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In conjunction with pandemics, medical image data are growing exponentially. In some countries, hospitals collect biometric data from patients, such as fingerprints, iris, or faces. This data can be used for things like identity verification and security management. However, this medical data can be easily compromised by hackers. In order to prevent illegal tampering with medical images and invasion of privacy, a new texture fusion medical image encryption (TFMIE) algorithm derived from biometric images is proposed, which can encrypt the image using biometric information for storage or transmission. First, the medical image is decomposed into n-bit-planes by bit-plane decomposition. Secondly, a fusion image is generated by a biometric image with a circular local binary pattern and pixel-weighted average method. The fused image is further decomposed into n bit-planes through bit-plane decomposition and performs XOR operation with the original medical image in reverse order. Following the execution of the XOR operation, a new scrambling and diffusion algorithm based on a one-dimensional fractional trigonometric function (1DFTF) chaotic map is employed to form the cipher image. The experimental results show that compared with the existing methods, the average information entropy value of TFMIE is 7.99, and the average values of NPCR and UACI reach 0.9958 and 0.3346, respectively, which have strong key sensitivity, good robustness, and anti-attack ability. The method is lossless and has high transmission efficiency, which can meet the needs of medical big data encryption. [ABSTRACT FROM AUTHOR]

Land cover mapping is an important part of resource management, planning, and economic predictions. Improvements in remote sensing, machine learning, image processing, and object based image analysis (OBIA) has made the process of identifying land cover types increasingly faster and reliable but these advances have not been able to utilize all of the information encompassed within ultra-high (sub-meter) resolution imagery. There have been few known attempts to try and maximize this detailed information in high resolution imagery using advanced textural components. Hierarchical land classes are also rarely used as an attribute within the machine learning step of object-based image analysis. In this study we try to circumnavigate the inherent problems associated with high resolution imagery by combining well researched data transformations that aid the OBIA process with a seldom used texture transformation in Geographic Object Based Image Analyses (GEOBIA/OBIA) known as the Gabor Transform and the hierarchal organization of landscapes. We will observe the difference made in segmentation and classification accuracy of a random forest classifier when we fuse a Gabor transformed image to a Normalized Difference Vegetation Index (NDVI), high resolution multi-spectral imagery (RGB and NIR) and Light Detection and Ranging (LiDAR) derived canopy height model (CHM) within a riparian area in Southeast Iowa, United States. Additionally, we will observe the effects on classification accuracy when adding multi-scale land cover data to objects. Both, the addition of hierarchical information and Gabor textural information, could aid the GEOBIA process in delineating and classifying the same objects that human experts would delineate within this riparian landscape. [ABSTRACT FROM AUTHOR]

We present a new self-supervised monocular depth estimation method with multi-scale texture detail enhancement. Based on the observation that the image texture detail and the semantic information have essential significance on the depth estimation, we propose to provide them to the network to learn more sharpness and structural integrity of depth. Firstly, we generate the filtered images and detail images by multi-scale decomposition and use a deep neural network to automatically learn their weights to construct the texture detail enhanced image. Then, we consider the semantic features by putting deep features from the VGG-19 network into a self-attention network, guide the depth decoder network to focus on the integrity of objects in the scene. Finally, we propose a scale-invariant smooth loss to improve the structural integrity of the predicted depth. We evaluate our method on the KITTI 2015 and Make3D datasets and apply the predicted depth to novel view synthesis. The experimental results show that it has achieved satisfactory results compared with the existing methods. [ABSTRACT FROM AUTHOR]

Landforms are an important element of natural geographical environment, and textures are the research basis for the spatial differentiation, evolution features, and analysis rules of the landform. Using the regional difference of texture to describe the spatial distribution pattern of macro landform features is helpful to the landform classification and identification. Digital elevation model (DEM) image texture, which gives full expression to texture difference, is key data source to reflect the surface features and landform classification. Following the texture analysis, landform features analysis is assistant to different landforms classification, even in landform boundary. With the increasing accuracy requirement of landform information acquisition in geomorphic thematic mapping, hierarchical landform classification has become the focus and difficulty in research. Recently, the pattern recognition represented by Convolutional Neural Network has made great achievements in landform research, whose multichannel feature fusion structure satisfies the network structure of different landform classification. In this paper, DEM image texture was taken as the data source, and gray level co-occurrence matrix was applied to extract texture measures. Owing to the similarity of similar landform and the difference of different landform in a certain scale, a comprehensive texture factor reflecting landform features was proposed, and the spatial distribution pattern of landform features was systematically analyzed. On this basis, the coupling relationship between texture and landform type was explored. Thus, the deep learning method of Convolutional Neural Network is used to train the texture features, and the second-class landform classification is carried out through softmax. The classification results in small relief and mid-relief low mountains, overall accuracy are 84.35% and 69.95% respectively, while kappa coefficient are 0.72 and 0.40 respectively, were compared to that of traditional unsupervised landform classification results, and the superiority of Convolutional Neural Network classification was verified, it approximately improved 6% in overall accuracy and 0.4 in kappa coefficient. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of cirrhosis staging diagnosis based on MR images, a diagnostic method combining image texture feature extraction and classification algorithm is proposed. Firstly, the liver MR image is preprocessed, the region of interest (ROI) image patch is extracted therefrom, and the ROI image is quantized and compressed by the Lloyd algorithm. Then, the ROI image is filtered by a local binary pattern (LBP) operator, and then the texture feature of a 20-dimensional gray-level co-occurrence Matrix (GLCM) in four directions on the LBP image is extracted. Finally, MR image is classified by performing support vector machine (SVM) and the final diagnosis of liver cirrhosis is obtained. The experimental results show that the proposed method can accurately diagnose liver cirrhosis. [ABSTRACT FROM AUTHOR]

Modifications of algorithms for the classification of single-layer, vertical development clouds, and multilayer clouds based on a probabilistic neural network and a neuro-fuzzy classifier are proposed. Clouds are classified into 16 types according to the meteorological standard, including the combined subtypes of stratus, altocumulus, cirrus, and cirrostratus clouds. The article uses a description of clouds based on information about the texture of their images on satellite images from MODIS and its products with data on the physical parameters of clouds. The structure of classification algorithms is described. The results of the use of statistical models of image texture and physical parameters of clouds for initializing membership functions for a neural-fuzzy classifier are presented. Systems of effective classification characteristics for different classification algorithms are formed based on the GRAD modified truncated search method. The recognition results of single-layer, vertical development clouds, and multilayer clouds based on the corresponding test samples and full-size sets of MODIS satellite data with different spatial resolution are discussed. [ABSTRACT FROM AUTHOR]

Objectives: Sickle cell disease (SCD) can cause osteoporotic changes in the jaw bones. In this study, it was aimed to evaluate possible bone changes using fractal analysis (FA) and morphometric analyses in dental panoramic radiographs of children and adolescents diagnosed with both homozygous and heterozygous forms of SCD. Methods: Sixty-five individuals (33 SCD, 32 controls) aged 6–17 years were included in the study. Four separate areas of interest (ROI) were selected for the right and left sides of all panoramic radiographs, and the FA value of the ROIs was calculated. Mandibular cortical width (MCW), panoramic mandibular index (PMI) and mandibular cortical index (MCI) and were evaluated. Data were statistically analyzed and p < 0.05 was accepted for statistical significance. Results: Fractal values of right and left ROI1 (the center of the mandibular angle.) and ROI4 (the cortical bone), and right ROI2 (the middle of the mandibular ramus) were statistically lower in the case group (p < 0.05). Right ROI2 and ROI4 fractal values of individuals in the case group were lower than those on the left side (p < 0.05). While MCI categories did not differ from the case–control group (p > 0.05), PMI and MCW values were lower in the case group (p < 0.05). All evaluated parameters did not differ according to age and gender (p > 0.05). Conclusion: The results of this study showed that SCD affects the mandible. FA, MCW and PMI parameters can be used to detect early osteoporotic changes in the disease. [ABSTRACT FROM AUTHOR]

Context: Pattern metrics drawn from image processing and remote sensing have been applied as descriptors of the texture of landscape gradient data. Like some classical pattern metrics in ecology, texture has several facets which are measured by examining an adjacency matrix—the frequencies of co-occurring pixel values on a map—in different ways. Objectives: To improve the interpretation and application of such metrics in landscape ecology we reformulate and interpret several of them by analogy to traditional metrics used with categorical data. Results and conclusions: 1. Four of the eight classical texture metrics measure attraction—the tendency for the same or similar values to be adjacent. Four others measure dispersion—the diversity of adjacencies relative to the entire adjacency matrix, the diagonal of the matrix, or the origin of the matrix. 2. The attraction metrics (dissimilarity, contrast, inverse difference, and homogeneity) differ only in the algebraic weights applied to different parts of an adjacency matrix. 3. The dispersion metrics (entropy, uniformity, difference entropy, and sum entropy) can be made more comparable by rescaling them to their maximum possible values. 4. While the metrics may be applied to any adjacency matrix, the choices about the method used to create an adjacency matrix have subtle yet important implications for the use and comparability of some metrics. [ABSTRACT FROM AUTHOR]

Image analysis gives us a new opportunity in corrosion science. Fuzzy Kolmogorov-Sinai (K-S) entropy is used to quantify the average amount of uncertainty of a dynamical system through a sequence of observations. The fuzzy K-S entropy for horizontal and vertical orientations is sensitive to distribution of corrosion product or corrosion degree, and the entropy values decrease as the corrosion becomes more and more serious. It is concluded that the fuzzy K-S entropy is illustrated as an effective feature for image analysis and corrosion classification. [ABSTRACT FROM AUTHOR]

Objectives: Magnetic resonance imaging (MRI) is useful for assessing temporomandibular disorders (TMDs). However, few studies have attempted texture analysis of the lateral pterygoid muscle in patients with rheumatoid arthritis (RA). This study aims to investigate the usefulness of MRI texture analysis of the lateral pterygoid muscle of patients with RA of the temporomandibular joint (TMJ). Methods: We analyzed the data from 36 patients (18 non-RA patients and 18 RA patients) who complained of pain and underwent MRI between April 2008 and August 2021. From the MRI scans of these patients, 279 radiomics features were extracted using STIR image data of the ROIs on the lateral pterygoid muscle of patients with RA and analyzed using MaZda ver. 3.3. Seven gray-level co-occurrence matrix features (Sum entropy, Sum variance) were picked up using the Fisher coefficient, for comparison between the RA and non-RA groups. Data analysis was performed using the Mann–Whitney U test A P value of < 0.05 was considered as statistically significant. Results: All seven lateral pterygoid muscle radiomic features indicated significant differences between the non-RA and RA groups (P < 0.05). Conclusion: MRI texture analysis shows potential for application in radiomics diagnosis of RA in TMJ. [ABSTRACT FROM AUTHOR]

A comprehensive method of B-mode ultrasound image texture analysis for the determination of the liver fibrosis stage is suggested. The algorithm is based on the use of Rotation Forest and KNN classifiers for the texture classification. 720 textural characteristics were extracted using methods based on Laws' masks analysis, co-occurrence matrix, gray level run-length matrix and statistical characteristics of the images. An optimal subset of 22 informative features was selected using correlation-based method. Testing the algorithm with liver images of 57 patients divided into 5 stages of fibrosis showed 72.7% classification accuracy for single regions of interest. In the case of entire image classification the fibrosis stage was correctly identified for the vast majority of cases. [ABSTRACT FROM AUTHOR]

In the flotation process, the flotation froth texture is an indicator of the flotation state. To recognize the flotation state based on texture features accurately and to provide guidance for production operations, this paper proposes a method for flotation froth image texture extraction based on the deterministic tourist walks algorithm. First, a weighted graph model of a froth image is built using deterministic tourist walks. Next, the degree distribution and the unit intensity distribution of the weighted graph are extracted. The contrast of the node degree and the contrast of the node unit intensity are calculated as the texture feature indexes. The texture feature indexes are used for flotation production state classification and recognition. The experimental results demonstrate that the proposed method can extract froth image texture features accurately and provide effective guidance for flotation production. [ABSTRACT FROM AUTHOR]

The feasibility of image texture analysis to evaluate X-ray images of fungal-infected maize kernels was investigated. X-ray images of maize kernels infected with Fusarium verticillioides and control kernels were acquired using high-resolution X-ray micro-computed tomography. After image acquisition and pre-processing, several algorithms were developed to extract image textural features from selected two-dimensional (2D) images of the kernels. Four first-order statistics (mean, standard deviation, kurtosis and skewness) and four grey level co-occurrence matrix (GLCM) features (correlation, energy, homogeneity and contrast) were extracted from the side, front and top views of each kernel and used as inputs for principal component analysis (PCA). The first-order statistical image features gave a better separation of the control from infected kernels on day 8 post-inoculation. Classification models were developed using partial least squares discriminant analysis (PLS-DA), and accuracies of 67 and 73% were achieved using first-order statistical features and GLCM extracted features, respectively. This work provides information on the possible application of image texture as method for analysing X-ray images. [ABSTRACT FROM AUTHOR]

The different percentages of boron carbide (B4C) reinforced Magnesium metal matrix composites were processed using Powder Metallurgy method. The characterizations are performed by Scanning Electronic Microscope (SEM), X-ray Diffraction pattern, Electron Backscattered Diffraction, and Energy Dispersive Spectrum. The texture analysis of the composites is conducted via an image processing method. Wear tests are executed in the dry sliding state under ambient room conditions. The three levels Box-Behnken design of Response Surface Methodology has been selected to create the mathematical models for the three variables (weight percentage of B4C, sliding velocity, and load) to predict the responses namely specific wear rate (SWR) and coefficient of friction (COF). The developed model is validated and analyzed through the Analysis of variance (ANOVA). This model has a 95% efficient confidence to forecast the COF and SWR. The worn surface features are extracted and analyzed through an image processing technique. The roughness measurement of the worn surfaces is executed via Atomic Force Microscopy analysis. [ABSTRACT FROM AUTHOR]

A new texture defect detection method for automatic visual inspection systems is presented in this paper. It divides an analysed texture image into non-overlapping samples, and then calculates features of each sample using the Principle Component Analysis technique. Finally, the fuzzy c-means clustering of these features is applied to classify the sample as defective or non-defective. Unlike many existing methods, the proposed scheme does not require a training step to collect defective and non-defective texture samples. The experimental results show that the method is at least as effective and accurate as many existing methods for image texture defect detection. [ABSTRACT FROM AUTHOR]

Effectiveness of local binary pattern (LBP) features is well proven in the field of texture image classification and retrieval. This paper presents a more effective completed modeling of the LBP. The traditional LBP has a shortcoming that sometimes it may represent different structural patterns with same LBP code. In addition, LBP also lacks global information and is sensitive to noise. In this paper, the binary patterns generated using threshold as a summation of center pixel value and average local differences are proposed. The proposed local structure patterns (LSP) can more accurately classify different textural structures as they utilize both local and global information. The LSP can be combined with a simple LBP and center pixel pattern to give a completed local structure pattern (CLSP) to achieve higher classification accuracy. In order to make CLSP insensitive to noise, a robust local structure pattern (RLSP) is also proposed. The proposed scheme is tested over three representative texture databases viz. Outex, Curet, and UIUC. The experimental results indicate that the proposed method can achieve higher classification accuracy while being more robust to noise. [ABSTRACT FROM AUTHOR]

Texture segmentation is one of the most difficult and important tasks in the remote sensing image processing. Through the years, several methods, based on statistical features, structural features and other image features, have been proposed for texture segmentation. This paper proposes a new feature based on Contourlet transform and Fractal analysis for texture segmentation. The performance of the proposed feature is compared with that of fractal, statistical, fuzzy and power spectrum features in presence of noise and the obtained results show superiority of the new proposed feature. [ABSTRACT FROM AUTHOR]

Effective disturbance indices for Hyrcanian forests in Kheyroud, Nowshahr, Iran were determined. The study area was divided into landscape mosaics based on ecosystem parameters including profile type, slope and elevation. Co-occurrence texture indices were derived as forest disturbance factors on the first five bands of Landsat TM, ETM+ and OLI images for the prevailing wood harvest disturbance regimes. These indices were screened using ten types of trend analyses and used for modeling disturbance of the harvesting regime through artificial neural networks. The results show that the selected indices can be useful in distinguishing areas with different disturbance intensities and as such, used in the context of health assessment through the health distance method. The accuracy of the health maps derived from the indices [increasing disturbance] led to give rise higher disturbance classification accuracy. [ABSTRACT FROM AUTHOR]

Computer vision has arisen as one of the most important application areas in manufacturing processes. This work shows a new real-time texture analysis for medium-density fiberboard with melamine paper, using edge detection techniques and threshold detection methods. To minimize the time of identification of defects, the images of fiberboard are sent to a grid system. In a first phase, several tests are carried out using different image resolutions and sizes. In a second phase, to optimize the system, with the best resolution obtained and using a grid system, our aim is to minimize the time of detection of possible defects without jeopardizing the performance of the quality control system. Results show that, using accurate resolutions, the error detection process is quicker and the defect identification rate significantly improves. [ABSTRACT FROM AUTHOR]

A rapid and nondestructive machine vision sensor was developed for predicting the vibrated bulk density (VBD) of petroleum coke particles based on image texture analysis. It could be used for making corrective adjustments to a paste plant operation to reduce green anode variability (e.g., changes in binder demand). Wavelet texture analysis (WTA) and gray level co-occurrence matrix (GLCM) algorithms were used jointly for extracting the surface textural features of coke aggregates from images. These were correlated with the VBD using partial least-squares (PLS) regression. Coke samples of several sizes and from different sources were used to test the sensor. Variations in the coke surface texture introduced by coke size and source allowed for making good predictions of the VBD of individual coke samples and mixtures of them (blends involving two sources and different sizes). Promising results were also obtained for coke blends collected from an industrial-baked carbon anode manufacturer. [ABSTRACT FROM AUTHOR]

In case of illumination change, the local binary pattern (LBP) descriptor have found to be used in analysis of texture of the image because of the ease of computation and robustness to such changes. However, the LBP technique also comes with limitations such as its inability to capture the discriminative information completely. For enhancing the LBP's performance, we proposed a new texture descriptor for rotation, illumination and scale invariance (IRSLBP) for texture classification. The proposed approach extracts the color features through quantification of the RGB space into single channel, which is marked by a smaller number of shades to reduce computation and to improve the efficiency. The IRSLBP descriptor provides the scale invariance by considering the circular neighbor set of every central pixel other than the normal neighboring pixels. Moreover, the proposed IRSLBP decomposed the difference vector into sign part and magnitude part by local difference sign magnitude transform. In addition, these mitigated the influence of rotation, illumination or noise and demonstrated effective robustness. Using the proposed IRSLBP descriptor, we have classified the different textures using Multi kernel support vector machine (SVM) approach. [ABSTRACT FROM AUTHOR]

As one of fundamental texture classification methods, LBP-based descriptors have attracted considerable attention due to the efficiency, simplicity, and high performance. However, most of binary pattern methods cannot effectively capture the texture information with scale changes. Inspired by this, this paper proposes a multi-scale threshold integration encoding strategy for texture classification. The essence of this strategy is to introduce the multi-scale local texture information in the view of thresholding. Based on this, we propose the local multi-scale center pattern, local multi-scale sign pattern, and local multi-scale magnitude pattern to extract and describe the multi-scale local texture information. Then, the three sub-patterns are jointly combined to generate the final descriptor for texture classification tasks. The experimental results on three popular texture databases significantly demonstrate that the proposed texture descriptor is very discriminative and powerful for visual texture classification tasks. [ABSTRACT FROM AUTHOR]

According to the pulverized coal combustion flame image texture features of the rotary-kiln oxide pellets sintering process, a combustion working condition recognition method based on the generalized learning vector (GLVQ) neural network is proposed. Firstly, the numerical flame image is analyzed to extract texture features, such as energy, entropy and inertia, based on grey-level co-occurrence matrix (GLCM) to provide qualitative information on the changes in the visual appearance of the flame. Then the kernel principal component analysis (KPCA) method is adopted to deduct the input vector with high dimensionality so as to reduce the GLVQ target dimension and network scale greatly. Finally, the GLVQ neural network is trained by using the normalized texture feature data. The test results show that the proposed KPCA-GLVQ classifier has an excellent performance on training speed and correct recognition rate, and it meets the requirement for real-time combustion working condition recognition for the rotary kiln process. [ABSTRACT FROM AUTHOR]

The dynamic image information of typical gas-liquid two-phase flow patterns in vertical upward pipe is captured by a highspeed dynamic camera. The texture spectrum descriptor is used to describe the texture characteristics of the processed images whose content is represented in the form of texture spectrum histogram, and four time-varying characteristic parameter indexes which represent image texture structure of different flow patterns are extracted. The study results show that the amplitude fluctuation of texture characteristic parameter indexes of bubble flow is lowest and shows very random complex dynamic behavior; the amplitude fluctuation of slug flow is higher and shows intermittent motion behavior between gas slug and liquid slug, and the amplitude fluctuation of churn flow is the highest and shows better periodicity; the amplitude fluctuation of bubble-slug flow is from low to high and oscillating frequence is higher than that of slug flow, and includes the features of both slug flow and bubble flow; the slug-churn flow loses the periodicity of slug flow and churn flow, and the amplitude fluctuation is high. The results indicate that the image texture characteristic parameter indexes of different flow pattern can reflect the flow characteristics of gas-liquid two-phase flow, which provides a new approach to understand the temporal and spatial evolution of flow pattern dynamics. [ABSTRACT FROM AUTHOR]

In order to improve the visualization simulation effect of water wave, we use the images of actual water wave as a feedback to correct the control parameters of Shallow Water (SW) equations in this paper. First, we employ a kind of simplified numerical method to resolve the SW equations and create an initial water wave animation. The initial control parameters of SW equations can be set arbitrarily. Second we cut some water wave images from the artificial animation above to build an image dataset of Simulated Water Wave (SWW). Third, we capture the Actual Water Wave (AWW) images by cameras, which are fixed in the selected locations of a moving boat, to build another image dataset. After that, we make a correlation analysis of image texture between the artificial image dataset of SWW and the actual image dataset of AWW to compare their similarity. In this phase, some image quality metrics of Tamura's texture, together with the mathematic tools of Mojette transform, Gabor wavelet and Detrended Fluctuation Analysis (DFA) technique are utilized to accomplish the static and dynamic texture analysis tasks. Finally, we use the results of correlation analysis above as a feedback to give a guidance to tune the control parameters of SW equations and regenerate the water wave animation with better visualization effects. To enhance the fidelity of SWW images, we also use Gabor wavelet and the criterion of minimized distance to estimate the environment illumination direction of AWW if the texture definition is good enough. By this means, we can set proper light source parameters to the visualization animation of SWW. Extensive experiment results have shown us that the visualization simulation effect can be improved effectively by the application of our texture feedback based techniques. [ABSTRACT FROM AUTHOR]

We investigated the association between the textural features obtained from 18F-FDG images, metabolic parameters (SUVmax, SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated.MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage. [ABSTRACT FROM AUTHOR]

The alternative starches and flours used in gluten-free bakery in replacement of wheat have very low quantities of fibre. As a consequence, the resulting products have a poor nutritional profile in terms of this nutrient, and present technological difficulties and quality challenges related to texture. In this work, the texture of gluten-free cookies added with a source of fibre, and its impact in consumer's perception was studied. Due to the complexity of texture, its description was assessed through three different methods: instrumental force/deformation measurements, image analysis, and sensory evaluation, in order to reach a better representation of the product's texture and to compare the different techniques' capacity to describe it. The instrumental method showed that the control cookie presented a higher maximum force (Fmax), indicating that this sample was significantly more resistant to fracture, while the cookie added with fibre showed a lower deformation before fracture (ΔFmax), which corresponds to a more fragile structure. The results obtained by image texture analysis using the Gray Level Co-occurrence Matrix method were consistent with those determined by instrumental analysis. Good correlations were reached between the instrumental firmness and the five image parameters, showing the capacity of image attributes to predict mechanical properties of the samples. Sensory analysis with consumers showed a good acceptability of the product, but was not as useful to detect small differences between cookies. The study of texture through different techniques could be an interesting approach to attain complementary information, providing a more comprehensive perspective of this multifaceted attribute. [ABSTRACT FROM AUTHOR]

Background: Recurrence after resection of colorectal liver metastases (CRLMs) occurs in up to 75% of patients. Preoperative prediction of hepatic recurrence may inform therapeutic strategies at the time of initial resection. Texture analysis (TA) is an established technique that quantifies pixel intensity variations (heterogeneity) on cross-sectional imaging. We hypothesized that tumoral and parenchymal changes that are predictive of overall survival (OS) and recurrence in the future liver remnant (FLR) can be detected using TA on preoperative computed tomography (CT) images. Methods: Patients who underwent resection for CRLM between 2003 and 2007 with appropriate preoperative CT scans were included ( n = 198) in this retrospective study. Texture features extracted from the tumor and FLR, and clinicopathologic variables, were incorporated into a multivariable survival model. Results: Quantitative imaging features of the FLR were an independent predictor of both OS and hepatic disease-free survival (HDFS). Tumor texture showed significant association with OS. TA of the FLR allowed patient stratification into two groups, with significantly different risks of hepatic recurrence (hazard ratio 2.09, 95% confidence interval 1.33-3.28; p = 0.001). Patients with homogeneous parenchyma had approximately twice the risk of hepatic recurrence (41 vs. 20%). Conclusion: TA of the tumor and FLR are independently associated with OS, and TA of the FLR is independently associated with HDFS. Patients with homogeneous parenchyma had a significantly higher risk of hepatic recurrence. Preoperative TA of the liver represents a potential biomarker to identify patients at risk of liver recurrence after resection for CRLM. [ABSTRACT FROM AUTHOR]

This paper introduces a new method for clustering of documents, which have been written in a language evolving during different historical periods, with an example of the Italian language. In the first phase, the text is transformed into a string of four numerical codes, which have been derived from the energy profile of each letter, defining the height of the letters and their location in the text line. Each code represents a gray level and the text is codified as a 1-D image. In the second phase, texture features are extracted from the obtained image in order to create document feature vectors. Subsequently, a new clustering algorithm is employed on the feature vectors to discriminate documents from different historical periods of the language. Experiments are performed on a database of Italian documents given in Italian Vulgar and modern Italian. Results demonstrate that this proposed method perfectly identifies the historical periods of the language of the documents, outperforming other well-known clustering algorithms generally adopted for document categorization and other state-of-the-art text-based language models. [ABSTRACT FROM AUTHOR]

Images of titanium alloy welds detected by time-of-flight diffraction (TOFD) have problems, including large noise signals and many interference streaks around the defects, all of which seriously limit the accuracy and effectiveness of defect recognition. Existing image denoising methods lack the knowledge of the noise characteristics of TOFD images of titanium alloy weld and the preprocessing experience of technicians in the field. In addition, it is difficult to select the parameters of the preprocessing methods, and they are easily influenced by the level of technical personnel, resulting in low efficiency and poor consistency in preprocessing. To address these problems, we proposed a denoising method based on the combination of wavelet band features and deep-learning theory for TOFD images of titanium alloy weld. First, based on the wavelet preprocessing method and the experience of nondestructive testing (NDT) technicians, we constructed an image pair dataset consisting of the original TOFD images of titanium alloy weld and the desired target images to realize the accumulation of engineers' preprocessing knowledge. Second, we constructed a multiband wavelet feature fusion U-net image denoising model (WU-net) and designed a loss function under three constraints of image consistency, image texture information consistency, and structural similarity. This model was able to learn to achieve end-to-end adaptive denoising for TOFD images of titanium alloy weld. Third, we illustrated and validated the effectiveness of TOFD image preprocessing for titanium alloy weld. The results showed that the proposed method effectively eliminated TOFD image noise and improved the accuracy of defect recognition. [ABSTRACT FROM AUTHOR]

Purpose: 123I-Ioflupane SPECT is an effective tool for the diagnosis and progression assessment of Parkinson's disease (PD). Radiomics and deep learning (DL) can be used to track and analyze the underlying image texture and features to predict the Hoehn-Yahr stages (HYS) of PD. In this study, we aim to predict HYS at year 0 and year 4 after the first diagnosis with combined imaging, radiomics and DL-based features using 123I-Ioflupane SPECT images at year 0. Methods: In this study, 161 subjects from the Parkinson's Progressive Marker Initiative database underwent baseline 3T MRI and 123I-Ioflupane SPECT, with HYS assessment at years 0 and 4 after first diagnosis. Conventional imaging features (IF) and radiomic features (RaF) for striatum uptakes were extracted from SPECT images using MRI- and SPECT-based (SPECT-V and SPECT-T) segmentations respectively. A 2D DenseNet was used to predict HYS of PD, and simultaneously generate deep features (DF). The random forest algorithm was applied to develop models based on DF, RaF, IF and combined features to predict HYS (stage 0, 1 and 2) at year 0 and (stage 0, 1 and ≥ 2) at year 4, respectively. Model predictive accuracy and receiver operating characteristic (ROC) analysis were assessed for various prediction models. Results: For the diagnostic accuracy at year 0, DL (0.696) outperformed most models, except DF + IF in SPECT-V (0.704), significantly superior based on paired t-test. For year 4, accuracy of DF + RaF model in MRI-based method is the highest (0.835), significantly better than DF + IF, IF + RaF, RaF and IF models. And DL (0.820) surpassed models in both SPECT-based methods. The area under the ROC curve (AUC) highlighted DF + RaF model (0.854) in MRI-based method at year 0 and DF + RaF model (0.869) in SPECT-T method at year 4, outperforming DL models, respectively. And then, there was no significant differences between SPECT-based and MRI-based segmentation methods except for the imaging feature models. Conclusion: The combination of radiomic and deep features enhances the prediction accuracy of PD HYS compared to only radiomics or DL. This suggests the potential for further advancements in predictive model performance for PD HYS at year 0 and year 4 after first diagnosis using 123I-Ioflupane SPECT images at year 0, thereby facilitating early diagnosis and treatment for PD patients. No significant difference was observed in radiomics results obtained between MRI- and SPECT-based striatum segmentations for radiomic and deep features. [ABSTRACT FROM AUTHOR]

Image information degradation significantly complicates single-image dehazing. Despite the noteworthy progress of learning-based methods in removing haze, many existing algorithms focus solely on haze removal while neglecting the crucial aspect of image texture recovery, often resulting in the loss of image details and texture blurring. To tackle this issue, we propose a dual-branch feature fusion dehazing network via multi-spectral channel attention (MS-DBFFN). First, we design a multi-spectral image pre-processing module (MS-PPM), maximizing the utilization of correlation features in hazy images by introducing additional frequency components. This process results in preliminary dehazing images. Second, a dual-branch feature fusion module (DBFF) adaptively fuses features from both the hazy image and preliminary dehazing image via an iterative update strategy. During this gradual iterative updating process, the structural features of the preliminary dehazing image become richer as more feature information is assimilated. Third, our backbone network facilitates the sharing of valuable information across multiple scales from the feature map generated by the DBFF module. Finally, an improved contrastive regularization loss (ICR) generates sharper images with enhanced details and textures. Extensive experiments on both real and synthetic datasets demonstrate that the proposed method, MS-DBFFN, achieves state-of-the-art performance on several image dehazing benchmarks. [ABSTRACT FROM AUTHOR]

We present a new efficient approach for characterizing image texture based on a recently published discrete, orthonormal space-frequency transform known as the DOST. We develop a frequency-domain implementation of the DOST in two dimensions for the case of dyadic frequency sampling. Then, we describe a rapid and efficient approach to obtain local spatial frequency information for an image and show that this information can be used to characterize the horizontal and vertical frequency patterns in synthetic images. Finally, we demonstrate that DOST components can be combined to obtain a rotationally invariant set of texture features that can accurately classify a series of texture patterns. The DOST provides the computational efficiency and multi-scale information of wavelet transforms, while providing texture features in terms of Fourier frequencies. It outperforms leading wavelet-based texture analysis methods. [ABSTRACT FROM AUTHOR]

Purpose: Given the paucity of reliable predictors of tumor recurrence, progression, or response to somatostatin receptor ligand (SRL) therapy in acromegaly, we attempted to determine whether preoperative MR image texture was predictive of these clinical outcomes. We also determined whether image texture could differentiate somatotroph adenomas from non-functioning pituitary adenomas (NFPAs). Methods: We performed a retrospective study of patients with acromegaly due to a macroadenoma who underwent transsphenoidal surgery at our institution between 2007 and 2015. Clinical data were extracted from electronic medical records. MRI texture analysis was performed on preoperative non-enhanced T1-weighted images using ImageJ (NIH). Logistic and Cox models were used to determine if image texture parameters predicted outcomes. Results: Eighty-nine patients had texture parameters measured, which were compared to that of NFPAs, while 64 of these patients had follow-up and were included in the remainder of analyses. Minimum pixel intensity, skewness, and kurtosis were significantly different in somatotroph adenomas versus NFPAs (area under the receiver operating characteristic curve, 0.7771, for kurtosis). Furthermore, those with a maximum pixel intensity above the median had an increased odds of IGF-I normalization on SRL therapy (OR 5.96, 95% CI 1.33–26.66), which persisted after adjusting for several potential predictors of response. Image texture did not predict tumor recurrence or progression. Conclusion: Our data suggest that MRI texture analysis can distinguish NFPAs from somatotroph macroadenomas with good diagnostic accuracy and can predict normalization of IGF-I with SRL therapy. [ABSTRACT FROM AUTHOR]

A local region in an image can be defined using centre pixel and its differences with neighboring pixels. In order to characterize different texture structure in a discriminating manner, this paper proposes twoframeworks of Noise Invariant Structure Pattern (NISP) which utilizes both the centre pixel and local and global information of an image. To replace the centre pixel, a threshold computed from adding centre pixel and intensity averages is used in the LBP code computation. For adding the magnitude information, binary patterns generated by taking thresholds involving centre pixel and local and global average contrast are adopted. Also for adding the information of individual neighborhood of a given pixel, the binary patterns generated from global thresholding of local averages are used. Based on the use of local and global information, this paper suggests two noise invariant models that are CNLP and CNGP (i.e. Completed Noise-invariant Local-structure Pattern and Global-structure Pattern). The proposed NISPs are also insensitive to noise as the centre pixel is not directly used as threshold. The proposed texture descriptors are tested on some of the representative texture databases like Outex, Curet, UIUC, Brodatz and XU -HR. The experimental results have shown that the proposed schemes can achieve higher classification and retrieval rates while being more robust to noise. [ABSTRACT FROM AUTHOR]

Fractal-based image analysis methods are investigated to extract textural features related to the anisotropic structure of trabecular bone from the X-ray images of cubic bone specimens. Three methods are used to quantify image textural features: power spectrum, Minkowski dimension and mean intercept length. The global fractal dimension is used to describe the overall roughness of the image texture. The anisotropic features formed by the trabeculae are characterised by a fabric ellipse, whose orientation and eccentricity reflect the textural anisotropy of the image. Tests of these methods with synthetic images of known fractal dimension show that the Minkowski dimension provides a more accurate and consistent estimation of global fractal dimension. Tests on bone x-ray (eccentricity range 0.25-0.80) images indicate that the Minkowski dimension is more sensitive to the changes in textural orientation. The results suggest that the Minkowski dimension is a better measure for characterising trabecular bone anisotropy in the x-ray images of thick specimens. [ABSTRACT FROM AUTHOR]

Objective: To evaluate image quality and diagnostic confidence improvement using a thin slice and a deep learning image reconstruction (DLIR) in contrast-enhanced abdominal CT. Methods: Forty patients with hepatic lesions in enhanced abdominal CT were retrospectively analyzed. Images in the portal phase were reconstructed at 5 mm and 1.25 mm slice thickness using the 50% adaptive statistical iterative reconstruction (ASIR-V) (ASIR-V50%) and at 1.25 mm using DLIR at medium (DLIR-M) and high (DLIR-H) settings. CT number and standard deviation of the hepatic parenchyma, spleen, portal vein, and subcutaneous fat were measured, and contrast-to-noise ratio (CNR) was calculated. Edge-rise-slope (ERS) was measured on the portal vein to reflect spatial resolution and the CT number skewness on liver parenchyma was calculated to reflect image texture. Two radiologists blindly assessed the overall image quality including subjective noise, image contrast, visibility of small structures using a 5-point scale, and object sharpness and lesion contour using a 4-point scale. Results: For the 1.25-mm images, DLIR significantly reduced image noise, improved CNR and overall subjective image quality compared to ASIR-V50%. Compared to the 5-mm ASIR-V50% images, DLIR images had significantly higher scores in the visibility and contour for small structures and lesions; as well as significantly higher ERS and lower CT number skewness. At a quarter of the signal strength, the 1.25-mm DLIR-H images had a similar subjective noise score as the 5-mm ASIR-V50% images. Conclusion: DLIR significantly reduces image noise and maintains a more natural image texture; image spatial resolution and diagnostic confidence can be improved using thin slice images and DLIR in abdominal CT. Key Points: • DLIR further reduces image noise compared with ASIR-V while maintaining favorable image texture. • In abdominal CT, thinner slice images improve image spatial resolution and small object visualization but suffer from higher image noise. • Thinner slice images combined with DLIR in abdominal CT significantly suppress image noise for detecting low-density lesions while significantly improving image spatial resolution and overall image quality. [ABSTRACT FROM AUTHOR]

Finding patches is a crucial step in a pavement performance survey. The study develops a machine learning and image processing algorithm-established automatic method for identifying asphalt pavement patches. The GrayLevel Co-Occurrence Matrix and image texture-based features derived from color channel statistics are used as input parameters to describe the condition of the pavement. For feature extraction, image processing methods like the projective integral of images, steerable filters, and an improved image thresholding method were used. Support Vector Machine is employed for categorizing the differentiating patched regions from non-patch ones. The suggested combination of image texture analytical methods has been trained using an IA data set created from 400 image samples. The feature set, which combines the characteristics of cracked objects with those generated from the projective integral, can produce the desired result. To make the model's execution simpler, a patch recognition program was created and implemented in MATLAB. As a result, the recently created method has the potential to be an instrument for traffic management organizations through the assessment of pavement performance. The results of the experiments indicate that the newly developed method may achieve excellent accuracy prediction results with a classifier performance rate of about 96%. Such a method could enable transportation authorities to assess the state of asphalt pavement. [ABSTRACT FROM AUTHOR]