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Purpose: The noninvasive monitoring of mild hyperthermia or thermal ablation is important to guarantee therapeutic safety and efficacy. The potential of ultrasound B-mode image texture features in monitoring temperature or coagulation zones studied in this article. Materials and methods: The experiments carried out on eighteen in vitro porcine liver samples heated from 20°C to 60°C in the water bath. The ultrasound radiofrequency signal at different temperature collected to reconstruct B-mode ultrasound images. The texture features based on gray level histogram (GLH), gray level co-occurrence matrix (GLCM), and gray level-gradient co-occurrence matrix (GGCM) extracted, respectively. Accordingly, we analyze the correlations between these texture features and temperature based on the experiment results. Results: The results showed that five texture feature parameters closely related to temperature, including mean gray scale of GLH, homogeneity of GLCM, hybrid entropy, inverse difference moment, and correlation of GGCM. Some of these feature parameters have correlation coefficients larger than 0.9 within the temperature range of 20°C to 60°C. Conclusions: The above-mentioned five feature parameters expected to apply for noninvasive monitoring of MH or TA. [ABSTRACT FROM AUTHOR]

Reports have suggested that tumor textures presented on T2-weighted images correlate with the genetic status of glioma. Therefore, development of an image analyzing framework that is capable of objective and high throughput image texture analysis for large scale image data collection is needed. The current study aimed to address the development of such a framework by introducing two novel parameters for image textures on T2-weighted images, i.e., Shannon entropy and Prewitt filtering. Twenty-two WHO grade 2 and 28 grade 3 glioma patients were collected whose pre-surgical MRI and IDH1 mutation status were available. Heterogeneous lesions showed statistically higher Shannon entropy than homogenous lesions (p = 0.006) and ROC curve analysis proved that Shannon entropy on T2WI was a reliable indicator for discrimination of homogenous and heterogeneous lesions (p = 0.015, AUC = 0.73). Lesions with well-defined borders exhibited statistically higher Edge mean and Edge median values using Prewitt filtering than those with vague lesion borders (p = 0.0003 and p = 0.0005 respectively). ROC curve analysis also proved that both Edge mean and median values were promising indicators for discrimination of lesions with vague and well defined borders and both Edge mean and median values performed in a comparable manner (p = 0.0002, AUC = 0.81 and p < 0.0001, AUC = 0.83, respectively). Finally, IDH1 wild type gliomas showed statistically lower Shannon entropy on T2WI than IDH1 mutated gliomas (p = 0.007) but no difference was observed between IDH1 wild type and mutated gliomas in Edge median values using Prewitt filtering. The current study introduced two image metrics that reflect lesion texture described on T2WI. These two metrics were validated by readings of a neuro-radiologist who was blinded to the results. This observation will facilitate further use of this technique in future large scale image analysis of glioma. [ABSTRACT FROM AUTHOR]

For decades, ecologists have measured habitat attributes in the field to understand and predict patterns of animal distribution and abundance. However, the scale of inference possible from field measured data is typically limited because large-scale data collection is rarely feasible. This is problematic given that conservation and management typical require data that are fine grained yet broad in extent. Recent advances in remote sensing methodology offer alternative tools for efficiently characterizing wildlife habitat across broad areas. We explored the use of remotely sensed image texture, which is a surrogate for vegetation structure, calculated from both an air photo and from a Landsat TM satellite image, compared with field-measured vegetation structure, characterized by foliage-height diversity and horizontal vegetation structure, to predict avian density and species richness within grassland, savanna, and woodland habitats at Fort McCoy Military Installation, Wisconsin, USA. Image texture calculated from the air photo best predicted density of a grassland associated species, grasshopper sparrow (Ammodramus savannarum), within grassland habitat (R2 = 0.52, p-value <0.001), and avian species richness among habitats (R2 = 0.54, p-value <0.001). Density of field sparrow (Spizella pusilla), a savanna associated species, was not particularly well captured by either field-measured or remotely sensed vegetation structure variables, but was best predicted by air photo image texture (R2 = 0.13, p-value = 0.002). Density of ovenbird (Seiurus aurocapillus), a woodland associated species, was best predicted by pixel-level satellite data (mean NDVI, R2 = 0.54, p-value <0.001). Surprisingly and interestingly, remotely sensed vegetation structure measures (i.e., image texture) were often better predictors of avian density and species richness than field-measured vegetation structure, and thus show promise as a valuable tool for mapping habitat quality and characterizing biodiversity across broad areas. [ABSTRACT FROM AUTHOR]

