Scale Texture Research Articles

Computerize classification of Benign and malignant thyroid nodules by ultrasound imaging

Introduction: Early detection and treatment of thyroid nodules increase the cure rate and provide optimal treatment. Ultrasound is the chosen imaging technique for assessment of thyroid nodules. Confirmation of the diagnosis usually demands repeated fine needle aspiration biopsy (FNAB). So, current management, has morbidity and non zero mortality. The goal of the present study is to explore diagnostic potential of automatic texture analysis (TA) methods in differentiation benign and malignant thyroid nodules by ultrasound imaging in order to help for reliable diagnosis and monitoring of the thyroid nodules with no need more biopsy. Materials and Methods: The database consist of 70 thyroid patients (26 benign and 44 malignant). They under- went thyroid sonography which were reported by radiologist and proven by the biopsy. One ultrasound image per patient was loaded in Mazda Software version 4.6 for automatic texture analysis. Regions of interests (ROIs) were defined within the abnormal part of the thyroid nodules ultrasound images. Gray levels intensity within a ROIs normalized and then up to 270 multi scale texture features parameters per ROIs per normalization schemes were computed via statistical methods employed in Mazda software. From the statistical point of view, all calculated texture features parameters are not useful for texture analysis. So, the features based on F: maximum Fisher coefficient and P: minimum probability of classification error and average correlation coefficients (POE+ACC) eliminated to 10 best and most effective features per normalization schemes. We analyze this feature under two standardization states standard (S) and nonstandard (nS) with Linear Discriminate Analysis (LDA) and None Linear Discriminate Analysis (NDA). The first nearest neighbor (1NN) and artificial neural network (A-NN) classifier were performed for features obtained via LDA and NDA respectively to differential diagnosis benign versus malignant thyroid nodules. The confusion matrix applied between visual and automated texture analysis results to find out sensitivity and specificity of the applied texture analysis methods under different applied options. The Receiver operating characteristic (ROC) curve analysis used for comparison discrimination performance of the employed texture analysis methods.Results: The results demonstrated the influence of the normalization, reduction and standardization on the effectiveness of the obtained features in discrimination tasks by LDA and NDA texture analysis.In comparison with LDA, The selected subset features represent the highest discrimination performance for NDA in distinguishing benign from malignant thyroid with sensitivity of 94%, specificity of 100%, and Az value of 0.97. It means that via our method 97% patient with thyroid nodules lesions can be diagnostic without doing clinical biopsy. For LDA, this discrimination result has sensitivity of 85.7%, specificity of 87.5%, and Az value of 0.86.Conclusion: Our results indicate Computer aided diagnosis can provide useful information to help radiologists in classification of benign and malignant thyroid nodules.

Classification of scaled texture patterns with transfer learning

Classification of texture patterns with large scale variations poses a great challenge for expert and intelligent systems. A pure learning approach addresses this issue by including texture patterns at all scales in the training dataset. This approach makes the construction of an expert system quite costly and unrealistic given the large variations in real-world texture scales and patterns. We propose a transfer learning approach where the full range of texture scales is available only for a small subset of the texture classes. Such a subset is used to learn the scaling map through partial least-square regression or coupled dictionary learning. Experimental results on classifiers equipped with the learned maps show promising reduction in training data scale variability with improved classification accuracy compared to the data-intensive pure learning approach. The proposed approach can be followed to build image-based expert systems of reasonable accuracy and limited data requirements.

