with TFe2O3 content, polarizability is correlated with TFe2O3, SiO2 content is correlated with K2O, and resistivity is correlated with MS and polarizability, respectively. Resistivity of iron ore and dolomite is negatively correlated with TFe2O3 content. Resistivity of iron ore is negatively correlated with TFe2O3, total REE ([Formula: see text]), and Nb, respectively, and correlated with thorium. The methods used have intuitive visual expression and reflect the characteristics of the physical properties and their correlation with the mineralogical composition. The results will be beneficial for determining the geometry of ore-hosting rock masses and providing crucial evidence for the resource evaluation.

Exotic collective excitation patterns in triaxially deformed &lt;sup&gt;131&lt;/sup&gt;Ba

&lt;sec&gt;In the last two decades, several unique phenomena in triaxially deformed nuclei, such as chiral doublet bands and wobbling motion have been revealed. Up to now, there are still many open questions which require further experimental and theoretical studies. To explore the collective motion in &lt;sup&gt;131&lt;/sup&gt;Ba, an experiment was performed using the XTU Tandem accelerator in the Legnaro laboratory, Italy. High-spin states of &lt;sup&gt;131&lt;/sup&gt;Ba have been populated via the heavy-ion fusion-evaporation &lt;sup&gt;122&lt;/sup&gt;Sn(&lt;sup&gt;13&lt;/sup&gt;C, 4n) reaction. &lt;i&gt;γ&lt;/i&gt;-rays, charged particles and neutrons emitted from the residues were detected by the GALILEO array, EUCLIDES silicon ball, and the Neutron Wall, respectively. A total of 1.2&lt;inline-formula&gt;&lt;tex-math id="M12"&gt;\begin{document}$ \times $\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="11-20240212_M12.jpg"/&gt;&lt;graphic specific-use="print" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="11-20240212_M12.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;10&lt;sup&gt;9&lt;/sup&gt; triple- or higher-fold events were collected by the GALILEO data acquisition system. The &lt;i&gt;γ&lt;/i&gt;-&lt;i&gt;γ&lt;/i&gt;-&lt;i&gt;γ&lt;/i&gt; coincidence events were sorted into a three-dimensional histogram (cube) and the analysis was carried out with the RADWARE and GASPWARE software packages.&lt;/sec&gt;&lt;sec&gt;Through analysis of the coincidences between &lt;i&gt;γ&lt;/i&gt;-rays, the most comprehensive level schemes of &lt;sup&gt;131&lt;/sup&gt;Ba to date was deduced from the present work. The extended level-scheme consists of 15 rotational bands, and newly observed transitions are marked in red. Three nearly degenerate pairs of doublet bands (Band 3–8) are identified in &lt;sup&gt;131&lt;/sup&gt;Ba. Two pairs of chiral doublets (Band 3–6) with configuration &lt;inline-formula&gt;&lt;tex-math id="M20"&gt;\begin{document}$ {\textit{\pi}}h_{11/2}(g_{7/2},d_{5/2}){\otimes}{\nu}h_{11/2} $\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="11-20240212_M20.jpg"/&gt;&lt;graphic specific-use="print" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="11-20240212_M20.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; are interpreted as a set of pseudospin-chiral quartet bands. The quartet bands are fed by another pair of chiral doublet bands (Band 7–8) built on a &lt;inline-formula&gt;&lt;tex-math id="M21"&gt;\begin{document}$ {\textit{\pi}}h^2_{11/2}{\otimes}{\nu}h_{11/2} $\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic specific-use="online" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="11-20240212_M21.jpg"/&gt;&lt;graphic specific-use="print" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="11-20240212_M21.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt; configuration via a series of enhanced E1 transitions. We extracted the energy displacement &lt;i&gt;δ&lt;/i&gt;E and the B(E1)/B(E2) branching ratios between the positive-parity band 3 and the negative-parity band 7 in &lt;sup&gt;131&lt;/sup&gt;Ba and in comparison with those in &lt;sup&gt;124&lt;/sup&gt;Ba, &lt;sup&gt;224&lt;/sup&gt;Th, &lt;sup&gt;133&lt;/sup&gt;Ce and &lt;sup&gt;135&lt;/sup&gt;Nd. The energy displacement &lt;i&gt;δ&lt;/i&gt;E and the B(E1)/B(E2) branching ratios in &lt;sup&gt;131&lt;/sup&gt;Ba are comparable with those in &lt;sup&gt;124&lt;/sup&gt;Ba but deviate appreciably from those in &lt;sup&gt;224&lt;/sup&gt;Th which has been reported to have stable octupole deformation. The results indicate the existence of octupole correlations in &lt;sup&gt;131&lt;/sup&gt;Ba without stable octupole deformation. A new rotational band (Band 10) discovered in the low-spin region exhibits a level structure similar to a wobbling band. Assuming it as a wobbling band, the wobbling frequency was extracted and compared with other reported wobbling bands in the neighboring nuclei. The wobbling frequency of this band decreases with increasing angular momentum, and even exhibits negative value at the highest spin. Considering that the wobbling phonon should contribute a positive amount to the excitation energy, this band is unlikely to be explained by this mechanism. The band may originate from other collective excitation mechanisms such as &lt;i&gt;γ&lt;/i&gt; vibration. The newly identified rotational band (Band 9) composed of M1 transitions is tentatively assigned as a magnetic rotational band through a systematic analysis of the level structure. Finally, the configurations of other 4 bands, Band 12-15, are also suggested based on previous researches and the extracted quasiparticle alignments.&lt;/sec&gt;

