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Automatic classification in medical image processing with new problems to computer-aided detection methods is attributed to the dynamics of the image acquisition conditions. Such problems request t...

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.

Color constancy is the capability to observe the true color of a scene from its image regardless of the scene's illuminant. It is a significant part of the digital image processing pipeline and is utilized when the true color of an object is required. Most existing color constancy methods assume a uniform illuminant across the whole scene of the image, which is not always the case. Hence, their performances are influenced by the presence of multiple light sources. This paper presents a color constancy adjustment technique that uses the texture of the image pixels to select pixels with sufficient color variation to be used for image color correction. The proposed technique applies a histogram-based algorithm to determine the appropriate number of segments to efficiently split the image into its key color variation areas. The K-means++ algorithm is then used to divide the input image into the pre-determined number of segments. The proposed algorithm identifies pixels with sufficient color variation in each segment using the entropies of the pixels, which represent the segment's texture. Then, the algorithm calculates the initial color constancy adjustment factors for each segment by applying an existing statistics-based color constancy algorithm on the selected pixels. Finally, the proposed method computes color adjustment factors per pixel within the image by fusing the initial color adjustment factors of all segments, which are regulated by the Euclidian distances of each pixel from the centers of gravity of the segments. The experimental results on benchmark single- and multiple-illuminant image datasets show that the images that are obtained using the proposed algorithm have significantly higher subjective and very competitive objective qualities compared to those that are obtained with the state-of-the-art techniques.

The traditional image texture extraction algorithm, most of the lifting of the texture is a single scale and direction of the texture. When you change the scale and direction, the corresponding texture will change as well. This is the lack of the traditional texture extraction method. In this paper, aiming at the single direction of the traditional texture extraction, a novel image texture feature extraction algorithm based on Gabor filter and CS-LBP operator is proposed. The extracted frequency components are transformed to the time domain to achieve the texture extraction. Based on the principle of polarization, the curvature factor is introduced based on the traditional Gabor model, which solves the problem that the Gabor filter can’t extract the local image warp and distant scene blurred. The experimental results show that the proposed method uses the Gabor filter and the LBP operator to extract the polarized image texture effectively than the traditional image texture extraction algorithm.

The rendering effect of known visual image texture is poor and the output image is not always clear. To solve this problem, this paper proposes a visual image rendering based on scene visual understanding algorithm. In this approach, the color segmentation of known visual scene is carried out according to a predefined threshold, and the segmented image is processed by morphology. For this purpose, the extraction rules are formulated to screen the candidate regions. The color image is fused and filtered in the neighborhood, the pixels of the image are extracted, and the 2D texture recognition is realized by multilevel fusion and visual feature reconstruction. Using compact sampling to extract more target features, feature points are matched, the coordinate system of known image information are integrated into a unified coordinate system, and design images are generated to complete art-aided design. Simulation results show that the proposed method is more accurate than the original method for extracting the information of known images, which helps to solve the problem of clearly visible output images and improves the overall design effect.

To evaluate the effect of specific nursing intervention in children with mycoplasma pneumonia (MP), a feature extraction algorithm based on gray level co-occurrence matrix (GLCM) was proposed and combined with computed tomography (CT) image texture features. Then, 98 children with MP were rolled into the observation group with 49 cases (specific nursing) and the control group with 49 cases (routine nursing). CT images based on feature extraction algorithm of optimized GLCM were used to examine the children before and after nursing intervention, and the recovery of the two groups of children was discussed. The results showed that the proportion of lung texture increase, rope shadow, ground glass shadow, atelectasis, and pleural effusion in the observation group (24.11%, 3.86%, 8.53%, 15.03%, and 3.74%) was significantly lower than that in the control group (28.53%, 10.23%, 13.34%, 21.15%, and 8.13%) after nursing ( P < 0.05 ). There were no significant differences in the proportion of small patchy shadows, large patchy consolidation shadows, and bronchiectasis between the observation group and the control group ( P > 0.05 ). In the course of nursing intervention, in the observation group, the disappearance time of cough, normal temperature, disappearance time of lung rales, and absorption time of lung shadow (2.15 ± 0.86 days, 4.81 ± 1.14 days, 3.64 ± 0.55 days, and 5.96 ± 0.62 days) were significantly shorter than those in the control group (2.87 ± 0.95 days, 3.95 ± 1.06 days, 4.51 ± 1.02 days, and 8.14 ± 1.35 days) ( P < 0.05 ). After nursing intervention, the proportion of satisfaction and total satisfaction in the experimental group (67.08% and 28.66%) was significantly higher than that in the control group (40.21% and 47.39%), while the proportion of dissatisfaction (4.26%) was significantly lower than that in the control group (12.4%) ( P < 0.05 ). To sum up, specific nursing intervention was more beneficial to improve the progress of characterization recovery and the overall recovery effect of children with MP relative to conventional nursing. CT image based on feature extraction algorithm of optimized GLCM was of good adoption value in the diagnosis and treatment of MP in children.

