Texture Model Research Articles

Using a Portable Autostereoscopic Screen to Improve Anatomy Teaching and Learning

Conventional anatomical models and cadaveric specimens can be time-consuming and resource intensive for any anatomical institute. In recent years, there has been a push for more flexible and varied approaches to teaching, including problem-based and computer-aided learning, which includes web-based anatomical models or the use of three-dimensional visualization technology. With advances in hardware, autostereoscopic (AS) 3D screens have become more affordable, portable, and accessible to individuals, not just institutes. At King’s College London (KCL), we developed the Virtual Anatomy and Histology (VAH) platform—an online resource which focuses on perspective volumetric 3D viewing of medical scan data and 3D models to facilitate the online teaching and learning of anatomy. This paper presents the features of VAH and details a study that was conducted in 2022, to evaluate the VAH 3D AS viewer configured with The Looking Glass Portrait (TLG) (Looking Glass, New York, NY, USA) 8-inch AS display. We tested the hypothesis that using an AS display can improve spatial understanding of cardiovascular anatomy. A cardiovascular 3D textured model was used from our gallery to carry out a spatial test. Twenty current healthcare students at King’s participated in the study and completed a structured questionnaire. Results showed that 47.6% and 52.4% of participants agreed and strongly agreed, respectively, that identifying anatomical structures was easier in 3D compared to 2D. Qualitative feedback was positive as most students found King’s VAH and TLG display “useful for people who need help with spatial understanding” and that “it was a good tool to test your anatomical knowledge”. In conclusion, based on the quantitative results and feedback, we are optimistic that King’s VAH and portable AS displays can be beneficial in anatomy education. With the increasing availability of such systems and competitive pricing, this technology is likely to have a significant impact in education in coming years.

MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study

BackgroundTo develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function.MethodsA retrospective study of 174 diabetic patients (training cohort, n = 123; validation cohort, n = 51) who underwent renal MRI scans was included. They were assigned to normal function (n = 71), mild or moderate impairment (n = 69), and severe impairment groups (n = 34) according to renal function. Four methods of kidney segmentation on T2-weighted images (T2WI) were compared, including regions of interest covering all coronal slices (All-K), the largest coronal slices (LC-K), and subregions of the largest coronal slices (TLCO-K and PIZZA-K). The speeded-up robust features (SURF) and support vector machine (SVM) algorithms were used for texture feature extraction and model construction, respectively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of models.ResultsThe models based on LC-K and All-K achieved the nonsignificantly highest accuracy in the classification of renal function (all p values > 0.05). The optimal model yielded high performance in classifying the normal function, mild or moderate impairment, and severe impairment, with an area under the curve of 0.938 (95% confidence interval [CI] 0.935–0.940), 0.919 (95%CI 0.916–0.922), and 0.959 (95%CI 0.956–0.962) in the training cohorts, respectively, as well as 0.802 (95%CI 0.800–0.807), 0.852 (95%CI 0.846–0.857), and 0.863 (95%CI 0.857–0.887) in the validation cohorts, respectively.ConclusionWe developed and internally validated an MRI-based machine-learning model that can accurately evaluate renal function. Once externally validated, this model has the potential to facilitate the monitoring of patients with impaired renal function.

Origin of clinopyroxene-ilmenite symplectites in mafic granulites from eastern parts of the Chotanagpur granite gneissic complex, East Indian shield

Abstract In this manuscript, we report a rare occurrence of rod-like intergrowths of clinopyroxene-ilmenite that variably replace titanite in a suite of high-pressure mafic granulites from the Chotanagpur Granite Gneissic Complex, Eastern Indian Shield. Garnet proximal to the clinopyroxene-ilmenite intergrowth is invariably replaced with symplectic clinopyroxene-plagioclase or a rind of plagioclase. Textural modeling of the reaction textures and mineral compositions suggests that the garnet-titanite became unstable and the ilmenite-clinopyroxene preferentially develop after titanite. The presence and texture of halogen rich apatites within the ilmenite-clinopyroxene symplectite are consistent with a fluid mediated process. Thermodynamic modeling in the NCFMAST (+H2O) system, demonstrates that the clinopyroxene-ilmenite symplectite was formed along a steeply decompressive retrograde P-T path (from ~13 to ~7 kbar, at ~800 °C), in the presence of partial melts. The study demonstrates that relative to Fe, Mg, and Ca, Ti was less mobile and that the mobility of Ti was restricted within the confines of titanite being replaced by the clinopyroxene-ilmenite symplectite. This study implies that besides the ligand activity, the fluid/rock ratio exerts a strong control on the length scale of Ti transport in natural rocks.