Controls on soil arylsulfatase activity at a regional scale

Soil arylsulfatase (ARS) is an important enzyme that controls the acquisition of organic sulfur and thus the soil sulfur cycling. However, compared to those enzymes that involved in the acquisition of organic carbon, nitrogen, and phosphorus, ARS activity and its regulations have rarely been investigated. Here, a regional-scale investigation on soil ARS activity was conducted in southwest China to determine the major controls on soil ARS activity. Results showed that soil ARS activity increased from cropland through to forest following post-agriculture succession, and was significantly lower in clasolite than in dolomite or limestone. Among 14 studied soil variables, five variables (i.e. soil pH, exchangeable calcium, organic carbon, clay and silt) showed significant relationships with ARS activity, and these relationships were non-linear. Path analysis further showed that the activity of soil ARS was most controlled by soil pH and soil texture, but soil calcium and organic carbon only had indirect or spurious effects on ARS activity. Differing from most previous studies that used linear analysis, our results highlight that non-linear analysis is a better way for exploring the controlling factors of soil ARS activity, and we suggest that at the regional scale, soil pH and soil texture may be the most sensitive indicators of soil ARS activity.

Quantitative assessment of myocardial scar heterogeneity using cardiovascular magnetic resonance texture analysis to risk stratify patients post-myocardial infarction

To determine whether heterogeneity of cardiac scar, as assessed by cardiovascular magnetic resonance (CMR) texture analysis, may provide insight into better risk stratification for patients with previous myocardial infarction (MI). Patients with previous MI (n=76) were followed for a median of 371.5 days after late gadolinium enhancement (LGE) CMR. The primary endpoint was a composite of ventricular tachycardia, ventricular fibrillation, or unexplained syncope. Areas of LGE were identified and manually segmented on a short-axis projection. The characteristics of the scar heterogeneity were evaluated via CMR texture analysis. This is a filtration-histogram technique, where images are filtered using the Laplacian of a Gaussian filter to extract features different sizes (2-6 mm in radius) corresponding to fine, medium, and coarse texture scales followed by a quantification step using histogram analysis (skewness and kurtosis). Patients suffering arrhythmic events during the follow-up period demonstrated significantly higher kurtosis (coarse-scale, p=0.005) and lower skewness (fine-scale, p=0.046) compared to those suffering no arrhythmic events. Furthermore, Kaplan-Meier analysis showed significantly higher coarse kurtosis (p=0.004), and lower fine skewness (p=0.035) were able to predict increased incidence of ventricular arrhythmic events. In this pilot study, indices of texture analysis reflecting textural heterogeneity were significantly associated with a greater incidence of arrhythmic events. Further work is required to delineate the role of texture analysis techniques in risk stratification post-MI.

Are the number and size of scales in Liolaemus lizards driven by climate?

Ectothermic vertebrates are sensitive to thermal fluctuations in the environments where they occur. To buffer these fluctuations, ectotherms use different strategies, including the integument, which is a barrier that minimizes temperature exchange between the inner body and the surrounding air. In lizards, this barrier is constituted by keratinized scales of variable size, shape and texture, and its main function is protection, water loss avoidance and thermoregulation. The size of scales in lizards has been proposed to vary in relation to climatic gradients; however, it has also been observed that in some groups of Iguanian lizards it could be related to phylogeny. Thus, here, we studied the area and number of scales (dorsal and ventral) of 60 species of Liolaemus lizards distributed in a broad latitudinal and altitudinal gradient to determine the nature of the variation of the scales with climate, and found that the number and size of scales are related to climatic variables, such as temperature and geographical variables as altitude. The evolutionary process that best explained how these morphological variables evolved was the Ornstein-Uhlenbeck model. The number of scales seemed to be related to common ancestry, whereas dorsal and ventral scale areas seemed to vary as a consequence of ecological traits. In fact, the ventral area is less exposed to climate conditions such as ultraviolet radiation or wind and is, thus, under less pressure to change in response to alterations in external conditions. It is possible that scale ornamentation, such as keels and granulosity, may bring some more information in this regard.

