Gray-level Co-occurrence Matrix Variance Research Articles

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.

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.

Application of textural analysis to map the sea ice concentration with sentinel 1A in the western region of the Antarctic Peninsula

This article proposes the mapping of the concentration of sea ice in the oceanic region of the western Antarctic Peninsula using backscatter coefficients and textural features from synthetic aperture radar (SAR) C-band, horizontal single polarization (HH) images obtained by the Sentinel 1 A satellite sensor. Pure samples of open water and sea ice were obtained from SAR images with the aid of PlanetScope optical images during the austral winter. The statistical technique of logistic regression was applied to identify the best textural features to discriminate the two sampled targets. These selected textural features were used with the backscatter coefficients of SAR images in a maximum likelihood supervised classifier to analyze the images. With sea ice classified in the time series between 2016 and 2019 during winter and austral spring, the concentration of sea ice in the study region was calculated and mapped. The textural features statistically indicated by the logistic regression were gray level co-occurrence matrix (GLCM) Mean and GLCM Variance, resulting in the supervised classification having an accuracy of 86% and precision ranging from 86% to 98% in the validation stage.

Tree Species Classification in Mixed Deciduous Forests Using Very High Spatial Resolution Satellite Imagery and Machine Learning Methods

Spatially explicit information on tree species composition is important for both the forest management and conservation sectors. In combination with machine learning algorithms, very high-resolution satellite imagery may provide an effective solution to reduce the need for labor-intensive and time-consuming field-based surveys. In this study, we evaluated the possibility of using multispectral WorldView-3 (WV-3) satellite imagery for the classification of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) in a lowland, mixed deciduous forest in central Croatia. The pixel-based supervised classification was performed using two machine learning algorithms: random forest (RF) and support vector machine (SVM). Additionally, the contribution of gray level cooccurrence matrix (GLCM) texture features from WV-3 imagery in tree species classification was evaluated. Principal component analysis confirmed GLCM variance to be the most significant texture feature. Of the 373 visually interpreted reference polygons, 237 were used as training polygons and 136 were used as validation polygons. The validation results show relatively high overall accuracy (85%) for tree species classification based solely on WV-3 spectral characteristics and the RF classification approach. As expected, an improvement in classification accuracy was achieved by a combination of spectral and textural features. With the additional use of GLCM variance, the overall accuracy improved by 10% and 7% for RF and SVM classification approaches, respectively.

MRI Radiomics for Prediction of Tumor Response and Downstaging in Rectal Cancer Patients after Preoperative Chemoradiation

PurposeThis study aimed to investigate radiomic features extracted from magnetic resonance imaging (MRI) scans performed before and after neoadjuvant chemoradiotherapy (nCRT) in predicting response of locally advanced rectal cancer (LARC). Methods and MaterialsThirty-nine patients who underwent nCRT for LARC were included, with 294 radiomic features extracted from MRI that was performed before (pre-CRT) and 6 to 8 weeks after completing nCRT (post-CRT). Based on tumor regression grade (TRG), 26 patients were classified as having a histopathologic good response (GR; TRG 0-1) and 13 as non-GR (TRG 2-3). Tumor downstaging (T-downstaging) occurred in 25 patients. Univariate analyses were performed to assess potential radiomic and delta-radiomic predictors for TRG in pathologic complete response (pCR) versus non-pCR, GR versus non-GR, and T-downstaging. The support vector machine-based multivariate model was used to select the best predictors for TRG and T-downstaging. ResultsWe identified 13 predictive features for pCR versus non-pCR, 14 for GR versus non-GR, and 16 for T-downstaging. Pre-CRT gray-level run length matrix nonuniformity, pre-CRT neighborhood intensity difference matrix (NIDM) texture strength, and post-CRT NIDM busyness predicted all 3 treatment responses. The best predictor for GR versus non-GR was pre-CRT global minimum combined with clinical N stage in the multivariate analysis. The best predictor for T-downstaging was the combination of pre-CRT gray-level co-occurrence matrix correlation, NIDM-texture strength, and gray-level co-occurrence matrix variance. The pre-CRT, post-CRT, and delta radiomic-based models had no significant difference in predicting all 3 responses. ConclusionsPre-CRT MRI, post-CRT MRI, and delta radiomic-based models have the potential to predict tumor response after nCRT in LARC. These data, if validated in larger cohorts, can provide important predictive information to aid in clinical decision making.

Gray-level co-occurrence matrix analysis of chromatin architecture in periportal and perivenous hepatocytes.

Periportal hepatocytes (PPHs) and perivenous hepatocytes (PVHs) in standard optical microscopy appear to be morphologically identical. However, the functional properties of these two cell populations and their roles in liver lobules are not the same. Despite significant differences in gene expression between these two hepatocyte populations, it is still unclear whether the differences are present at the higher levels of chromatin organization. In this study, we present results, indicating that periportal and perivenous hepatocytes, when stained using toluidine blue histological dye, have different chromatin textural patterns quantified with gray-level co-occurrence matrix (GLCM) method. Hepatic tissue was obtained from ten male, healthy mice. Chromatin structures were analyzed using GLCM. For each structure, we measured the values of angular second moment, inverse difference moment, GLCM Contrast, GLCM Variance, and GLCM Sum Variance. The results indicate that there is a statistically significant difference in all GLCM mathematical parameters except the contrast. In addition, some chromatin GLCM features were in correlation with serum aminotransferase levels in perivenous, but not in periportal hepatocytes. To the best of our knowledge, this is the first study to test the nuclear morphological differences between hepatocytes using GLCM and to investigate the respective relation with serum liver enzymes.