ESTABLISHMENT AND ANALYSIS OF FRACTAL CONTACT MODEL FOR POINT GRINDING PARTS

In this work, we establish a fractal contact model and analyze the effect of the parameters on stress. The study provided a method to make a link between contact characteristics and surface condition. The surface texture model was built based on the process mechanism of point grinding. The experiment for the effect of the surface texture on contour height was carried out on TR300 roughness shape-measuring instrument and the structural function method of fractal theory was used to obtain the fractal dimensions and scale coefficients of surfaces with different surface textures. The fractal contact model was established based on the fractal characteristics of the surface point ground and the simulation was carried out using Matlab. Furthermore, the validation of compression experiment was carried out by WAW-600C universal testing machine, the high-definition pictures before and after the experiment were obtained by KEYENCE VH-Z500R ultra-depth-of-field 3D microscope and the surface topographies before and after the experiment were obtained by NANOVEA PS50 three-dimensional noncontact surface profile meter. The obtained findings can help to choose appropriate surface with the best contact characteristics under certain condition.

Modeling and simulation of surface generation in manufacturing

The paper describes the state-of-the-art in modeling and simulation of surface texture and topography generation at micro and nano dimensional scales. Three main classes of manufacturing processes used for the generation of engineering surfaces are considered: material removal processes, material conservative processes, and material additive processes. Types of modeling techniques for the simulation of surface generation are reviewed and discussed including analytical models, numerical multi-physics models, and data-driven methods. After presenting the application of those modeling techniques for the prediction of characteristics and geometry of surfaces generated by different manufacturing processes, their performance, implementation, and accuracy are discussed. Finally, a roadmap for the realization of a complete surface generation digital twin in manufacturing is outlined.

Virtual reality learning system for remote rock mass mapping

Rock mass quality mapping is essential in rock engineering and mining projects. However, the current manual mapping approach and its teaching are restricted due to the dangerous nature of work near rock walls and the constraints of resources. Therefore, a virtual reality learning system for teaching remote mapping of rock mass quality was developed at Aalto University. Two rock wall sections of an underground tunnel and a roadside rock-cut were scanned using photogrammetry and imported as high-resolution textured 3D models. The user wears a head-mounted display and performs mapping using a pointing device. The system collects the measurements automatically and assists the user in rock mass mapping. Along with the development of the system, qualitative research was conducted to evaluate user performance in a gamified learning system. The results demonstrate that with proper game elements implemented, the learning system can be used to steer student behaviour towards the target range. Once set up, the VR system is a tireless, user-paced aid in training and teaching.

SKLBP14: A new textural environmental sound classification model based on a square-kernelled local binary pattern

SKLBP14: A new textural environmental sound classification model based on a square-kernelled local binary pattern

Detection of geometric properties of discontinuities on the Špičunak rock slope (Croatia) using high-resolution 3D Point Cloud generated from Terrestrial Laser Scanning

