Gray-level Co-occurrence Matrix Contrast Research Articles

Radiomic Parameters in Periapical Lesions: A CBCT Analysis Evaluating Volumetric Size, Cortical Expansion, Erosion, and Shape.

To investigate significant differences in selected radiomic parameters when classifying periapical lesions based on volumetric size, cortical expansion, erosion, and shape using Cone Beam Computed Tomography (CBCT). A retrospective analytical and comparative study was conducted on 100 small field of view (FOV) 50×50 mm CBCT scans collected between the years 2018 and 2023. The study involved qualitative classification of periapical lesions, followed by segmentation and extraction of radiomic parameters. The extracted parameters included first-order features such as energy, entropy, total energy, and uniformity; texture features like grey-level co-occurrence matrix contrast (GLCMC) and neighbouring grey tone difference matrix contrast (NGTDMC); and shape features including elongation, flatness, sphericity, and mesh volume, utilising 3D Slicer and Pyradiomics. The normal distribution of the variables was determined using the Shapiro-Wilk test. Various tests were used to assess significant differences, including Student's t-test, Mann-Whitney U test, ANOVA, and Tukey's post hoc analysis. Significant differences were observed in the following parameters among the classification levels when classifying periapical lesions according to their volumetric size. There were significant differences in energy with a p-value of 0.001 and total energy with a p-value of 0.02. NGTDMC also showed a significant difference with a p-value of 0.001. A larger volumetric size is associated with greater energy and lower contrast. Significant differences in periapical lesions with erosion were found in shape sphericity (mean 0.34, SD 0.10, p=0.01), energy (mean 3.73×10¹⁰, SD 4.52×10¹⁰, p=0.002), and NGTDMC (mean 0.05, SD 0.02, p=0.001) compared to lesions without erosion. GLCMC was lower in erosive lesions (mean 18.94, SD 6.81, p=0.03) than in non-erosive ones (mean 22.28, SD 8.48). Regular-shaped periapical lesions demonstrated significantly greater elongation (mean 0.794, SD 0.115, p=0.006) and flatness (mean 0.614, SD 0.107, p=0.005) than irregular-shaped lesions. These findings suggest that regular-shaped periapical lesions are more elongated and flatter than irregular ones. No significant differences were found in radiomic features depending on the presence or absence of expansion in the periapical lesion. There are significant differences in texture and first-order radiomic features in periapical lesions classified based on size, erosion, and shape. This research's relevance lies in its potential to improve the quantitative characterisation of periapical lesions, leading to an objective interpretation. (EEJ-2023-11-159).

Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques

Apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) is an indispensable imaging technique in clinical neuroimaging that quantitatively assesses the diffusivity of water molecules within tissues using diffusion-weighted imaging (DWI). This study focuses on developing a robust machine learning (ML) model to predict the aggressiveness of gliomas according to World Health Organization (WHO) grading by analyzing patients’ demographics, higher-order moments, and grey level co-occurrence matrix (GLCM) texture features of ADC. A population of 722 labeled MRI-ADC brain image slices from 88 human subjects was selected, where gliomas are labeled as glioblastoma multiforme (WHO-IV), high-grade glioma (WHO-III), and low-grade glioma (WHO I-II). Images were acquired using 3T-MR systems and a region of interest (ROI) was delineated manually over tumor areas. Skewness, kurtosis, and statistical texture features of GLCM (mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence, and shade) were calculated using ADC values within ROI. The ANOVA f-test was utilized to select the best features to train an ML model. The data set was split into training (70%) and testing (30%) sets. The train set was fed into several ML algorithms and selected most promising ML algorithm using K-fold cross-validation. The hyper-parameters of the selected algorithm were optimized using random grid search technique. Finally, the performance of the developed model was assessed by calculating accuracy, precision, recall, and F1 values reported for the test set. According to the ANOVA f-test, three attributes; patient gender (1.48), GLCM energy (9.48), and correlation (13.86) that performed minimum scores were excluded from the dataset. Among the tested algorithms, the random forest classifier(0.8772 ± 0.0237) performed the highest mean-cross-validation score and selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14% over the test set. The study concludes that the developed ML model using the above features except for patient gender, GLCM energy, and correlation, has high prediction accuracy in glioma grading. Therefore, the outcomes of this study enable to development of advanced tumor classification applications that assist in the decision-making process in a real-time clinical environment.