The identification of chronic medical conditions and its associated mortality has led to the emergence of less invasive methods for medical diagnostic imaging. This work proposes a Computer Aided Diagnostic tool useful in automatic classification of kidney images as normal, simple cysts, kidney stones and the less investigated complex cystic renal cell carcinoma. The first part of the work investigates an effective despeckling algorithm with a proposed adaptive wavelet based denoising technique. Encouraging increased PSNR values ranging from 15 dB to 24 dB were obtained. Second part of work suggests a set of wavelet coefficient based feature set which showed a classification accuracy of 92.2%, better by 20.3% to 0.8% against existing methods. The final part of the work to develop a complete tool for kidney image classification combines the proposed wavelet based features with three existing statistical based feature sets yielded a classification accuracy of 96.9%. The suggested features were extracted from the region of interest from an image set. A reduced feature set of 18 from the original size of 163 was obtained using principal component analysis and applied for training a support vector machine classifier.

This study proposes a new feature extraction model to identify CXR images of covid-19 and pneumonia has a high visual resemblance. The feature extraction model starts by using histogram equalization and average filters as lowpass features and high pass features obtained through Laplacian and LoG filters. In the next step, covariance matrix of image along with the entire features are used to produce an eigen vector that will be used as a feature vector in the classification process. The final stage is the process of testing features on the classification algorithms KNN, SVM, LDA, Naïve Bayes, and Decision Tree through a 10-foldcross validation scheme with 0.9 training data and 0.1 test data. The first experiment for the Covid-19 and normal classes shows that the proposed model is able to produce an accuracy of 96% as the comparison model with GLCM texture extraction have an accuracy value of 91%. The second test is conducted for the class Covid-19 and pneumonia and obtained an accuracy value of 89% for the proposed model and 73% for the GLCM texture extraction. Experiments proved that the proposed model successfully outperformed the GLCM texture extraction model in all of classification algorithms used.

International audience; The importance of image compression is now essential during transmission or storage processes in various data applications, especially in medical and biometric systems. To perform the effectiveness of the compression process on images and evaluate degradation caused by this process, image quality assessment becomes an important tool in image services. We note that the objective criteria in image quality depend especially on the image type and image texture composition. The actual tendency is to find metrics making better qualification on errors in compressed images and correlate with the human visual system. This paper presents an investigation to examine and evaluate image compression degradation by the use of a new tendency concept of image quality assessment based on texture and edge analysis. To perform and practice this evaluation, we compress the medical and biometric images using second-generation wavelet compression algorithms and study the degradation of texture information in these images.

Purpose: The aim of this study was to estimate a metastasis prediction image factor in non-small cell lung cancer by correlation next generation sequence gene expression level and fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography image features.Methods: RNA-sequencing data and 18F-FDG PET images of 63 patients with NSCLC (29 metastasis and 34 non-metastasis) from The Cancer Imaging Archive and The Cancer Genome Atlas Program databases were used in a combined analysis. Weighted correlation network analysis was performed to identify gene groups were related metastasis. Module was selected with high module significance. Genes selection was performed by gene function related metastasis and high AUC (AUC > 0.6). A total of 47 image features were extracted from PET images as radiomics. The relationship of Gene expression and image features were calculated by using a hypergeometric distribution test with the Pearson correlation method. Metastasis prediction model was validated by random forest algorithm using image texture features related gene expression.Results: 36 modules were identified by gene expression pattern with WGCNA assay. The modules had highest module significance was selected assay. 7 genes from selected module were identified to involve in the epithelial mesenchymal transition pathway that have important role in the cancer metastasis and had high AUC. Also, expression of these genes was related to quantitative of image feature (GLCM_contrast, -log10 P-value: 2.45~3.89). The AUC value (accuracy: 0.856 ± 0.06, AUC: 0.868 ± 0.05) was shown from the EMT-related gene and GLCM_contrast model and AUC value (accuracy: 0.842 ± 0.06, AUC: 0.838 ± 0.09) was shown from GLCM_contrast image texture model. Conclusion: GLCM_contrast image texture feature shows relationship with EMT related gene expression. We developed a model for predicting metastasis of non-small cell lung cancer using 18F-FDG PET image feature and evaluated its accuracy.

Tongue image texture analysis is the main content of inspection diagnostics in traditional Chinese medicine (TCM). The tough and tender classification for tongue image is mainly represented by image texture. The traditional extraction method of texture feature is not robust and is not suitable for variable illumination and different image acquisition equipment. So we select 'Xception' as the main network model to complete feature extraction firstly. Then, two full connection layers and a softmax layer are combined, two dropout layers are added in their middle to form a classification network. The experimental results show that the better accuracy rate can be obtained by our method.

Species loss is occurring globally at unprecedented rates, and effective conservation planning requires an understanding of landscape characteristics that determine biodiversity patterns. Habitat heterogeneity is an important determinant of species diversity, but is difficult to measure across large areas using field-based methods that are costly and logistically challenging. Satellite image texture analysis offers a cost-effective alternative for quantifying habitat heterogeneity across broad spatial scales. We tested the ability of texture measures derived from 30-m resolution Enhanced Vegetation Index (EVI) data to capture habitat heterogeneity and predict bird species richness across the conterminous United States. We used Landsat 8 satellite imagery from 2013-2017 to derive a suite of texture measures characterizing vegetation heterogeneity. Individual texture measures explained up to 21% of the variance in bird richness patterns in North American Breeding Bird Survey (BBS) data during the same time period. Texture measures were positively related to total breeding bird richness, but this relationship varied among forest, grassland, and shrubland habitat specialists. Multiple texture measures combined with mean EVI explained up to 41% of the variance in total bird richness, and models including EVI-based texture measures explained up to 10% more variance than those that included only EVI. Models that also incorporated topographic and land cover metrics further improved predictive performance, explaining up to 51% of the variance in total bird richness. A texture measure contributed predictive power and characterized landscape features that EVI and forest cover alone could not, even though the latter two were overall more important variables. Our results highlight the potential of texture measures for mapping habitat heterogeneity and species richness patterns across broad spatial extents, especially when used in conjunction with vegetation indices or land cover data. By generating 30-m resolution texture maps and modeling bird richness at a near-continental scale, we expand on previous applications of image texture measures for modeling biodiversity that were either limited in spatial extent or based on coarse-resolution imagery. Incorporating texture measures into broad-scale biodiversity models may advance our understanding of mechanisms underlying species richness patterns and improve predictions of species responses to rapid global change.

In the context of nuclear security, uranium ore concentrates (UOCs) play an important role: they are traded in large quantities and this makes their use “out of regulatory control” a possible scenario. Once an incident of illicit trafficking o f n uclear m aterial is detected, an understanding of its origin and production process is required; this implies the necessity to use analytical techniques able to measure characteristic parameters (e.g. physical, chemical, isotopic characteristics of the nuclear materials) which are referred to, in the field o f t he n uclear f orensics, a s signatures. The present study investigates the potential of image texture analysis (i.e. the angle measure technique), combined with the spectrophotometric determination of colours for the evaluation of the origin of several UOCs. The use of different multivariate statistical techniques allows the categorization of about 80 different samples into a few groups of UOCs powders, which makes this approach a promising method complementing the already established methods in nuclear forensics.

At present, rapid, nondestructive, and objective identification of unqualified salted sea cucumbers with excessive salt content is extremely difficult. Artificial identification is the most common method, which is based on observing sea cucumber deformation during recovery after applying-removing pressure contact. This study is aimed at simulating the artificial identification method and establishing an identification model to distinguish whether the salted sea cucumber exceeds the standard by means of machine vision and machine learning technology. The system for identification of salted sea cucumbers was established, which was used for delivering the standard and uniform pressure forces and collecting the deformation images of salted sea cucumbers during the recovery after pressure removal. Image texture features of contour variation were extracted based on histograms (HIS) and gray level cooccurrence matrix (GLCM), which were used to establish the identification model by combining general regression neural networks (GRNN) and support vector machine (SVM), respectively. Contour variation features of salted sea cucumbers were extracted using a specific algorithm to improve the accuracy and stability of the model. Then, the dimensionality reduction and fusion of the feature images were achieved. According to the results of the models, the SVM identification model integrated with GLCM (GLCM-SVM) was found to be optimal, with accuracy, sensitivity, and specificity of 100%, 100%, and 100%, respectively. In particular, the sensitivity reached 100%, demonstrating an excellent identification ability to excessively salted sea cucumbers of the optimized model. This study illustrated the potential for identification of salted sea cucumbers based on pressure contact by combining image texture of contour varying with machine learning.

As the detection of horse state after exercise is constantly developing, a link between blood biomarkers and infrared thermography (IRT) was investigated using advanced image texture analysis. The aim of the study was to determine which combinations of RGB (red-green-blue), YUI (brightness-UV-components), YIQ (brightness-IQ-components), and HSB (hue-saturation-brightness) color models, components, and texture features are related to the blood biomarkers of exercise effect. Twelve Polish warmblood horses underwent standardized exercise tests for six consecutive days. Both thermal images and blood samples were collected before and after each test. All 144 obtained IRT images were analyzed independently for 12 color components in four color models using eight texture-feature approaches, including 88 features. The similarity between blood biomarker levels and texture features was determined using linear regression models. In the horses’ thoracolumbar region, 12 texture features (nine in RGB, one in YIQ, and two in HSB) were related to blood biomarkers. Variance, sum of squares, and sum of variance in the RGB were highly repeatable between image processing protocols. The combination of two approaches of image texture (histogram statistics and gray-level co-occurrence matrix) and two color models (RGB, YIQ), should be considered in the application of digital image processing of equine IRT.

In this study, the application of a backscattering imaging system with different approaches of transform-based image texture analysis for the evaluation of banana quality at different ripening stages was investigated with Wavelet, Gabor and Tamura transforms. The attenuated images of the fruits were acquired using Laser Light Backscattering Imaging (LLBI) with laser diodes emitting light at three wavelengths viz 532, 660, and 830 nm. The elasticity, chlorophyll index and soluble solids content (SSC) of each sample were measured as reference parameters by using a texture analyser, a Delta Absorbance (DA) meter, and a refractometer, respectively. The performance of the extracted features from the selected transform-based image texture analysis for analysing the quality parameters of the fruit was evaluated by means of an artificial neural network (ANN) and a support vector machine (SVM). The results indicated that there were significant changes of elasticity, chlorophyll index and SSC as the ripening stages increased. Prediction model analysis showed that the Wavelet transform exhibited the most reliable results for all of the reference parameters followed by Tamura and the Gabor transform. The results also revealed that analysis using an ANN approach recorded better performance than SVM as reflected by higher coefficient of determination (R 2 ) values. Thus, this study indicated that an LLBI system with transform-based image texture analysis coupled with computational intelligence techniques can be used for the evaluation of the quality of bananas.

High Efficiency Video Coding (HEVC) shows excellent performance in improving video compression rate, but it also has considerable computational complexity. In the process of intra prediction, Coding Unit(CU) size decision and Prediction Unit(PU) prediction mode selection consume a lot of time. Aiming at these two aspects, this paper uses Sobel operator to calculate the luma gradient and texture complexity of the image. Then we determine the split methods of some CUs in advance according to the texture complexity, and optimize the PU prediction mode selection process. Experimental results show that the proposed algorithm can reduce the computation time by 49.39% compared with the algorithm in HM16.9 platform, increase the bit rate by 1.08%, and decrease the PSNR by 0.06dB, which has no impact on the video quality.

The block compressed sensing approaches based on matrix permutations reduce storage space costs, reduce transmission costs, and support the high quality reconstruction of images by reducing the blocking artifact. An enhancement in the literature includes the block compressed sensing method based on ripple matrix permutations, which further balances the textures of sub-blocks. Based on the block compressed sensing method by ripple matrix permutations, this paper proposes a novel method for high-frequency images that uses the energy distribution characteristics of high-low frequency images. The proposed method makes full use of this characteristic and performs matrix permutations only on the high-frequency image. This method initially performs a wavelet decomposition on the image. Subsequently, the transformed high-frequency image is subjected to a ripple matrix permutation to achieve texture balancing. Finally, the compressed sensing processing is performed on the high-frequency image. The simulation results show that the high-frequency part of the image wavelet domain is texture balanced while the low-frequency part remains unchanged. The image is reconstructed after the compressed sensing step, and the image quality is significantly improved.

Up-to-date information regarding impervious surface is valuable for urban planning and management. The objective of this study is to develop neural computing models used for automatic impervious surface area detection at a regional scale. To achieve this task, advanced optimizers of adaptive moment estimation (Adam), a variation of Adam called Adamax, Nesterov-accelerated adaptive moment estimation (Nadam), Adam with decoupled weight decay (AdamW), and a new exponential moving average variant (AMSGrad) are used to train the artificial neural network models employed for impervious surface detection. These advanced optimizers are benchmarked with the conventional gradient descent with momentum (GDM). Remotely sensed images collected from Sentinel-2 satellite for the study area of Da Nang city (Vietnam) are used to construct and verify the proposed approach. Moreover, texture descriptors including statistical measurements of color channels and binary gradient contour are employed to extract useful features for the neural computing model-based pattern recognition. Experimental result supported by statistical test points out that the Nadam optimizer-based neural computing model has achieved the most desired predictive accuracy for the data collected in the studied region with classification accuracy rate of 97.331%, precision = 0.961, recall = 0.984, negative predictive value = 0.985, and F1 score = 0.972. Therefore, the model developed in this study can be a helpful tool for decision-makers in the task of urban land-use planning and management.

Image processing has played a relevant role in various industries, where the main challenge is to extract specific features from images. Specifically, texture characterizes the phenomenon of the occurrence of a pattern along the spatial distribution, taking into account the intensities of the pixels for which it has been applied in classification and segmentation tasks. Therefore, several feature extraction methods have been proposed in recent decades, but few of them rely on entropy, which is a measure of uncertainty. Moreover, entropy algorithms have been little explored in bidimensional data. Nevertheless, there is a growing interest in developing algorithms to solve current limits, since Shannon Entropy does not consider spatial information, and SampEn2D generates unreliable values in small sizes. We introduce a proposed algorithm, EspEn (Espinosa Entropy), to measure the irregularity present in two-dimensional data, where the calculation requires setting the parameters as follows: m (length of square window), r (tolerance threshold), and ρ (percentage of similarity). Three experiments were performed, the first two were on simulated images contaminated with different noise levels. The last experiment was with grayscale images from the Normalized Brodatz Texture database (NBT). First, we compared the performance of EspEn against the entropy of Shannon and SampEn2D. Second, we evaluated the dependence of EspEn on variations of the values of the parameters m, r, and ρ. Third, we evaluated the EspEn algorithm on NBT images. The results revealed that EspEn could discriminate images with different size and degrees of noise. Finally, EspEn provides an alternative algorithm to quantify the irregularity in 2D data, the recommended parameters for better performance are m = 3, r = 20, and ρ = 0.7.