MODELLING ABOVE GROUND BIOMASS OF MANGROVE FOREST USING SENTINEL-1 IMAGERY

Abstract. Many studies have been conducted in the estimation of forest above ground biomass (AGB) using features from synthetic aperture radar (SAR). Specifically, L-band ALOS/PALSAR (wavelength ~23 cm) data is often used. However, few studies have been made on the use of shorter wavelengths (e.g., C-band, 3.75 cm to 7.5 cm) for forest mapping especially in tropical forests since higher attenuation is observed for volumetric objects where energy propagated is absorbed. This study aims to model AGB estimates of mangrove forest using information derived from Sentinel-1 C-band SAR data. Combinations of polarisations (VV, VH), its derivatives, grey level co-occurrence matrix (GLCM), and its principal components were used as features for modelling AGB. Five models were tested with varying combinations of features; a) sigma nought polarisations and its derivatives; b) GLCM textures; c) the first five principal components; d) combination of models a−c; and e) the identified important features by Random Forest variable importance algorithm. Random Forest was used as regressor to compute for the AGB estimates to avoid over fitting caused by the introduction of too many features in the model. Model e obtained the highest r2 of 0.79 and an RMSE of 0.44 Mg using only four features, namely, σ°VH GLCM variance, σ°VH GLCM contrast, PC1, and PC2. This study shows that Sentinel-1 C-band SAR data could be used to produce acceptable AGB estimates in mangrove forest to compensate for the unavailability of longer wavelength SAR.

Effective water surface mapping in macrophyte-covered reservoirs in NE Brazil based on TerraSAR-X time series

Water supplies in northeastern Brazil strongly depend on the numerous surface water reservoirs of various sizes there. However, the seasonal and long-term water surface dynamics of these reservoirs, particularly the large number of small ones, remain inadequately known. Remote sensing techniques have shown great potentials in water bodies mapping. Yet, the widespread presence of macrophytes in most of the reservoirs often impedes the delineation of the effective water surfaces. Knowledge of the dynamics of the effective water surfaces in the reservoirs is essential for understanding, managing, and modelling the local and regional water resources.In this study, a two-year time series of TerraSAR-X (TSX) satellite data was used to monitor the effective water surface areas in nine reservoirs in NE Brazil. Calm open water surfaces were obtained by segmenting the backscattering coefficients of TSX images with minimum error thresholding. Linear unmixing was implemented on the distributions of gray-level co-occurrence matrix (GLCM) variance in the reservoirs to quantify the proportions of sub-populations dominated by different types of scattering along the TSX time series. By referring to the statistics and the seasonal proportions of the GLCM variance sub-populations the GLCM variance was segmented to map the vegetated water surfaces. The effective water surface areas that include the vegetation-covered waters as well as calm open water in the reservoirs were mapped with accuracies >77%. The temporal and spatial change patterns of water surfaces in the nine reservoirs over a period of two consecutive dry and wet seasons were derived. Precipitation-related soil moisture changes, topography and the dense macrophyte canopies are the main sources of errors in the such-derived effective water surfaces. Independent from in-situ data, the approach employed in this study shows great potential in monitoring water surfaces of different complexity and macrophyte coverage. The effective water surface areas obtained for the reservoirs can provide valuable input for efficient water management and improve the hydrological modelling in this region.

Gray level co-occurrence matrix algorithm as pattern recognition biosensor for oxidopamine-induced changes in lymphocyte chromatin architecture

We demonstrate that a proapoptotic chemical agent, oxidopamine, induces dose dependent changes in chromatin textural patterns which can be quantified using the Gray level co-occurrence matrix (GLCM) method. Peripheral blood (heparin-pretreated) samples were treated with oxidopamine (6-OHDA, 6-hydroxydopamine) to achieve effective concentrations of 100, 200 and 300µM. The samples were smeared on microscope slides and fixated in methanol. The smears were stained using a modification of Feulgen method for DNA visualization. For each stained smear, a sample of 30 lymphocyte chromatin structures were visualized and analyzed. This way, textural parameters for a total of 120 nuclei micrographs were calculated. For each chromatin structure, five different GLCM features were calculated: angular second moment, GLCM entropy, inverse difference moment, GLCM correlation, and GLCM variance. Oxidopamine induced the rise of the values of GLCM entropy and variance, and the reduction of angular second moment, correlation, and inverse difference moment. The trends for GLCM parameter changes were found to be highly significant (p<0.001). These results indicate that GLCM mathematical algorithm might be successfully used in detection and evaluation of discrete early apoptotic structural changes in Feulgen-stained chromatin of peripheral blood lymphocytes that are not detectable using conventional microscopy/cell biology techniques.