Rock mass characterization is a very important part of engineering geological investigation. For a better understanding of the rock mass behaviour, it is crucially important to obtain as much as possible information about the discontinuity network, especially about orientation and the number of dominant discontinuity sets. The traditional methodology includes field mapping which dominantly produces a limited amount of data and consequently only a rough estimate about discontinuity network. To increase the number of measurements and to eliminate orientation bias, rock mass on the Špičunak rock slope in Gorski kotar, Croatia, was analysed using a combination of 3D Point Cloud and Textured Mesh Model generated from 3D Point Cloud by Poisson surface reconstruction. Both models were obtained from Terrestrial Laser Scanning. Two considerably different parts of a rock slope, with different weathering conditions and different degrees of fracturing were mapped. Discontinuities were mapped in the field and on the models using manual mapping techniques and semi-automated methods. Manual mapping on a 3D Point Cloud and Textured Mesh Model was done by Compass plugin and by Trace a polyline tool in Cloud Compare software version V2.12 and semi-automated mapping methods were done by Discontinuity Set Extractor and qFacet Fast Marching plugin for Cloud Compare software version V2.12. This study was used to show how the application of different methodologies, for the detection of geometric properties of discontinuities, influences the result. Statistical analyses were performed on the collected data to determine differences in the accuracy between the mapping techniques. Manual mapping on the 3D Point Cloud and high-resolution Textured Mesh Model showed good agreement with field measurements, apart from the higher number of discontinuities mapped by remote sensing methods. On the other hand, significant deviations were found between manual and semi-automated mapping techniques. Semiautomated methods did not correctly detect certain discontinuities, especially bedding planes that are perpendicular to a rock face. Also, semi-automated methods overestimate the number of discontinuity sets, especially in a highly weathered and highly fractured rock mass. These differences between methods can influence kinematic analysis results. Based on the results, an appropriate methodology was proposed to utilize the advantages of both manual and semiautomated methods. The proposed approach presents a powerful tool to accurately map and measure discontinuity orientation with results comparable to the field measurements.

An Efficient Transformer Based on Global and Local Self-attention for Face Photo-Sketch Synthesis.

Face photo-sketch synthesis tasks have been dominated by convolutional neural networks (CNNs), especially CNN-based generative adversarial networks (GANs), because of their strong texture modeling capabilities and thus their ability to generate more realistic face photos/sketches beyond traditional methods. However, due to CNNs' locality and spatial invariance properties, there have weaknesses in capturing the global and structural information which are extremely important for face images. Inspired by the recent phenomenal success of the Transformer in vision tasks, we propose replacing CNNs with Transformers that are able to model long-range dependencies to synthesize more structured and realistic face images. However, the existing vision Transformers are mainly designed for high-level vision tasks and lack the dense prediction ability to generate high resolution images due to the quadratic computational complexity of their self-attention mechanism. In addition, the original Transformer is not capable of modeling local correlations which is an important skill for image generation. To address these challenges, we propose two types of memory-friendly Transformer encoders, one for processing local correlations via local self-attention and another for modeling global information via global self-attention. By integrating the two proposed Transformer encoders, we present an efficient GL-Transformer for face photo-sketch synthesis, which can synthesize realistic face photo/sketch images from coarse to fine. Extensive experiments demonstrate that our model achieves a comparable or better performance beyond the state-of-the-art CNN-based methods both qualitatively and quantitatively.

Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography

BackgroundChest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering. Material and methodWe used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm. ResultsSwin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity. ConclusionsOur handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate that our presented swin-textural is a self-organized, highly accurate, and lightweight image classification model and is better than the compared deep learning models for this dataset.

Texture segmentation method for computer-assisted dermatologic diagnostic system

The wide spread of dermatological diseases is an important medical and social problem. Doctors note the constant growth of psoriasis among people of all ages. The psoriasis disease symptoms are similar to the symptoms of such diseases as eczema, atopic dermatitis and medicinal disease. Therefore, there is a high probability of an error in the disease diagnosis, which prevents the full treatment and prevention of the disease. Dermatological diagnostic systems are decision-making support systems for dermatologists when establishing a diagnosis and assessing the severity of the disease course. The development of new image processing methods for dermatological diagnostic systems is an important task, which allow to increase the accuracy of the diagnostic decision. In this work, the segmentation method of psoriasis images for systems of medical dermatological diagnostics based on a vector-difference approach to improve the quality of segmentation was developed. The vector-difference approach allows to calculate a certain texture feature of the image as a vector transformation of various texture features by linear algebra methods. Psoriasis disease images can be described by texture (spectral, statistical, spectral-statistical) and color, so it is proposed to take into account textural and color characteristics of images for image segmentation. The color models that are most often used in segmentation methods of psoriasis disease images were analyzed. Based on the analysis, the Hue-Saturation-Intensity color model was chosen.It is proposed to use spectral, statistical and spectral-statistical texture models and color characteristics of image pixels to represent psoriasis disease images. The developed segmentation method includes the following stages: image pre-processing; identification; vector-difference transformation; threshold processing. At the pre-processing stage, homomorphic filtering was applied to psoriasis disease images. The result of the identification stage is a set of features calculated by the textural and color characteristics for image objects. The vector-difference transformation converts the texture image into intensity. Threshold processing is performed with a global threshold. Experimental research of the proposed segmentation method of psoriasis disease images was performed. As a result of the experimental research, it was found that the probability of correct identification of psoriasis disease area on average is 0.97, the probability of a false alarm is about 0.05.