Is deeper always better? Evaluating deep learning models for yield forecasting with small data

Predicting crop yields, and especially anomalously low yields, is of special importance for food insecure countries. In this study, we investigate a flexible deep learning approach to forecast crop yield at the provincial administrative level based on deep 1D and 2D convolutional neural networks using limited data. This approach meets the operational requirements—public and global records of satellite data in an application ready format with near real time updates—and can be transferred to any country with reliable yield statistics. Three-dimensional histograms of normalized difference vegetation index (NDVI) and climate data are used as input to the 2D model, while simple administrative-level time series averages of NDVI and climate data to the 1D model. The best model architecture is automatically identified during efficient and extensive hyperparameter optimization. To demonstrate the relevance of this approach, we hindcast (2002–2018) the yields of Algeria’s three main crops (barley, durum and soft wheat) and contrast the model’s performance with machine learning algorithms and conventional benchmark models used in a previous study. Simple benchmarks such as peak NDVI remained challenging to outperform while machine learning models were superior to deep learning models for all forecasting months and all tested crops. We attribute the poor performance of deep learning to the small size of the dataset available.

Usefulness of Three-Dimensional Iodine Mapping Quantified by Dual-Energy CT for Differentiating Thymic Epithelial Tumors.

Background: Dual-energy CT has been reported to be useful for differentiating thymic epithelial tumors. The purpose is to evaluate thymic epithelial tumors by using three-dimensional (3D) iodine density histogram texture analysis on dual-energy CT and to investigate the association of extracellular volume fraction (ECV) with the fibrosis of thymic carcinoma. Methods: 42 patients with low-risk thymoma (n = 20), high-risk thymoma (n = 16), and thymic carcinoma (n = 6) were scanned by dual-energy CT. 3D iodine density histogram texture analysis was performed for each nodule on iodine density mapping: Seven texture features (max, min, median, average, standard deviation [SD], skewness, and kurtosis) were obtained. The iodine effect (average on DECT180s-average on unenhanced DECT) and ECV on DECT180s were measured. Tissue fibrosis was subjectively rated by one pathologist on a three-point grade. These quantitative data obtained by examining associations with thymic carcinoma and high-risk thymoma were analyzed with univariate and multivariate logistic regression models (LRMs). The area under the curve (AUC) was calculated by the receiver operating characteristic curves. p values < 0.05 were significant. Results: The multivariate LRM showed that ECV > 21.47% in DECT180s could predict thymic carcinoma (odds ratio [OR], 11.4; 95% confidence interval [CI], 1.18-109; p = 0.035). Diagnostic performance was as follows: Sensitivity, 83.3%; specificity, 69.4%; AUC, 0.76. In high-risk thymoma vs. low-risk thymoma, the multivariate LRM showed that the iodine effect ≤1.31 mg/cc could predict high-risk thymoma (OR, 7; 95% CI, 1.02-39.1; p = 0.027). Diagnostic performance was as follows: Sensitivity, 87.5%; specificity, 50%; AUC, 0.69. Tissue fibrosis significantly correlated with thymic carcinoma (p = 0.026). Conclusions: ECV on DECT180s related to fibrosis may predict thymic carcinoma from thymic epithelial tumors, and the iodine effect on DECT180s may predict high-risk thymoma from thymoma.