Extraction of Slum Areas From VHR Imagery Using GLCM Variance

Many cities in the global South are facing the emergence and growth of highly dynamic slum areas, but often lack detailed information on these developments. Available statistical data are commonly aggregated to large, heterogeneous administrative units that are geographically meaningless for informing effective pro-poor policies. General base information neither allows spatially disaggregated analysis of deprived areas nor monitoring of rapidly changing settlement dynamics, which characterize slums. This paper explores the utility of the gray-level co-occurrence matrix (GLCM) variance to distinguish between slums and formal built-up (formal) areas in very high spatial and spectral resolution satellite imagery such as WorldView-2, OrbView, Quickbird, and Resourcesat. Three geographically different cities are selected for this investigation: Mumbai and Ahmedabad, India and Kigali, Rwanda. The exploration of the utility and transferability of the GLCM shows that the variance of the GLCM combined with the normalized difference vegetation index (NDVI) is able to separate slums and formal areas. The overall accuracy achieved is 84% in Kigali, 87% in Mumbai, and 88% in Ahmedabad. Furthermore, combining spectral information with the GLCM variance within a random forest classifier results in a pixel-based classification accuracy of 90%. The final slum map, aggregated to homogenous urban patches (HUPs), shows an accuracy of 88%–95% for slum locations depending on the scale parameter.

Discriminatory ability of fractal and grey level co-occurrence matrix methods in structural analysis of hippocampus layers.

Fractal and grey level co-occurrence matrix (GLCM) analysis represent two mathematical computer-assisted algorithms that are today thought to be able to accurately detect and quantify changes in tissue architecture during various physiological and pathological processes. However, despite their numerous applications in histology and pathology, their sensitivity, specificity and validity regarding evaluation of brain tissue remain unclear. In this article we present the results indicating that certain parameters of fractal and GLCM analysis have high discriminatory ability in distinguishing two morphologically similar regions of rat hippocampus: stratum lacunosum-moleculare and stratum radiatum. Fractal and GLCM algorithms were performed on a total of 240 thionine-stained hippocampus micrographs of 12 male Wistar albino rats. 120 digital micrographs represented stratum lacunosum-moleculare, and another 120 stratum radiatum. For each image, 7 parameters were calculated: fractal dimension, lacunarity, GLCM angular second moment, GLCM contrast, inverse difference moment, GLCM correlation, and GLCM variance. GLCM variance (VAR) resulted in the largest area under the Receiver operating characteristic (ROC) curve of 0.96, demonstrating an outstanding discriminatory power in analysis of stratum lacunosum-moleculare (average VAR equaled 478.1 ± 179.8) and stratum radiatum (average VAR of 145.9 ± 59.2, p < 0.0001). For the criterion VAR ≤ 227.5, sensitivity and specificity were 90% and 86.7%, respectively. GLCM correlation as a parameter also produced large area under the ROC curve of 0.95. Our results are in accordance with the findings of our previous study regarding brain white mass fractal and textural analysis. GLCM algorithm as an image analysis method has potentially high applicability in structural analysis of brain tissue cytoarcitecture.

Texture analysis of cartilage T2 maps: individuals with risk factors for OA have higher and more heterogeneous knee cartilage MR T2 compared to normal controls - data from the osteoarthritis initiative

IntroductionThe goals of this study were (i) to compare the prevalence of focal knee abnormalities, the mean cartilage T2 relaxation time, and the spatial distribution of cartilage magnetic resonance (MR) T2 relaxation times between subjects with and without risk factors for Osteoarthritis (OA), (ii) to determine the relationship between MR cartilage T2 parameters, age and cartilage morphology as determined with whole-organ magnetic resonance imaging scores (WORMS) and (iii) to assess the reproducibility of WORMS scoring and T2 relaxation time measurements including the mean and grey level co-occurrence matrix (GLCM) texture parameters.MethodsSubjects with risk factors for OA (n = 92) and healthy controls (n = 53) were randomly selected from the Osteoarthritis Initiative (OAI) incidence and control cohorts, respectively. The specific inclusion criteria for this study were (1) age range 45-55 years, (2) body mass index (BMI) of 19-27 kg/m2, (3) Western Ontario and McMaster University (WOMAC) pain score of zero and (4) Kellgren Lawrence (KL) score of zero at baseline. 3.0 Tesla MR images of the right knee were analyzed using morphological gradings of cartilage, bone marrow and menisci (WORMS) as well as compartment specific cartilage T2 mean and heterogeneity. Regression models adjusted for age, gender, and BMI were used to determine the difference in cartilage parameters between groups.ResultsWhile there was no significant difference in the prevalence of knee abnormalities (cartilage lesions, bone marrow lesions, meniscus lesions) between controls and subjects at risk for OA, T2 parameters (mean T2, GLCM contrast, and GLCM variance) were significantly elevated in those at risk for OA. Additionally, a positive significant association between cartilage WORMS score and cartilage T2 parameters was evident.ConclusionsOverall, this study demonstrated that subjects at risk for OA have both higher and more heterogeneous cartilage T2 values than controls, and that T2 parameters are associated with morphologic degeneration.