Background: Non-invasive characterization of a tumor's molecular features could enhance treatment management. Quantitative computed tomography (CT) based texture analysis (QTA) has been used to derive tumor heterogeneity information, and the appearance of the tumors has been shown to relate to patient outcome in non-small cell lung cancer (NSCLC) and other cancers. In this study, we examined the potential of tumoral QTA to differentiate K-ras mutant from pan-wildtype tumors and its prognostic potential using baseline pre-treatment non-contrast CT imaging in NSCLC. Methods: Tumor DNA from patients with early-stage NSCLC was analyzed on the LungCarta Panel. Cases with a K-ras mutation or pan-wildtype for 26 oncogenes and tumor suppressor genes were selected for QTA. QTA was applied to regions of interest in the primary tumor. Non-parametric Mann Whitney test assessed the ability of the QTA, clinical and patient characteristics to differentiate between K-ras mutation from pan-wildtype. A recursive decision tree was developed to determine whether the differentiation of K-ras mutant from pan-wildtype tumors could be improved by sequential application of QTA parameters. Kaplan-Meier survival analysis assessed the ability of these markers to predict survival. Results: QTA was applied to 48 cases identified, 27 had a K-ras mutation and 21 cases were pan-wildtype. Positive skewness and lower kurtosis were significantly associated with the presence of a K-ras mutation. A five node decision tree had sensitivity, specificity, and accuracy values (95% CI) of 96.3% (78.1–100), 81.0% (50.5–97.4), and 89.6% (72.9–97.0); respectively. Kurtosis was a significant predictor of OS and DFS, with a lower kurtosis value linked with poorer survival. Conclusions: Lower kurtosis and positive skewness are significantly associated with K-ras mutations. A QTA feature such as kurtosis is prognostic for OS and DFS. Non-invasive QTA can differentiate the presence of K-ras mutation from pan-wildtype NSCLC and is associated with patient survival. [ABSTRACT FROM AUTHOR]

Inspired by theories of higher local order autocorrelation (HLAC), this paper presents a simple, novel, yet very powerful approach for wood recognition. The method is suitable for wood database applications, which are of great importance in wood related industries and administrations. At the feature extraction stage, a set of features is extracted from Mask Matching Image (MMI). The MMI features preserve the mask matching information gathered from the HLAC methods. The texture information in the image can then be accurately extracted from the statistical and geometrical features. In particular, richer information and enhanced discriminative power is achieved through the length histogram, a new histogram that embodies the width and height histograms. The performance of the proposed approach is compared to the state-of-the-art HLAC approaches using the wood stereogram dataset ZAFU WS 24. By conducting extensive experiments on ZAFU WS 24, we show that our approach significantly improves the classification accuracy. [ABSTRACT FROM AUTHOR]