Spatio-temporal modelling with multi-gradient features and elongated quinary pattern descriptor for dynamic facial expression recognition

We propose a new spatio-temporal modelling approach for Dynamic Facial Expression Recognition (DFER). We first convert the domain of the spatial images in the sequence to the gradient of magnitude and angle images at different orientations. Robust gradient components are developed to deal with difficult types of illuminations, such as darkness, by forming the eight edge responses of the Gaussian mask. To describe the dynamic Facial Expression (FE) changes we extend the Elongated Quinary Pattern (EQP) descriptor to encode separately the anisotropic structure of the uniform patterns from Three Orthogonal Planes (TOP) of each gradient sequence. Then each encoded sequence is divided into a stack of block volumes in the XY, XT and YT planes. For each plane, the co-occurrence of histogram features are calculated from each block volume and concatenated together. Simple three-dimensional histogram features are generated by concatenating the histogram features of all planes. A Multi Classifier System (MCS) based on a multi-class Support Vector Machine (SVM) is adopted to combine all scores for the encoded sequences. The proposed approach is evaluated with the challenging MMI and Oulu-CASIA databases with different set-ups and advantage has been shown in terms of generalisation to different databases, together with robustness against difficult pose variations and illumination changes. In terms of Recognition Accuracy (RA), a comparison is established with DFER methods in the literature. A high recognition rate of 79.23% is attained in the case of six classes when applied to the MMI database which surpasses all the state-of-the-art results.

Evaluación del Modelo de Elevación Digital de la Misión Topográfica de Radar en Transbordador (SRTM) en la Macro-Cuenca del Papaloapan, Mexico, usando LiDAR como referencia

The Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM) is evaluated using a LiDAR DEM from INEGI as benchmark in the Papaloapan Basin (~58,000km2) in Mexico. Three representative regions are selected: 1) a hilly region with strong slopes and elevations over 3,000m, 2) a transitional region with relatively smoother slopes and elevations around 1,000m, and 3) a floodplain with flat terrain and elevations below 100m. The straight comparison of both datasets shows very similar elevation values at the hilly and transitional regions. However, in the floodplain, the relationship has a parabolic shape, and errors are relatively higher, in terms of the elevation range. This is probably due to systematic errors in SRTM being very close to the actual low elevations. Maps of errors suggest strong association with water bodies and the aspect. For example, in the transitional region, most negative errors are found on slopes facing east, while positive errors are found on slopes facing west. Three-dimensional histograms of errors vs. topographic features (elevation, slope, and aspect) are estimated. The histograms suggest a systematic error, which means SRTM could be improved with a simple calibration at least in these cases. Evaluations of public DEMs from different sources in Mexico are considered necessary for the identification of their strengths and weaknesses. We believe these evaluations might provide the grounds for the creation of improved MEDs in the future either by either a simple calibration or through composite MEDs from multiple sources.