Texture compression and scaling both contribute to perception of 3D slant from texture

Texture compression and scaling both contribute to perception of 3D slant from texture

Neighbourhood decision based impulse noise filter

A novel impulse noise filter that preserves the image details and effectively suppresses high-density noise has been proposed in this work. The proposed filter works in two phases: (i) noise pixel detection phase and (ii) noise pixel restoration phase. In the detection phase, the impulse noise corrupted pixels are detected using a neighbourhood decision approach. In the second phase, the true values of corrupted pixels are restored using a first-order neighbourhood decision approach. Experiments are carried out with both grey scale and colour images of various resolutions, texture and structures. The proposed scheme has high peak-signal-to-noise ratio and better visual quality in comparison to the standard median filter, modified decision based unsymmetrical trimmed median filter and improved fast peer-group filter with a varying noise density from 10 to 90%.

Numerical determination of hysteresis friction on different length scales and comparison to experiments

The interaction between tread materials of tires and rough pavement structures mainly depends on the characteristics of the road surface, the slip velocity, the contact pressure, temperature conditions and the elastomeric material itself. The subsequent description introduces two different numerical approaches, a three-dimensional multiscale homogenization methodology, which considers the asperities at different length scales of the surface texture and a microscale approach, which reduces the surface texture to a two-dimensional microscopic characterization. The results of the numerical approaches, which base on the same input information, are compared to experimental data in order to validate the outcome. Since local stress concentrations are the origin of wear, a realistic modeling of rubber friction on rough surfaces concerning different length scales of surface texture provides an important input to a subsequent modeling of wear of the tire tread as well as of the pavement asperities.

Uric acid versus non-uric acid urinary stones: differentiation with single energy CT texture analysis

To evaluate the accuracy of computed tomography (CT) texture analysis (TA) to differentiate uric acid (UA) stones from non-UA stones on unenhanced CT in patients with urinary calculi with exvivo Fourier transform infrared spectroscopy (FTIR) as the reference standard. Fourteen patients with 18 UA stones and 31 patients with 32 non-UA stones were included. All the patients had preoperative CT evaluation and subsequent surgical removal of the stones. CTTA was performed on CT images using commercially available research software. Each texture feature was evaluated using the non-parametric Mann-Whitney test. Receiver operating characteristic (ROC) curves were created and the area under the ROC curve (AUC) was calculated for texture parameters that were significantly different. The features were used to train support vector machine (SVM) classifiers. Diagnostic accuracy was evaluated. Compared to non-UA stones, UA stones had significantly lower mean, standard deviation and mean of positive pixels but higher kurtosis (p<0.001) on both unfiltered and filtered texture scales. There were no significant differences in entropy or skewness between UA and non-UA stones. The average SVM accuracy of texture features for differentiating UA from non-UA stones ranged from 88% to 92% (after 10-fold cross validation). A model incorporating standard deviation, skewness, and kurtosis from unfiltered texture scale images resulted in an AUC of 0.965±00.029 with a sensitivity of 94.4% and specificity of 93.7%. CTTA can be used to accurately differentiate UA stones from non-UA stones invivo using unenhanced CT images.

Study on Multi-Scale Window Determination for GLCM Texture Description in High-Resolution Remote Sensing Image Geo-Analysis Supported by GIS and Domain Knowledge

Texture features based on the gray-level co-occurrence matrix (GLCM) can effectively improve classification accuracy in geographical analyses of optical remote sensing (RS) images, with the parameters of scale of the GLCM texture window greatly affecting the validity. By analyzing human visual attention characteristics for geo-texture cognition, it was found that there is a strong correlation between the texture scale parameters and the domain shape knowledge in a specified geo-scene. Therefore, a new approach for quickly determining the multi-scale parameters of the texture with the assistance of a geographic information system (GIS) and domain knowledge is proposed in this paper. First, the validity of domain knowledge from an existing GIS database is measured by spatial data mining algorithms, including spatial partitioning, image segmentation, and space-time system evaluation. Second, the general domain shape knowledge of each category is described by the GIS minimum enclosing rectangle indices and rectangular-degree indices. Then, the corresponding multi-scale texture windows can be quickly determined for each category by a correlation analysis with the shape indices. Finally, the Fisher function is used to evaluate the validity of the scale parameters. The experimental results show that the multi-scale value keeps a one-to-one relationship with the classified objects, and their value ranges are from a few to tens, instead of the smaller values of a traditional analysis; thus, effective texture features at such a scale can be built to identify categories in a geo-scene. In this way, the proposed method can increase the total number of categories for a certain geo-scene and reduce the classification uncertainty, as well as better meet the requirements of large-scale image geo-analysis. It also has as high a calculation efficiency and as good a performance as the traditional enumeration method.