Quantitative Assessment of Hyperpigmentation Changes in Human Skin after Microneedle Mesotherapy Using the Gray-Level Co-Occurrence Matrix (GLCM) Method.

The aim of the study was to quantitatively assess the effectiveness of microneedle mesotherapy in reducing skin discoloration. The results were analyzed using the gray-level co-occurrence matrix (GLCM) method. The skin of the forearm (7 × 7 cm) of 12 women aged 29 to 68 was examined. Microneedle mesotherapy was performed using a dermapen with a preparation containing 12% ascorbic acid. Each of the volunteers underwent a series of four microneedle mesotherapy treatments. The effectiveness of the treatment was quantified using the methods of image analysis and processing. A series of clinical images were taken in cross-polarized light before and after a series of cosmetic procedures. Then, the treated areas were analyzed by determining the parameters of the gray-level co-occurrence matrix (GLCM) algorithm: contrast and homogeneity. During image pre-processing, the volunteers' clinical images were separated into red (R), green (G) and blue (B) channels. The photos taken after the procedure show an increase in skin brightness compared to the photos taken before the procedure. The average increase in skin brightness after the treatment was 10.6%, the average decrease in GLCM contrast was 10.7%, and the average homogeneity increased by 14.5%. Based on the analysis, the greatest differences in the GLCM contrast were observed during tests performed in the B channel of the RGB scale. With a decrease in GLCM contrast, an increase in postoperative homogeneity of 0.1 was noted, which is 14.5%.

Image Processing Techniques based Feature Extraction for Insect Damage Areas

Monitoring of forests is important for the diagnosis of insect damage to vegetation. Detection and monitoring of damaged areas facilitates the control of pests for practitioners. For this purpose, Unmanned Aerial Vehicles (UAVs) have been recently used to detect damaged areas. In order to distinguish damage areas from healthy areas on UAV images, it is necessary to extract the feature parameters of the images. Therefore, feature extraction is an important step in Computer Aided Diagnosis of insect damage monitored with UAV images. By reducing the size of the UAV image data, it is possible to distinguish between damaged and healthy areas from the extracted features. The accuracy of the classification algorithm depends on the segmentation method and the extracted features. The Grey-Level Co-occurrence Matrix (GLCM) characterizes areas texture based on the number of pixel pairs with specific intensity values arranged in specific spatial relationships. In this paper, texture characteristics of insect damage areas were extracted from UAV images using with GLCM. The 3000*4000 resolution UAV images containing damaged and healthy larch trees were analyzed using Definiens Developer (e-Cognition software) for multiresolution segmentation to detect the damaged areas. In this analysis, scale parameters were applied as 500, shape 0.1, color 0.9 and compactness 0.5. As a result of segmentation, GLCM homogeneity, GLCM contrast and GLCM entropy texture parameters were calculated for each segment. When calculating the texturing parameters, neighborhoods in different angular directions (0,45,90,135) are taken into account. As a result of the calculations made by considering all directions, it was found that GLCM homogeneity values ranged between 0.08 - 0.2, GLCM contrast values ranged between 82.86 - 303.58 and GLCM entropy values ranged between 7.81 - 8.51. On the other hand, GLCM homogeneity for healthy areas varies between 0.05 - 0.08, GLCM contrast between 441.70 - 888.80 and GLCM entropy between 8.93 - 9.40. The study demonstrated that GLCM technique can be a reliable method to detection of insect damage areas from UAV imagery.