Evaluation of asphalt mixture micromechanical behavior evolution in the failure process based on Discrete Element Method

Asphalt mixture was a granular material, and traditional mechanical testing methods were difficult to reveal the failure mechanism. To evaluate the micromechanical behavior of asphalt mixture in the failure process, the DEM (Discrete Element Method) model of the uniaxial compression test was established. The virtual aggregate particle database with real morphology properties was constructed, and the linear parallel bond model was used to simulate the adhesion of asphalt. In the DEM model of the asphalt mixture failure process, the movement of asphalt mortar and coarse aggregate was evaluated, and the Discrete Fracture Network (DFN) was proposed to represent the microcrack. Finally, the three-dimensional spherical coordinate histogram was used to analyze the evolution of magnitude and direction of the coarse aggregate skeleton contact force. It found that the movement of coarse aggregate was less than that of asphalt mortar in the process of the uniaxial compression test, and the coarse aggregate movement was affected by its particle size and location. The result also found that although the adhesive and cohesion failure could occur at the same time, the adhesive failure was less than cohesion failure in the early stage of the uniaxial compression test, and the aggregate with larger particle size could reduce the generation of adhesive failure. Compared with the magnitude of coarse aggregate skeleton contact force, the change of direction was the main reason for a decrease in stress when the asphalt mixture specimen exceeded the peak strength.

A Study on Reversible Data Hiding Technique Based on Three-Dimensional Prediction-Error Histogram Modification and a Multilayer Perceptron

In the past few years, with the development of information technology and the focus on information security, many studies have gradually been aimed at data hiding technology. The embedding and extraction algorithms are mainly used by the technology to hide the data that requires secret transmission into a multimedia carrier so that the data transmission cannot be realized to achieve secure communication. Among them, reversible data hiding (RDH) is a technology for the applications that demand the secret data extraction as well as the original carrier recovery without distortion, such as remote medical diagnosis or military secret transmission. In this work, we hypothesize that the RDH performance can be enhanced by a more accurate pixel value predictor. We propose a new RDH scheme of prediction-error expansion (PEE) based on a multilayer perceptron, which is an extensively used artificial neural network in plenty of applications. The scheme utilizes the correlation between image pixel values and their adjacent pixels to obtain a well-trained multilayer perceptron so that we are capable of achieving more accurate pixel prediction results. Our data mapping method based on the three-dimensional prediction-error histogram modification uses all eight octants in the three-dimensional space for secret data embedding. The experimental results of our RDH scheme show that the embedding capacity greatly increases and the image quality is still well maintained.

Gravity versus Magnetic Fields in Forming Molecular Clouds

Abstract Magnetic fields are dynamically important in the diffuse interstellar medium. Understanding how gravitationally bound, star-forming clouds form requires modeling of the fields in a self-consistent, supernova-driven, turbulent, magnetized, stratified disk. We employ the FLASH magnetohydrodynamics code to follow the formation and early evolution of clouds with final masses of 3–8 × 103 M ⊙ within such a simulation. We use the code’s adaptive mesh refinement capabilities to concentrate numerical resolution in zoom-in regions covering single clouds, allowing us to investigate the detailed dynamics and field structure of individual self-gravitating clouds in a consistent background medium. Our goal is to test the hypothesis that dense clouds are dynamically evolving objects far from magnetohydrostatic equilibrium. We find that the cloud envelopes are magnetically supported with field lines parallel to density gradients and flow velocity, as indicated by the histogram of relative orientations and other statistical measures. In contrast, the dense cores of the clouds are gravitationally dominated, with gravitational energy exceeding internal, kinetic, or magnetic energy and accelerations due to gravity exceeding those due to magnetic or thermal pressure gradients. In these regions, field directions vary strongly, with a slight preference toward being perpendicular to density gradients, as shown by three-dimensional histograms of relative orientation.