Advanced Hepatocellular Carcinoma: Pretreatment Contrast-enhanced CT Texture Parameters as Predictive Biomarkers of Survival in Patients Treated with Sorafenib

Purpose To determine whether texture features on pretreatment contrast material-enhanced computed tomographic (CT) images can help predict overall survival (OS) and time to progression (TTP) in patients with advanced hepatocellular carcinoma (HCC) treated with sorafenib. Materials and Methods This retrospective study included 92 patients with advanced HCC treated with sorafenib between January 2009 and April 2015 at two independent university hospitals. Sixty-four of the 92 patients (70%) (six women, 58 men; median age, 66 years) were included from institution 1 and constituted a training cohort; 28 patients (30%) (five women, 23 men; median age, 64 years) were included from institution 2 and constituted a validation cohort. Pretreatment CT texture analysis was performed on late arterial and portal venous phase HCC images. Mean gray-level intensity, entropy, kurtosis, skewness, and standard deviation values were derived from the pixel distribution histogram before and after spatial filtration at different anatomic scales ranging from fine to coarse texture. Lesion heterogeneity was also visually graded on a 4-point scale. Correlations between visual analysis and texture parameters were assessed with the Spearman rank correlation. Univariate Kaplan-Meier and multivariate Cox proportional hazards regression analyses were performed in the training cohort to identify independent predictors of OS and TTP. Their predictive capacity was tested on the validation cohort by using Kaplan-Meier analysis. Results Visual analysis of tumor heterogeneity correlated with entropy at both arterial (P = .012) and portal venous (P = .038) phases. Portal phase-derived entropy at fine (hazard ratio [HR], 5.08; P = .0033), medium (HR, 2.23; P = .019), and coarse (HR, 2.26; P = .0032) texture scales was identified as an independent predictor of OS and confirmed in the validation cohort (P < .05). The difference in median survival between patients in the validation cohort with entropy values below and above the identified threshold was 272 days (with fine texture) and 741 days (with medium and coarse textures). Arterial phase-derived texture parameters (P > .085) and visual analysis (P > .11) were not associated with changes in survival. Conclusion Pretreatment portal venous phase-derived tumor entropy may be a predictor of survival in patients with advanced HCC treated with sorafenib.

In Vivo Imaging of Tau Pathology Using Magnetic Resonance Imaging Textural Analysis.

Background: Non-invasive characterization of the pathological features of Alzheimer's disease (AD) could enhance patient management and the development of therapeutic strategies. Magnetic resonance imaging texture analysis (MRTA) has been used previously to extract texture descriptors from structural clinical scans in AD to determine cerebral tissue heterogeneity. In this study, we examined the potential of MRTA to specifically identify tau pathology in an AD mouse model and compared the MRTA metrics to histological measures of tau burden.Methods: MRTA was applied to T2 weighted high-resolution MR images of nine 8.5-month-old rTg4510 tau pathology (TG) mice and 16 litter matched wild-type (WT) mice. MRTA comprised of the filtration-histogram technique, where the filtration step extracted and enhanced features of different sizes (fine, medium, and coarse texture scales), followed by quantification of texture using histogram analysis (mean gray level intensity, mean intensity, entropy, uniformity, skewness, standard-deviation, and kurtosis). MRTA was applied to manually segmented regions of interest (ROI) drawn within the cortex, hippocampus, and thalamus regions and the level of tau burden was assessed in equivalent regions using histology.Results: Texture parameters were markedly different between WT and TG in the cortex (E, p < 0.01, K, p < 0.01), the hippocampus (K, p < 0.05) and in the thalamus (K, p < 0.01). In addition, we observed significant correlations between histological measurements of tau burden and kurtosis in the cortex, hippocampus and thalamus.Conclusions: MRTA successfully differentiated WT and TG in brain regions with varying degrees of tau pathology (cortex, hippocampus, and thalamus) based on T2 weighted MR images. Furthermore, the kurtosis measurement correlated with histological measures of tau burden. This initial study indicates that MRTA may have a role in the early diagnosis of AD and the assessment of tau pathology using routinely acquired structural MR images.