Purpose: Image texture is increasingly used to discriminate tissues and lesions in PET/CT. For quantification or in computer-aided diagnosis, textural feature analysis must produce robust and comparable values. Because statistical feature values depend on image count statistics, we investigated in depth the stability of Haralick features values as functions of acquisition duration, and for common image resolutions and reconstructions. Methods: A homogeneous cylindrical phantom containing 9.6 kBq/ml Ge-68 was repeatedly imaged on a Siemens Biograph mCT, with acquisition durations ranging from three seconds to three hours. Images with 1.5, 2, and 4 mm isometrically spaced voxels were reconstructed with filtered back-projection (FBP), ordered subset expectation maximization (OSEM), and the Siemens TrueX algorithm. We analysed Haralick features derived from differently quantized (3 to 8-bit) grey level co-occurrence matrices (GLCMs) as functions of exposure E, which we defined as the product of activity concentration in a volume of interest (VOI) and acquisition duration. The VOI was a 50 mm wide cube at the centre of the phantom. Feature stability was defined for df/dE → 0. Results: The most stable feature values occurred in low resolution FBPs, whereas some feature values from 1.5 mm TrueX reconstructions ranged over two orders of magnitude. Within the same reconstructions, most feature value-exposure curves reached stable plateaus at similar exposures, regardless of GLCM quantization. With 8-bit GLCM, median time to stability was 16 s and 22 s for FBPs, 18 s and 125 s for OSEM, and 23 s, 45 s, and 76 s for PSF reconstructions, with longer durations for higher resolutions. Stable exposures coincided in OSEM and TrueX reconstructions with image noise distributions converging to a Gaussian. In FBP, the occurrence of stable values coincided the disappearance of negatives image values in the VOI. Conclusions: Haralick feature values depend strongly on exposure, but invariance exists within defined domains of exposure. Here, we present an easily replicable procedure to identify said stable exposure domains, where image noise does not substantially add to textural feature values. Only by imaging at predetermined feature-invariant exposure levels and by adjusting exposure to expected activity concentrations, can textural features have a quantitative use in PET/CT. The necessary exposure levels are attainable by modern PET/CT systems in clinical routine. [ABSTRACT FROM AUTHOR]

Background: Image texture analysis is a noninvasive technique for quantifying intratumoral heterogeneity, with derived texture features reported to be closely related to the treatment outcome of tumors. Gastric cancer is one of the most common tumors and the third leading cause of cancer-related deaths worldwide. Although trastuzumab is associated with a survival gain among patients with human epidermal growth factor receptor 2 (HER2)-positive advanced gastric cancer, optimal patient selection is challenging. The purpose of this study was to determine whether CT texture features of HER2-positive gastric cancer were related to the survival rate after trastuzumab treatment. Methods and Findings: Patients diagnosed with HER2-positive advanced gastric cancer from February 2007 to August 2014 were retrospectively selected. Using in-house built software, histogram features (kurtosis and skewness) and gray-level co-occurrence matrices (GLCM) features (angular second moment [ASM], contrast, entropy, variance, and correlation) were derived from the CT images of HER2-positive advanced gastric cancer in 26 patients. All the patients were followed up for more than 6 months, with no confirmed deaths. The patients were dichotomized into a good and poor survival group based on cutoff points of overall survival of 12 months. A receiver-operating characteristics (ROC) analysis was performed to test the ability of each texture parameter to identify the good survival group. Kaplan–Meier curves for patients above and below each threshold were constructed. Using a threshold of >265.8480 for contrast, >488.3150 for variance, and ≤0.1319×10−3. for correlation, all of the area under the ROC curves showed fair accuracy (>0.7). Kaplan–Meier analysis showed statistically significant survival difference between two groups according to optimal cutoff values of contrast, variance, correlation and ASM. However, as this study had a small number of patients, a further study with a larger population will be needed to validate the results. Conclusions: Heterogeneous texture features on CT images were associated with better survival in patients with HER2-positive advanced gastric cancer who received trastuzumab-based treatment. Therefore, texture analysis shows potential to be a clinically useful imaging biomarker providing additional prognostic information for patient selection. [ABSTRACT FROM AUTHOR]

The human heart has a heterogeneous structure, which is characterized by different cell types and their spatial configurations. The physical structure, especially the fibre orientation and the interstitial fibrosis, determines the electrical excitation and in further consequence the contractility in macroscopic as well as in microscopic areas. Modern image processing methods and parameters could be used to describe the image content and image texture. In most cases the description of the texture is not satisfying because the fibre orientation, detected with common algorithms, is biased by elements such as fibrocytes or endothelial nuclei. The goal of this work is to figure out if cardiac tissue can be analysed and classified on a microscopic level by automated image processing methods with a focus on an accurate detection of the fibre orientation. Quantitative parameters for identification of textures of different complexity or pathological attributes inside the heart were determined. The focus was set on the detection of the fibre orientation, which was calculated on the basis of the cardiomyocytes’ nuclei. It turned out that the orientation of these nuclei corresponded with a high precision to the fibre orientation in the image plane. Additionally, these nuclei also indicated very well the inclination of the fibre. [ABSTRACT FROM AUTHOR]