Texture and material classification with multi-scale ternary and septenary patterns

This paper ideates, inspired by LBP and its variants, a novel local feature extraction operator for texture classification, referred to as Multi-scale Ternary and Septenary Pattern (MTSP). MTSP is a histogram-based feature reṕresentation that is composed of two single-scale STP and SSP (single-scale ternary and septenary patterns, respectively) encoders designed according to a novel set theory based pattern encoding scheme that integrates the concepts of both LQP’s and LTP’s operators. The essence of STP and SSP is to compute several virtual pixels based on various local and global image statistics and progressively encode interactions between local and non-local pixels by examining the directional information and differential excitation according to relationships between adjacent pixels rearranged in a variety of spatial arrangements. Unlike various parametric state-of-the-art texture operators that perform thresholding based on static thresholds, MTSP incorporates dynamic thresholds estimated automatically. MTSP descriptor has good ability as faithfully as possible to capture more detailed image information via complementary texture information generated from the fusion of both STP and SSP encoders. Experimental results show that MTSP ensures reliable performance stability over ten texture datasets and against several recent representative methods. In addition, the performance of MTSP is further proved statistically via the Wilcoxon signed rank test demonstrating thus that MTSP is a good candidate for texture modeling.

Fluctuation-based fracture mechanics of heterogeneous materials.

We present results of a hybrid analytical-simulation investigation of the fracture resistance of heterogeneous materials. We show that bond-energy fluctuations sampled by Monte Carlo simulations in the semigrand canonical ensemble provide a means to rationalize the complexity of heterogeneous fracture processes, encompassing probability and percolation theories of fracture. For a number of random and textured model materials, we derive upper and lower bounds of fracture resistance and link bond fracture fluctuations to statistical descriptors of heterogeneity, such as two-point correlation functions, to identify the origin of toughening mechanisms. This includes a shift from short- to long-range interactions of bond fracture processes in random systems to the transition from critical to subcritical bond fracture percolation in textured materials and the activation of toughness reserves at compliant interfaces. Induced by elastic mismatch, they connect to a number of disparate experimental observations, including toughening of brittle solids by deformable polymers or organics in, e.g., gas shale, nacre; stress-induced transformational toughening in ceramics; and toughening of sparse elastic networks in hydrogels, to name a few.

3D EDGE DETECTION AND COMPARISON USING FOUR-CHANNEL IMAGES

Abstract. Point cloud segmentation, is a widespread field of research and it is useful in several research topics and applications such as 3D point cloud analysis, scene understanding, semantic segmentation etc. Architectural vector drawings constitute a valuable platform source for scientists and craftsmen while the production of such drawings is time-consuming because many of the creation steps are done manually. Detecting 3D edges in point clouds could provide useful information for the automation of the creation of 3D architectural vector drawings. Hence, a 3D edge detection method is proposed and evaluated with a proof-of-concept experiment and another one using a professional software. The scope of this effort is twofold, firstly the production of semantically enriched 3D dense point clouds exploiting four-channel images in order to detect 3D edges and secondly the comparison of the detected 3D edges with their corresponding edges in a textured 3D model. Comparing 3D edges in the early step of the 3D dense point cloud production and in the final step of 3D textured mesh, provides useful conclusions of the data used for the automatic creation of 3D drawings. Both of the experiments i.e., the proof-of-concept and using the professional SfM-MVS software were conducted using real world data of cultural heritage objects.

Design of surface micro texture in the cutter-chip contact area of a cemented carbide cutter

Placing micro textures on a cutter surface can enhance the cutter-chip and cutter-workpiece contact, improving the cutter performance and machined surface quality. However, both the proper placement of micro textures and careful modeling of their density function are required for optimizing the cutting performance. In this study, a ball end milling cutter was utilized to demonstrate how these can be achieved. Initially, the theoretical cutter-chip contact length of the was calculated based on the chip curl theory and the theoretical cutter-chip contact area was obtained by combining the theories of cutter contact area and micro texture placement. Then, a mathematical model describing the micro texture distribution was established using a uniform density function, which allowed the determination of the specific distribution model for the micro textures. Finally, both simulations and experiments were used to validate the accuracy of the theoretical solution for the cutter-chip contact area and the uniformity of the micro texture density function. The results showed that the theoretical cutter-chip contact area and the uniform micro texture density mathematical model are consistent with the simulation and experimental results. The results reported in this paper provide a theoretical basis for the accurate preparation of micro textures.

Geospatial modeling and mapping of soil organic carbon and texture from spectroradiometric data in Nile basin

Rapid soil properties monitoring and modeling are crucial for sustainable soil management interventions. The analysis of soil properties in the laboratory using standard procedures is time-consuming and requires the use of chemicals. The research aimed to develop a regression model based on the visible-near-infrared-short-wave infrared (Vis-NIR-SWIR, 0.35–2.5 μm) spectral reflectance of soils in Aba Gerima (425 ha), a highland of the Ethiopian Nile Basin. Soil samples were obtained at a 0–20 cm depth from 101 plots in the study area and analyzed for soil organic carbon (SOC) and texture using standard laboratory procedures. Soil reflectance was measured using a spectroradiometer on the air-dried, ground, and sieved soils. ArcGIS software 10.5 was used to conduct the semi-variogram and geostatistical methods—Ordinary Kriging (OK) and Least Distance Weighting (IDW). Strong degrees of spatial dependence are depicted through semi-variogram models. This outcome illustrates a strong spatial dependence and continuity between nearby soil samples. The prediction accuracy of the model based on the values of coefficient of determination (R2) and root mean square error (RMSE) was explained. For clay content, the R2 and RMSE obtained in the Vis-NIR-SWIR were excellent (0.91) and 2.93. An R2 of 0.85 with an RMSE of 1.96 was obtained in predicting sand. The SOC model established indicated an R2 of 0.94 and an RMSEC of 0.01. The generated maps showed that the soil characteristics' spatial variability was adequate to forecast the values of soil fertility indicators in the research area and nearby regions. The soil maps may assist farmers and policymakers in identifying soil nutrient status, improving soil management strategies, rising crop productivity, and guaranteeing ecological integrity. Moreover, further study is required to corroborate our findings and improve the prediction performance of Vis-NIR-SWIR spectroscopy to help better land management interventions.

Evaluation of Three-Dimensional Surface Roughness in Microgroove Based on Bidimensional Empirical Mode Decomposition.

Micromilling is an extremely important advanced manufacturing technology in the micromanufacturing industry. Compared with the traditional milling process, micromilling has stricter requirements on the surface roughness of the workpiece, and the roughness of the microcurved surface is not easy to measure. In order to more accurately characterize the curved surface morphology of the microgrooves obtained by micromilling, this paper proposes a method to extract the reference plane of the curved surface based on the bidimensional empirical mode decomposition algorithm and characterize the three-dimensional surface roughness of the curved surface. First, we synthesize the morphologies of the microgrooves by simulated non-Gaussian rough surfaces and models of textures. Second, the bidimensional empirical mode decomposition algorithm was used to extract the reference planes of the simulated microgrooves. Third, the three-dimensional roughness parameters suitable for the curved surfaces of microgrooves were selected to establish an evaluation system. The results show that the mean squared errors of the reference planes are below 1%, so bidimensional empirical mode decomposition can effectively extract reference planes, and the evaluation system of three-dimensional surface roughness proposed in this paper reflects morphological characteristics of the curved surfaces of microgrooves more thoroughly than that of two-dimensional surface roughness parameters.

Image-based geometric digital twinning for stone masonry elements

We present an image-based pipeline for generating geometrical digital twins (GDTs) of stone masonry elements with detail down to the stone level. For this purpose, we acquire RGB images of the individual stones and of the wall during the construction phase. In our framework, we use structure from motion (SfM) to first generate 3D source and destination models, which are then registered to form the GDT through non-linear least squares and 2D point feature correspondences detected on the SfM images. This method contrasts with traditional techniques that register point clouds using 3D point descriptors. Because of the robustness of image feature descriptors, we found that using 2D instead of 3D point features facilitates the automation of the GDT generation. To benchmark our algorithm, we compared the results through an Euclidean–distance-based proposed metric with a known 3D textured model from which images were synthetically generated. We show the robustness and feasibility of our method for full size elements, wherein GDTs were generated for dry-stone and stone-mortar systems. This study allows researchers to produce accurate representations of the 3D geometry of walls built for experimental research, reducing therefore uncertainties related to the stone size, shape and arrangement to a minimum when comparing 3D numerical simulations of these walls to experimental results. Codes and data sets are publicly available (https://github.com/eesd-epfl/stone_masonry_GDT and https://doi.org/10.5281/zenodo.7266587).

Drag Reduction Analysis of the Hydrostatic Bearing with Surface Micro Textures

Surface microtexturing has been widely used due to its good hydrophobic or drag reduction characteristics, and become an effective method to improve product performance and reduce energy consumption. This paper mainly discusses the improvement of microtextures on the dynamic pressure characteristics of hydrostatic bearings, and explores the effects of texture parameters on carrying capacity, macroscopic wall two-plane shear force, cavity area and other factors. In the oil film model calculation of the smooth wall surface of the radial hydrostatic bearing under the action of high speed and large external load, the oil film divergent wedge often has a negative pressure area, which is obviously not in line with the actual situation, so the cavitation effect needs to be considered. The CFD analysis method of the “gas-oil” two-phase flow model was carried out by using the mixture model to seek the optimal texture model scheme and thus to improve the load carrying capacity (LCC) and reduce the wall shear force. The effects of the texture area arrangement and geometric parameters on the lubrication characteristics were compared and analyzed. It is found that the carrying capacity of local texture is better than that of global texture, and different texture arrangements can achieve better drag reduction rates. The work presented in this paper studies the lubrication of the surface texture of a hydrostatic bearing. Taking the oil film carrying capacity and shear force as the target parameters, the factors, such as texture morphology, geometric parameters, texture distribution and cavitation phenomenon, are investigated through simulation and experimental methods. The surface textured hydrostatic bearing is expected to obtain the maximum oil film carrying capacity and the minimum friction resistance. The analysis results show that by arranging the partial streamwise texture at the rear end of the diverging wedge, the maximum shear force of the wall can be reduced by about 15%, and the LCC can be increased by about 18%.

Texture Analysis of CT Images in Head and Neck Tumors Differentiation

Objective: to determine the diagnostic significance of computed tomography texture analysis (CTTA) in differentiating head and neck tumors.Material and methods. The study included 118 patients aged from 4 to 80 years with a verified diagnosis of benign and malignant (37 and 81, respectively) head and neck tumors. CTTA was performed using the LIFEx program, version 6.30. Thirty eight (38) texture indices extracted from routine CT images were tested by regression analysis with creation of logistic texture models with associations of four indices as independent predictors.Results. The possibility of using derived models – probability textural indices for benign and malignant tumors differentiation was established: area under ROC-curve (AUC) 0.854 ± 0.035 (p < 0.001); for differentiation of locally spread from locally limited tumors: AUC 0.840 ± 0.049 (p < 0.001); for differentiation of moderately, poorly, and undifferentiated cancer (G2, G3, G4) from well-differentiated (G1) head and neck cancer: AUC 0.826 ± 0.085 (p < 0.001).Conclusion. CT images texture analysis allows to make non-invasive prognosis of benign or malignant nature of a visualized head and neck tumor, as well as to determine the extent and degree of tumor malignancy.