Effect of WEDM surface texturing on Al2O3/TiCN composite ceramic tools in dry cutting of hardened steel

Micro scale textures were fabricated on the rake surface of Al2O3/TiCN composite ceramic cutting tools using WEDM process. The cutting performance of ceramic cutting tools with plain and textured rake surfaces was investigated in the dry cutting of hardened AISI 52100 steel (62 HRC). The performance of cutting tools was evaluated by chip-tool contact length, tool wear, machining forces, cutting temperature, frictional coefficient and chip morphology. Further, a 3D finite element model has been used to study the temperature and stress distribution in the cutting tools. Application of textures obstructing the chip flow direction resulted in uniform stress distribution, and higher groove sliding area ensured maximum reduction of machining forces, material adhesion, cutting and tool temperature. However, the textures parallel to the chip flow direction resulted in bending and curling of chips on the rake face causing an increase of chip-tool contact length and material adhesion as compared to the tools with textures obstructing the chip flow resulting in an increase of friction. The textured tool with textures inclined at an angle to the chip flow direction exhibited the best machining performance as compared to other textured cutting tools. Moreover, this tool exhibited superior anti-adhesion behavior as compared to the conventional cutting tool owing to the reduced chip-tool contact length and area, friction and chip flow angle.

Local structure of ordered and disordered states ofHe3-A in aerogel

Random textures of the orbital part of the order parameter of superfluid $^{3}\mathrm{He}$-A in aerogel are analyzed theoretically in the Ginzburg and Landau region both in the presence and in the absence of a global anisotropy. Correlation functions of angles, determining orientation of the order parameter, are found for relative distances which are small in comparison with the characteristic scale of the random texture. Modifications of the Larkin-Imry-Ma state in limiting cases of a relatively strong uniaxial compression and of a uniaxial stretching are analyzed, and characteristic parameters of the emerging states are found.

A Study on Piston Surface Defect by High Frequency Heat Treatment

This study is a study on the cracking of the main piston surface which is generated in the injection molding machine to generate mold force. The main piston machining process consists of high frequency heat treatment, grinding and super finishing after lathe turning. Scale, defect size, and fracture texture were observed for four cracks on the surface of the piston during the tempering process after high frequency heat treatment using a metallurgical microscope. In this study, it was confirmed that the cracked structure of the piston structure was ferrite and pearlite structure. It was confirmed that cracks progressed to 480 ㎛ and scale layer of 3 ㎛ or less. Surface hardening layer and hardness were min 2.0mm/HRc 58±2 spec 1.6 mm/HRc 56.5~57.5 In addition, cracks on the surface of the piston appear perpendicular to the rolling process. Therefore, it can be assumed that the crack occurred in the low temperature tempering process at 200°C or less after the high frequency heat treatment, not the material defect. Therefore, the temperature should be maintained at 200°C or higher during tempering after high-frequency heat treatment, and the cracking defect on the surface of the piston can be prevented by setting the feed rate to 1.3 mm/s or less during heat treatment.