Multi-resolution 3D-HOG feature learning method for Alzheimer’s Disease diagnosis

Background and Objective: Alzheimer’s Disease (AD) is a progressive irreversible neurodegeneration disease and thus timely identification is critical to delay its progression. Methods: In this work, we focus on the traditional branch to design discriminative feature extraction and selection strategies to achieve explainable AD identification. Specifically, a spatial pyramid based three-dimensional histogram of oriented gradient (3D-HOG) feature learning method is proposed. Both global and local texture changes are included in spatial pyramid 3D-HOG (SPHOG) features for comprehensive analysis. Then a modified wrapper-based feature selection algorithm is introduced to select the discriminative features for AD identification while reduce feature dimensions. Results: Discriminative SPHOG histograms with various resolutions are selected, which can represent the atrophy characteristics of cerebral cortex with promising performance. As subareas corresponding to selected histograms are consistent with clinical experience, explanatory is emphasized and illustrated with Hippocampus. Conclusion: Experimental results illustrate the effectiveness of the proposed method on feature learning based on samples obtained from common dataset and a clinical dataset. The proposed method will be useful for further medical analysis as its explanatory on other region-of-interests (ROIs) of the brain for early diagnosis of AD.

Selective Arterial Embolization of Renal Angiomyolipoma: Efficacy, Tumor Volume Reduction and Complications

Objective: To evaluate the efficacy and complications of selective arterial embolization in renal angiomyolipoma and to identify predictive factors for tumor rupture.Materials and Methods: Overall, 21 patients with 25 renal angiomyolipoma (AML) underwent selective arterial embolization (SAE) between January 2008 and June 2019, comprising 15 cases involving prophylaxis embolization of a tumor &gt;4 cm diameter and 10 involving embolization for a ruptured tumor. Multidetector computed tomography (MDCT) was performed pre- and post-SAE, using the 2D tumor diameter in the ruptured AMLs. Three-dimensional volumetry and density histogram were performed for determining the total tumor volume, fat, and angiomyogenic component reduction in the unruptured AMLs. The predictive factors for tumor rupture, the treatment outcome and complications were analyzed. Results: The clinical success rate was 84% (21/25 cases) and the technical success rate was 96% (24/25 cases). The 3D volume post-SAE within 1-3 months showed a greater decrement of the enhanced angiomyogenic component than the fat component, with median percentages of -62.2% and -18.4%, respectively (p-value = 0.333). Minor complications were post-embolization syndrome (5 case, 20%) and minimal renal infarction (4 cases, 16%). Renal abscesses were the major complications (3 cases, 12%). A factor associated with tumor rupture was the presence of an intra-tumoral aneurysm (p-value &lt; 0.05).Conclusion: SAE is an effective treatment for renal AML with a high technical and clinical success rate and limited complications. Three-dimensional volume measurement and density histogram analysis might be better tools than two-dimensional CT to evaluate post-SAE response, which is crucial for management planning.

An Improved Forest Fire Monitoring Algorithm With Three-Dimensional Otsu

Forest fires can destroy millions of acres of land at shockingly fast speeds. The forest fire points identification algorithm is the most critical step in the forest fire monitoring process. Most traditional forest fire monitoring methods use fixed thresholds, ignoring background pixels, and have low recognition rates, which could lead to many problems, such as false reporting and low recognition rate. This paper proposes and tests an adaptive forest fire points identification algorithm using Himawari-8 data. By calculating the three-dimensional histogram of brightness temperature, an adaptive threshold that can automatically identify potential forest fire points is obtained. Based on this three-dimensional Otsu method, the contextual test algorithm has also been adopted to specify forest fire points. The experimental results show that the omission rate of the improved algorithm is about 10% lower than that of the previous algorithm in small-scale fire incidents. The improved algorithm can quickly and effectively extract fire point information, and it is also sensitive to small and low-temperature fires, which provides an efficient means for monitoring fire disasters.

Constructing a Three-Dimensional Histogram Describing Dislocation Viscous Glide and Climb Mechanisms

Creep is the process by which material occurs at high temperatures and its slow deformation process. -2 since the slow...