Early Detection of Wheat Yellow Rust Disease and Its Impact on Terminal Yield with Multi-Spectral UAV-Imagery

The food production system is vulnerable to diseases more than ever, and the threat is increasing in an era of climate change that creates more favorable conditions for emerging diseases. Fortunately, scientists and engineers are making great strides to introduce farming innovations to tackle the challenge. Unmanned aerial vehicle (UAV) remote sensing is among the innovations and thus is widely applied for crop health monitoring and phenotyping. This study demonstrated the versatility of aerial remote sensing in diagnosing yellow rust infection in spring wheats in a timely manner and determining an intervenable period to prevent yield loss. A small UAV equipped with an aerial multispectral sensor periodically flew over, and collected remotely sensed images of, an experimental field in Chacabuco (−34.64; −60.46), Argentina during the 2021 growing season. Post-collection images at the plot level were engaged in a thorough feature-engineering process by handcrafting disease-centric vegetation indices (VIs) from the spectral dimension, and grey-level co-occurrence matrix (GLCM) texture features from the spatial dimension. A machine learning pipeline entailing a support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) was constructed to identify locations of healthy, mild infection, and severe infection plots in the field. A custom 3-dimensional convolutional neural network (3D-CNN) relying on the feature learning mechanism was an alternative prediction method. The study found red-edge (690–740 nm) and near infrared (NIR) (740–1000 nm) as vital spectral bands for distinguishing healthy and severely infected wheats. The carotenoid reflectance index 2 (CRI2), soil-adjusted vegetation index 2 (SAVI2), and GLCM contrast texture at an optimal distance d = 5 and angular direction θ = 135° were the most correlated features. The 3D-CNN-based wheat disease monitoring performed at 60% detection accuracy as early as 40 days after sowing (DAS), when crops were tillering, increasing to 71% and 77% at the later booting and flowering stages (100–120 DAS), and reaching a peak accuracy of 79% for the spectral-spatio-temporal fused data model. The success of early disease diagnosis from low-cost multispectral UAVs not only shed new light on crop breeding and pathology but also aided crop growers by informing them of a prevention period that could potentially preserve 3–7% of the yield at the confidence level of 95%.

Can magnetic resonance imaging texture analysis change the breast imaging reporting and data system category of breast lesions?

PurposeThis study aimed to reveal magnetic resonance imaging (MRI) texture analysis (TA)'s contribution to categorizing breast lesions according to the Breast Imaging-Reporting and Data System (BI-RADS) lexicon. MethodTwo hundred and seventeen women with BI-RADS category 3, 4, and 5 lesions on breast MRI were included in the study. For TA, the region of interest was drawn manually to encompass the entire lesion on the fat-suppressed T2W and the first post-contrast T1W images. To identify the independent predictors of breast cancer, multivariate logistic regression analyses were performed using texture parameters. Estimated benign and malignant groups were formed according to the TA regression model. ResultsTexture parameters extracted from T2WI, including median, gray-level co-occurrence matrix (GLCM) contrast, GLCM correlation, GLCM joint entropy, GLCM sum entropy, and GLCM sum of squares, and parameters extracted from T1WI, including maximum, GLCM contrast, GLCM joint entropy, GLCM sum entropy, were independent predictors of breast cancer. In the estimated new groups according to the TA regression model, 19 (91%) of the benign 4a lesions were downgraded to BI-RADS category 3. ConclusionsThe addition of quantitative parameters obtained by MRI TA to BI-RADS criteria significantly increased the accuracy rate in differentiating benign and malignant breast lesions. When categorizing BI-RADS 4a lesions, the use of MRI TA in addition to conventional imaging findings may reduce unnecessary biopsy rates.

Measuring spatio-temporal heterogeneity and interior characteristics of green spaces in urban neighborhoods: A new approach using gray level co-occurrence matrix

Urban green space (UGS) is a complex and highly dynamic interface between people and nature. The existing methods of quantifying and evaluating UGS are mainly implemented on the surface features at a landscape scale, and most of them are insufficient to thoroughly reflect the spatial-temporal relationships, especially the internal characteristics changes at a small scale and the neighborhood spatial relationship of UGS. This paper thus proposes a method to evaluate the internal dynamics and neighborhood heterogeneity of different types of UGS in Leipzig using the gray level co-occurrence matrix (GLCM) index. We choose GLCM variance, contrast, and entropy to analyze five main types of UGS through a holistic description of their vegetation growth, spatial heterogeneity, and internal orderliness. The results show that different types of UGS have distinct characteristics due to the changes of surrounding buildings and the distance to the built-up area. Within a one-year period, seasonal changes in UGS far away from built-up areas are more obvious. As for the larger and dense urban forests, they have the lowest spatial heterogeneity and internal order. On the contrary, the garden areas present the highest heterogeneity. In this study, the GLCM index depicts the seasonal alternation of UGS on the temporal scale and shows the spatial form of each UGS, being in line with local urban planning contexts. The correlation analysis of indices also proves that each type of UGS has its distinct temporal and spatial characteristics. The GLCM is valid in assessing the internal characteristics and relationships of various UGS at the neighborhood scales, and using the methodology developed in our study, more studies and field experiments could be fulfilled to investigate the assessment accuracy of our GLCM index approach and to further enhance the scientific understanding on the internal features and ecological functions of UGS.

Quantitative Evaluation of the Effectiveness of Chemical Peelings in Reducing Acne Lesions Based on Gray-Level Co-Occurrence Matrix (GLCM).

PurposeAcne vulgaris is a chronic, inflammatory disease accompanied by lesions affecting the structure of the skin. Chemical peels are one of the methods of reducing acne vulgaris. There is still a lack of quantitative methods of assessing impact of cosmetic procedure on the skin. Skin condition depends on skin texture characterization; therefore, the analysis that provides data about the textures can be helpful in assessing the effectiveness of cosmetic treatments.Patients and MethodsThe study involved 24 volunteers with acne lesions. Each participant underwent 4 treatments using chemical peels at two-week intervals. Before, during and after procedure clinical photography were made. To assess effectiveness of chemical peeling in acne lesion reduction, we were used gray-level co-occurrence matrix (GLCM) analysis. Qualitative assessment of acne severity was made by 12 experts in dermatology.ResultsAfter a series of treatments, the GLCM contrast value decreased in each area of the face, and the GLCM homogeneity value increased, which means that the number of acne lesions was reduced. Expert assessment according to the IGA scale confirms the effectiveness of therapy with both salicylic and glycolic acid and pyruvic acid.ConclusionThe results of this study prove that GLCM analysis is a useful tool for assessing the effectiveness of chemical peel treatments. It can also be used for quantitative assessment of skin texture.

Evaluation of MRI-based radiomic features in heart morphologic variations as a consequence of autoimmune thyroid disorders.

Radiomics (RC) was initially developed using computed tomography (CT) for oncological imaging. However, it can be applied to various scientific and clinical radiology fields regardless of the modalities involved. The purpose of this survey was to evaluate alterations in magnetic resonance imaging of the heart (CMR) in patients suffering from autoimmune thyroid disorders (AITD) by applying RC tools and mapping features. A total of 50 individuals were evaluated in this study. We searched for CMR examinations performed in our department between January 2019 and 2021 in patients with AITD. Thirty patients with AITD (21 men and 9 women, aged 51 to 78 years; mean age, 60 years) were enrolled in our survey. We enrolled a control group (CG) of 20 individuals (14 men and 6 women aged 53–87 years; mean age, 68 years) without AITD or cardiac disorders. Global native T1 and T2 mapping revealed no significant differences between groups. However, we identified significantly higher values of several texture parameters, including the gray-level co-occurrence matrix (GLCM) entropy, gray-level run-length matrix (GLRLM; short-run high gray-level emphasis (SRHGE), GLCM (Energy), gray-level size zone matrix length matrix (GLZLM; LZLGE), GLZLM (SZLGE), DISCRETIZED (HISTO-Energy) GLCM (Dissimilarity), and GLCM (Contrast), in patients with AITD in comparison to the CG (P < .01). Our results indicate that several RC properties extracted from CMR images can be used to discriminate between the AITD and CG groups.

Texture analysis of the microstructure of internal curing concrete based on image recognition technology

Mechanical properties of internal curing concrete are greatly affected by its physical properties such as water content, cementing material content, porosity, and saturation. At the micro- level, such impact is finally reflected in the surface texture of its materials. In this study, the image recognition technology was used to find that the internal curing concrete samples have significant micromorphology and texture features. A texture parameter–strength model was established based on the relationship between Tamura texture parameters, gray level co-occurrence matrix (GLCM) texture parameters, and the mechanical strength. Due to the sensitivity of material properties and parameters, in terms of Tamura texture characteristics, coarseness, regularity and directionality are effective parameters for predicting the compressive strength of internally curing concrete. In terms of the GLCM texture, energy, correlation, entropy, and contrast are effective parameters to predict the compressive strength of the internal curing concrete. Correlations between each texture parameter and compressive strength follow different laws.

Quantitative Texture Analysis of Parotid Gland Ultrasound Images Yield Higher Correlation with Scintigraphy than Semiquantitative Scoring in Primary Sjögren's Syndrome Patients.

Ultrasound (US) can detect salivary gland abnormalities in primary Sjögren's syndrome (SS). This study aimed to compare the correlation among the semiquantitative US scores, texture features, and the quantitative salivary gland scintigraphy (SGS) results. This retrospective study included 11 patients who were diagnosed with primary SS and underwent US examinations of the parotid glands and SGS simultaneously. We evaluated SGS quantitatively based on the calculation of maximum accumulation ratio (MAR) and stimulated excretion fraction (EF). The US findings were accessed through the semiquantitative Outcome Measures in Rheumatology scoring system and by gray-level co-occurrence matrix (GLCM) texture analysis. Spearman's rank correlation tests were performed. A significant moderate negative correlation was noted between the semiquantitative US score and MAR (rho = -0.57, P = 0.006), but not with EF (rho = -0.11, P = 0.613). The GLCM texture metrics, including contrast, dissimilarity, and homogeneity, were all determined to be significantly associated with both MAR and EF. The GLCM contrast correlated moderately to MAR (rho = -0.66, P = 0.001). The GLCM homogeneity highly correlated to EF (rho = 0.74, P < 0.001). The contrast and homogeneity can still discriminate the changes in MAR and EF in the subgroups with the same semiquantitative US scores. US findings on parotid gland can correlate with SGS results when analyzed based on GLCM texture features. With the GLCM texture metrics, US appears to be an excellent imaging tool for the assessment of the parotid glands in primary SS patients.

Radiomic Analysis of Tumour Heterogeneity Using MRI in Head and Neck Cancer Following Chemoradiotherapy: A Feasibility Study.

ObjectivesTo evaluate interval changes in heterogeneity on diffusion-weighted apparent diffusion coefficient (ADC) maps and T1-weighted post-gadolinium (T1w post gad) MRI in head and neck carcinoma (HNSCC), with and without chemo-radiotherapy (CRT) response.MethodsThis prospective observational cohort study included 24 participants (20 men, age 62.9 ± 8.8 years) with stage III and IV HNSCC. The primary tumour (n = 23) and largest lymph node (n = 22) dimensions, histogram parameters and grey-level co-occurrence matrix (GLCM) parameters were measured on ADC maps and T1w post gad sequences, performed pretreatment and 6 and 12 weeks post CRT. The 2-year treatment response at primary and nodal sites was recorded. The Wilcoxon signed-rank test was used to compare interval changes in parameters after stratifying for treatment response and failure (p < 0.001 statistical significance).Results23/23 primary tumours and 18/22 nodes responded to CRT at 2 years. Responding HNSCC demonstrated a significant interval change in ADC histogram parameters (kurtosis, coefficient of variation, entropy, energy for primary tumour; kurtosis for nodes) and T1w post gad GLCM (entropy and contrast in the primary tumour and nodes) by 6 weeks post CRT (p < 0.001). Lymph nodes with treatment failure did not demonstrate an interval alteration in heterogeneity parameters.ConclusionsADC maps and T1w post gad MRI demonstrate the evolution of heterogeneity parameters in successfully treated HNSCC by 6 weeks post CRT; however, this is not observed in lymph nodes failing treatment.Advances in KnowledgeEarly reduction in heterogeneity is demonstrated on MRI when HNSCC responds to CRT.

Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach.

Gray-level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computational method for the assessment of textural patterns, applicable in almost any area of microscopy. The aim of our research was to perform the GLCM analysis of cell nuclei in Saccharomyces cerevisiae yeast cells after the induction of sublethal cell damage with ethyl alcohol, and to evaluate the performance of various machine learning (ML) models regarding their ability to separate damaged from intact cells. For each cell nucleus, five GLCM parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, and textural variance. Based on the obtained GLCM data, we applied three ML approaches: neural network, random trees, and binomial logistic regression. Statistically significant differences in GLCM features were observed between treated and untreated cells. The multilayer perceptron neural network had the highest classification accuracy. The model also showed a relatively high level of sensitivity and specificity, as well as an excellent discriminatory power in the separation of treated from untreated cells. To the best of our knowledge, this is the first study to demonstrate that it is possible to create a relatively sensitive GLCM-based ML model for the detection of alcohol-induced damage in Saccharomyces cerevisiae cell nuclei.

The 3-Dimensional-Computed Tomography Texture Is Useful to Predict Pancreatic Neuroendocrine Tumor Grading.

The aim of this study is to evaluate the computed tomography texture parameters in predicting grading. This study analyzed 68 nonfunctioning pancreatic neuroendocrine neoplasms (Pan-NENs). Clinical and radiological parameters were studied. Four model models were built, including clinical and standard radiologic parameters (model 1), first- and second-order computed tomography features (models 2 and 3), all parameters (model 4). The diagnostic accuracy was reported as area under the curve. A score was computed using the best model and validated to predict progression-free survival. The size of tumors and heterogeneous enhancement were related to the risk of "non-G1" Pan-NENs (coefficients 0.471, P = 0.012, and 1.508, P = 0.027). Four second-order parameters were significantly related to the presence of "non-G1" Pan-NENs: the gray level co-occurrence matrix correlation (6.771; P = 0.011), gray level co-occurrence matrix contrast variance (0.349; P = 0.009), the neighborhood gray-level different matrix contrast (-63.129; P = 0.001), and the gray-level zone length matrix with the low gray-level zone emphasis (-0.151; P = 0.049). Model 4 was the best, with a higher area under the curve (0.912; P = 0.005). The score obtained predicted the progression-free survival. Computed tomography radiomics signature can be useful in preoperative workup.

Primary clinical study of radiomics for diagnosing simple bone cyst of the jaw.

To screen the radiomic features of simple bone cysts of the jaws and explore the potential application of radiomics in pre-operative diagnosis of jaw simple bone cysts. The investigators designed and implemented a case-control study. 19 patients with simple bone cysts who were admitted to the Department of Maxillofacial Surgery, Sun Yat-sen University Affiliated Stomatology Hospital from 2013 to 2019 were included in this study. Their clinical data and cone-beam computed tomography (CBCT) images were examined. The control group consisted of patients with odontogenic keratocyst. CBCT imaging features were analyzed and compared between the patient and control groups. Overall, 10,323 image features were extracted through feature analysis. A subset of 25 radiomic features obtained after feature selection were analyzed further. These 25 features were significantly different between the 2 groups (p < 0.05). The absolute value of correlation coefficient was 0.487-0.775. Gray-level co-occurrence matrix (GLCM) contrast, neighborhood gray tone difference matrix (NGTDM) contrast, and GLCM variance were the features with the highest correlation coefficients. Pre-operative radiomics analysis showed the differences between simple bone cysts and odontogenic keratocysts, can help to diagnose simple bone cysts. Three specific texture features-GLCM contrast, NGTDM contrast, and GLCM variance-may be the characteristic imaging features of simple bone cysts of the jaw.

Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification

Robust and reliable classification of sea ice types in synthetic aperture radar (SAR) images is needed for various operational and environmental applications. Previous studies have investigated the class-dependent decrease in SAR backscatter intensity with incident angle (IA); others have shown the potential of textural information to improve automated image classification. In this work, we investigate the inclusion of Sentinel-1 (S1) texture features into a Bayesian classifier that accounts for linear per-class variation of its features with IA. We use the S1 extra-wide swath (EW) product in ground-range detected format at medium resolution (GRDM), and we compute seven grey level co-occurrence matrix (GLCM) texture features from the HH and the HV backscatter intensity in the linear and logarithmic domain. While GLCM texture features obtained in the linear domain vary significantly with IA, the features computed from the logarithmic intensity do not depend on IA or reveal only a weak, approximately linear dependency. They can therefore be directly included in the IA-sensitive classifier that assumes a linear variation. The different number of looks in the first sub-swath (EW1) of the product causes a distinct offset in texture at the sub-swath boundary between EW1 and the second sub-swath (EW2). This offset must be considered when using texture in classification; we demonstrate a manual correction for the example of GLCM contrast. Based on the Jeffries–Matusita distance between class histograms, we perform a separability analysis for 57 different GLCM parameter settings. We select a suitable combination of features for the ice classes in our data set and classify several test images using a combination of intensity and texture features. We compare the results to a classifier using only intensity. Particular improvements are achieved for the generalized separation of ice and water, as well as the classification of young ice and multi-year ice.

Fundus image quality assessment for an eye model with cataract using adapted illumination patterns

PurposeIn recent years, different methods have been developed to improve fundus image quality in eyes with cataract. For stray light reduction, we have introduced a segmented LED light source. To analyze the effect of different illumination patterns at different cataract characteristics, we developed an optical cataract eye model. Accordingly, we performed fundus imaging using a fundus camera equipped with a LED light source controlled by a Raspberry Pi.MethodsA customized 24‐fold segmented LED light source was connected to a fundus camera. The eye model consists of a plano‐convex lens, a cover glass, and a test chart with five black‐white test fields. The pupil diameter was set to 7 mm. To test the influence of variable illumination patterns, four different cortical cataract patterns were produced by sand blasting the cover glasses. Three spoke‐like (1Spoke, 4S, 7S) patterns and one crescent‐shaped/CS pattern were realized. To analyze the effect of adapted illumination conditions, the gray‐level co‐occurrence matrix (GLCM) contrasts were calculated. At that, one image was taken at standard annular illumination (all LEDs on) as baseline followed by seven defined adapted configurations, for each type of cataract.ResultsFor the four types of cataracts, the baseline GLCM contrast values were 0.21 (1S), 0.18 (4S), 0.17 (7S), and 0.22 (CS). In each cataract type, the best‐fitting illumination pattern resulted in 0.23 (1S), 0.29 (4S), 0.22 (7S), and 0.23 (CS). Compared to the baseline values, the increases of the GLCM contrasts were 9.5% (1S), 61% (4S), 29% (7S), and 4.5% (CS).ConclusionsWe successfully performed structured LED illumination for different cortical cataract types using an optical eye model. An improvement of fundus image quality could be determined in all investigated cases using the gray‐level co‐occurrence matrix contrast.

Investigating the potential of Sentinel-1 to detect varying spatial heterogeneity in pasture cover in grasslands

ABSTRACT Selective grazing by livestock may be indicative of a site’s grass species diversity and depending on the grazing intensity; this may or may not promote further diversity. However, the detection of sites with spatial heterogeneity in pasture cover as a manifestation of selective grazing has not yet been investigated using satellite remote sensing. Thus, this study was conducted to address the question; can Sentinel-1 detect spatial heterogeneity induced by livestock grazing in grassy fields? Since Synthetic Aperture Radar (SAR) imaging is noted to be sensitive to vegetation architectural arrangement, this study used Sentinel-1 C-band SAR to detect spatial heterogeneity created by selective livestock grazing. The study examined a range of semivariogram, grey-level co-occurrence matrix (GLCM), and eigenvector-eigenvalue polarimetric decomposition features. The coefficient of variation estimates of the GLCM contrast feature consistently produced the strongest correlation (R 2 = 0.71) with Lloyd’s Patchiness Index and semivariogram sill while the polarimetric scattering entropy (range estimates) produced a significant linear correlation with semivariogram sill (R 2 = 0.55, p < 0.05). Inferably, the GLCM contrast and polarimetric scattering entropy can predict spatial heterogeneity in a grazing environment. This is the first time polarimetric scattering entropy estimated from Sentinel-1 has been used for the detection of spatial heterogeneity in a grazing landscape, which makes this study different from past similar studies. Nonetheless, we recommend the testing of this parameter (polarimetric scattering entropy) with a multitemporal SAR data and encourage future studies to investigate the potential of Sentinel-1 for the detection of spatial distances between grass clumps.

Prediction of liver regeneration in recipients after living-donor liver transplantation in using preoperative CT texture analysis and clinical features.

The aim of the study is to predict the rate of liver regeneration in recipients after living-donor liver transplantation using preoperative CT texture and shape analysis of the future graft. 102 donor-recipient pairs who underwent living-donor liver transplantation using right lobe grafts were retrospectively included. We semi-automatically segmented the future graft from preoperative CT. The volume of the future graft (LVpre) was measured, and texture and shape analyses were performed. The graft liver was segmented from postoperative follow-up CT and the volume of the graft (LVpost) was measured. The regeneration index was defined by the following equation: [(LVpost-LVpre)/LVpre] × 100(%). We performed a stepwise, multivariate linear regression analysis to investigate the association between clinical, texture and shape parameters and the RI and to make the best-fit predictive model. The mean regeneration index was 47.5 ± 38.6%. In univariate analysis, the volume of the future graft, energy, effective diameter, surface area, sphericity, roundnessm, compactness1, and grey-level co-occurrence matrix contrast as well as several clinical parameters were significantly associated with the regeneration index (p < 0.05). The best-fit predictive model for the regeneration index made by multivariate analysis was as follows: Regeneration index (%) = 127.020-0.367 × effective diameter - 1.827 × roundnessm + 47.371 × recipient body surface area (m2) + 12.041 × log(recipient white blood cell count) (× 103/μL)+ 18.034 (if the donor was female). The effective diameter and roundnessm of the future graft were associated with liver regeneration. Preoperative CT texture analysis of future grafts can be useful for predicting liver regeneration in recipients after living-donor liver transplantation.

Association Between the Size and 3D CT-Based Radiomic Features of Breast Cancer Hepatic Metastasis

To evaluate the effect of the anatomic size on 3D radiomic imaging features of the breast cancer hepatic metastases. CT scans of 81 liver metastases from 54 patients with breast cancer were evaluated. Ten most common 3D radiomic features from the histogram and gray level co-occurrence matrix (GLCM) categories were calculated for the hepatic metastases (HM) and compared to normal liver (NL). The effect of size was evaluated by using linear mixed-effects regression models. The effect of size on different radiomic features was analyzed for both liver lesions and background liver. Three-dimensional radiomic features from GLCM demonstrate an important size dependence. The texture-feature size dependence was found to be different among feature categories and between the HM and NL, thus demonstrating a discriminatory power for the tissue type. Significant difference in the slope was found for GLCM homogeneity (NL slope = 0.004, slope difference 95% confidence interval [CI] 0.06-0.1, p <0.001), contrast (NL slope = 45, slope difference 95% CI 205-305, p <0.001), correlation (NL slope = 0.04, slope difference 95% CI 0.11-0.21, p <0.001), and dissimilarity (NL slope = 0.7, slope difference 95% CI 3.6-5.4, p <0.001). The GLCM energy (NL slope = 0.002, slope difference 95% CI -0.0005 to -0.0003, p <0.007), and entropy (NL slope = 1.49, slope difference 95% CI 0.07-0.52, p <0.009) exhibited size-dependence for both NL and HM, although demonstrating a difference in the slope between themselves. Radiomic features of breast cancer hepatic metastasis exhibited significant correlation with tumor size. This finding demonstrates the complex behavior of imaging features and the need to include feature-specific properties into radiomic models.