Sensory Evaluation of Eleven Baked Russet-type Potato Varieties and Clones

Six hundred untrained panelists evaluated the baked sensory appeal of up to six varieties, three clones, and two Russet Norkotah (RN) strains. Panelists consumed small samples of plain baked potato and recorded their preference for aroma, flavor, texture, aftertaste and acceptance. After viewing photographs of two unidentified baked potatoes (RN and Classic Russet) sliced in half, they selected the photo that best described their visual preference of a baked potato; each potato was stored at 6.7 °C for 6 months prior to cooking. Eighty percent of panelists shown the photographs of the unidentified baked potatoes preferred the visual appearance of Classic R (white/cream - colored flesh) to that of RN (yellow/Gy - colored flesh). Mean scores for all culinary traits averaged > 5.0 on the 1-9 scale, which indicated that panelists generally liked the culinary attributes of all trialed varieties, however, panelists were able to detect differences among varieties. Flavor and texture were addressed in written comments from panelists more frequently than other attributes. On a texture scale of 1–7, with 1 = moist/dense and 7 = dry/crumbly, most panelists preferred baked potatoes with a texture of 3 (creamy/smooth).

Characterizing Grain-Oriented Silicon Steel Sheet Using Automated High-Resolution Laue X-ray Diffraction

Controlling texture in grain-oriented (GO) silicon steel sheet is critical for optimization of its magnetization performance. A new automated laboratory system, based on X-ray Laue diffraction, is introduced as a rapid method for large scale grain orientation mapping and texture measurement in these materials. Wide area grain orientation maps are demonstrated for both macroetched and coated GO steel sheets. The large secondary grains contain uniform lattice rotations, the origins of which are discussed.

A Preliminary Identification of Micro/Nano Scale Textures on Mineralization, Hydrocarbon Accumulation and Seismic Formation Structure

A Preliminary Identification of Micro/Nano Scale Textures on Mineralization, Hydrocarbon Accumulation and Seismic Formation Structure

Texture classification using partial differential equation approach and wavelet transform

Textures and patterns are the distinguishing characteristics of objects. Texture classification plays fundamental role in computer vision and image processing applications. In this paper, texture classification using PDE (partial differential equation) approach and wavelet transform is presented. The proposed method uses wavelet transform to obtain the directional information of the image. A PDE for anisotropic diffusion is employed to obtain texture component of the image. The feature set is obtained by computing different statistical features from the texture component. The linear discriminant analysis (LDA) enhances separability of texture feature classes. The features obtained from LDA are class representatives. The proposed approach is experimented on three gray scale texture datasets: VisTex, Kylberg, and Oulu. The classification accuracy of the proposed method is evaluated using k-NN classifier. The experimental results show the effectiveness of the proposed method as compared to the other methods in the literature.

Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer

Tumor phenotypes captured in computed tomography (CT) images can be described qualitatively and quantitatively using radiologist-defined “semantic” and computer-derived “radiomic” features, respectively. While both types of features have shown to be promising predictors of prognosis, the association between these groups of features remains unclear. We investigated the associations between semantic and radiomic features in CT images of 258 non-small cell lung adenocarcinomas. The tumor imaging phenotypes were described using 9 qualitative semantic features that were scored by radiologists, and 57 quantitative radiomic features that were automatically calculated using mathematical algorithms. Of the 9 semantic features, 3 were rated on a binary scale (cavitation, air bronchogram, and calcification) and 6 were rated on a categorical scale (texture, border definition, contour, lobulation, spiculation, and concavity). 32–41 radiomic features were associated with the binary semantic features (AUC = 0.56–0.76). The relationship between all radiomic features and the categorical semantic features ranged from weak to moderate (|Spearmen’s correlation| = 0.002–0.65). There are associations between semantic and radiomic features, however the associations were not strong despite being significant. Our results indicate that radiomic features may capture distinct tumor phenotypes that fail to be perceived by naked eye that semantic features do not describe and vice versa.