A NEW APPROACH OF FRACTAL COMPRESSION USING COLOR IMAGE

Fractal image compression depends on self-similarity, where one segment of a image is like the other one segment of a similar picture. Fractal coding is constantly connected to grey level images. The simplest technique to encode a color image by gray- scale fractal image coding algorithm is to part the RGB color image into three Channels - red, green and blue, and compress them independently by regarding each color segment as a specific gray-scale image. The colorimetric association of RGB color pictures is examined through the calculation of the relationship essential of their three-dimensional histogram. For normal color images, as a typical conduct, the connection necessary is found to pursue a power law, with a non- integer exponent type of a given image. This conduct recognizes a fractal or multiscale self-comparable sharing of the colors contained, in average characteristic pictures. This finding of a conceivable fractal structure in the colorimetric association of regular images complement other fractal properties recently saw in their spatial association. Such fractal colorimetric properties might be useful to the characterization and demonstrating of natural images, and may add to advance in vision. The outcomes got demonstrate that the fractal-based compression for the color image fills in similarly with respect to the color image.

A multimodal particle swarm optimization-based approach for image segmentation

Color image segmentation is a fundamental challenge in the field of image analysis and pattern recognition. In this paper, a novel automated pixel clustering and color image segmentation algorithm is presented. The proposed method operates in three successive stages. In the first stage, a three-dimensional histogram of pixel colors based on the RGB model is smoothened using a Gaussian filter. This process helps to eliminate unreliable and non-dominating peaks that are too close to one another in the histogram. In the next stage, the peaks representing different clusters in the histogram are identified using a multimodal particle swarm optimization algorithm. Finally, pixels are assigned to the most appropriate cluster based on Euclidean distance. Determining the number of clusters to be used is often a manual process left for a user and represents a challenge for various segmentation algorithms. The proposed method is designed to determine an appropriate number of clusters, in addition to the actual peaks, automatically. Experiments confirm that the proposed approach yields desirable results, demonstrating that it can find an appropriate set of clusters for a set of well-known benchmark images.

Three-dimensional Prediction-Error Histograms Based Reversible Data Hiding Algorithm for Color Images

Reversible watermarking technologies have rapidly developed in recent years, but there are few research achievements in the area of reversible watermarking technology for color images. Thus, in this study, a reversible data hiding (RDH) algorithm for color images was constructed based on three-dimensional (3D) histogram shifting. The algorithm improves the peak signal-to-noise ratio and reduces distortion by using rhombus prediction to predict the pixel values of each of the RGB channels of a color image to establish 3D prediction-error groups, and by modifying the mappings of the prediction-error groups in the prediction-error histogram such that mappings with high distortion are rejected and new mappings can be constructed. The experimental results proved that, under the condition of the same embedding capacity, the proposed color image-purposed algorithm, which takes advantage of the correlations between RGB channels, can yield lower distortion than conventional RDH algorithms for color images.

A context sensitive energy thresholding based 3D Otsu function for image segmentation using human learning optimization

In this paper, a novel context-based 3D Otsu algorithm using human learning optimization (HLO) is proposed for multilevel color image segmentation. The performance of 3D Otsu algorithm is reported to be poor while dealing with between-class variances through the aid of three-dimensional histogram. To overcome this problem, the concept of context thresholding has been exploited to derive pixel intensity values and spatial information. The nature of spatial context and histogram of an image is very similar. The use of energy curve for 3D Otsu gives satisfactory results but it is more time-consuming during the process of threshold selection. HLO is a recently developed meta-heuristic optimization algorithm that involves the use of learning operators developed by mimicking human learning mechanisms. In this paper, in order to avoid an exhaustive search to obtain optimal thresholds, HLO is used. Experimental studies reported in this paper demonstrate that the proposed method is better than the histogram-based 1D Otsu, 2D Otsu, and 3D Otsu methods. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy.