Gray-level Co-occurrence Matrix Homogeneity Research Articles

Radiomics in stereotactic body radiotherapy for non-small cell lung cancer: a systematic review and radiomic quality score study.

Stereotactic body radiotherapy (SBRT) has been widely utilized for curative treatment of early-stage non-small cell lung cancer (NSCLC). It has achieved good local control rate comparable to surgery. Currently, no standard risk model exists for SBRT outcome or complication prediction. Radiomics has the potential to improve clinical outcome prognostication. Here, we reviewed the current literature on the radiomic analyses of thoracic SBRT through the use of radiomic quality score (RQS). Literature search was conducted on PubMed and Embase to retrieve radiomics studies on SBRT for early NSCLC. The literature search included studies up to June 2021. Only full papers published in peer reviewed journals were included. Studies that included metastatic lung cancers or non-lung cancers were excluded. Two independent investigators evaluated each study using the RQS and resolved discrepancies through discussion. A total number of 25 studies were analysed. The mean RQS was 7.76 of a maximum score of 36. This corresponds to 21.56% of the maximum score. Lack of feature reduction strategies, external validation and open data sharing were identified as key limitations of the reviewed studies. Meanwhile, various common radiomic signatures across different studies such as gray level co-occurrence matrix Homogeneity and energy have been identified. Multiple robust radiomic models have also been reviewed that may improve outcome or complication prediction. Radiomics in thoracic SBRT has a very promising future as a prognostication tool. However, larger multicenter prospective studies are required to confirm radiomic signatures. Improvement in future study methodologies can also facilitate its wider application.

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.

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.

Predicting the Recurrence of Gastric Cancer Using the Textural Features of Perigastric Adipose Tissue on [18F]FDG PET/CT.

This study aimed to assess the relationship between the histopathological and textural features of perigastric adipose tissue (AT) on 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) and to evaluate the prognostic significance of perigastric AT textural features in predicting recurrence-free survival (RFS) in patients with gastric cancer. Sixty-nine patients with gastric cancer who underwent staging [18F]FDG PET/CT and subsequent curative surgery were retrospectively reviewed. Textural features of perigastric AT were extracted from PET images. On histopathological analysis, CD4, CD8, and CD163 cell infiltration and matrix metalloproteinase-11 and interleukin-6 (IL-6) expression in perigastric AT were graded. The degree of CD163 cell infiltration in perigastric AT was significantly correlated with the mean standardized uptake value (SUV), SUV histogram entropy, grey-level co-occurrence matrix (GLCM) energy, and GLCM entropy of perigastric AT. The degree of IL-6 expression in the perigastric AT was significantly correlated with the mean and median SUVs of perigastric AT. In multivariate survival analysis, GLCM entropy, GLCM dissimilarity, and GLCM homogeneity of perigastric AT were significant predictors of RFS. The textural features of perigastric AT on [18F]FDG PET/CT significantly correlated with inflammatory response in perigastric AT and were significant prognostic factors for predicting RFS in patients with gastric cancer.

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.

Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques

BackgroundDiffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors.MethodsThis prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed.ResultsAccording to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process.ConclusionsThis study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies.

Comparison of Foaming Properties Between the Shirasu Porous Glass Membrane Device and Tessari’s Three-way Stopcock Technique for Polidocanol and Ethanolamine Oleate Foam Production: A Benchtop Study

PurposeTo compare the characteristics of polidocanol (POL) and ethanolamine oleate (EO) sclerosing foams produced by a Shirasu porous glass membrane (SPGM) device with those made using a 3-way stopcock (3WSC). Materials and MethodsFoam half-life times were measured in an ex-vivo benchtop study. Computed tomography (CT) images of each foam were obtained over the time course, and a CT texture analysis was conducted. The bubble size in each foam was measured by an optical microscope. ResultsMedian foam half-life times were longer in the SPGM group than in the 3WSC group (POL: 198 vs 166 s, P = .02; EO: 640 vs 391 s, P < .01). In the CT texture analysis, median standard deviation (SD) and entropy (randomness) were lower, and median energy (uniformity) and gray-level cooccurrence matrix (GLCM) homogeneity were higher in the SPGM group than in the 3WSC group (POL SD: at 30 s and 50–300 s; POL entropy: at 0–60 s; EO SD: at 0–600 s; EO entropy: at 0–460 s; POL energy: at 0–40 s; POL GLCM homogeneity: at 0–250 s; EO energy: at 0–360 s; EO GLCM homogeneity: at 0–480 s; all P < .05). Median bubble diameters in the SPGM group and in the 3WSC group were 69 and 83 μm (P < .01), respectively, in the POL foam; and 36 and 36 μm (P = .45), respectively, in the EO foam. ConclusionsPOL and EO foams had greater uniformity and longer foam half-life time when prepared with an SPGM device than with a 3WSC.

Usefulness of texture features of apparent diffusion coefficient maps in predicting chemoradiotherapy response in muscle-invasive bladder cancer.

To examine the usefulness of the texture analysis (TA) of apparent diffusion coefficient (ADC) maps in predicting the chemoradiotherapy (CRT) response of muscle-invasive bladder cancer (MIBC). We reviewed 45 MIBC patients who underwent cystectomy after CRT. CRT response was assessed through histologic evaluation of cystectomy specimens. Two radiologists determined the volume of interest for the index lesions on ADC maps of pretherapeutic 1.5-T MRI and performed TA using the LIFEx software. Forty-six texture features (TFs) were selected based on their contribution to the prediction of CRT sensitivity. To evaluate diagnostic performance, diagnostic models from the selected TFs were created using random forest (RF) and support vector machine (SVM), respectively. Twenty-three patients achieved pathologic complete response (pCR) to CRT. The feature selection identified first quartile ADC (Q1 ADC), gray-level co-occurrence matrix (GLCM) correlation, and GLCM homogeneity as important in predicting CRT response. Patients who achieved pCR showed significantly lower Q1 ADC and GLCM correlation values (0.66 × 10-3mm2/s and 0.53, respectively) than those who did not (0.81 × 10-3mm2/s and 0.70, respectively; p < 0.05 for both). The AUCs of the RF and SVM models incorporating the selected TFs were 0.82 (95% confidence interval [CI]: 0.67-0.97) and 0.96 (95% CI: 0.91-1.00), respectively, and the AUC of the SVM model was better than that of the mean ADC value (0.76, 95% CI: 0.61-0.90; p = 0.0037). TFs can serve as imaging biomarkers in MIBC patients for predicting CRT sensitivity. TAs of ADC maps can potentially optimize patient selection for CRT. • Texture analysis of ADC maps and feature selection identified important texture features for classifying pathologic tumor response in patients with muscle-invasive bladder cancer. • The machine learning model incorporating the texture features set, which included first quartile ADC, GLCM correlation, and GLCM homogeneity, showed high performance in predicting chemoradiotherapy response. • Texture features could serve as imaging biomarkers that optimize eligible patient selection for chemoradiotherapy in muscle-invasive bladder cancer.

Road Extraction from Cartosat-2F Multispectral Data with Object-Oriented Analysis

For detection of a road network, high-resolution satellite data have been used following the object-oriented classification approach. We used object-based feature extraction algorithms for detection of road networks from a high resolution Cartosat-2F multispectral data in an Indian city with varying terrain conditions ranging from a compact built-up area to a predominantly vegetated area. The approach involves multi-resolution segmentation (MRS ) and spectral difference segmentation (SDS ) followed by road extraction using fuzzy rule-based algorithm based on various object features, viz. gray-level co-occurrence matrix homogeneity, density, rectangular fit, etc. With overall accuracies ranging from 77.46% to 92% SDS approach performed better than MRS which could afford 60.46% to 75.0% only. However, both of these approaches score over the classical Gaussian maximum likelihood classifier which could register only 50.0% to 68.0% overall accuracy. Furthermore, the maximum overall accuracy was obtained in compact built-up site (85% to 92%) followed by sparsely built-up site (75.0% to 88%) and predominantly vegetated site (60.46% to 77.14%).

Feasibility of prognosis assessment for cancer of unknown primary origin using texture analysis of 18F-fluorodeoxyglucose PET/computed tomography images of largest metastatic lymph node.

Cancers of unknown primary origin cannot be staged using images, making the prognosis difficult. We attempted to predict prognosis of patients with unknown primary origin using tumour heterogeneity recently introduced in F-fluorodeoxyglucose (F-FDG) PET/computed tomography (CT). Overall, 30 patients with unknown primary origin who underwent whole-body F-FDG PET/CT scans were retrospectively enrolled for texture analysis. The volume of interest was placed in the largest metastatic lymph nodes and conventional parameters and grey-level co-occurrence matrix (GLCM) were calculated. Statistical analysis of image-based variables was performed using Cox regression analyses. Patients were stratified into two groups based on cutoff values of GLCMentropy obtained using receiver operating characteristics (ROCs). Patients were analyzed, and overall survival (OS) was compared using Kaplan-Meier analysis. Univariate Cox regression analysis showed significant differences in prognosis for parenchymal organ metastasis (P < 0.01), GLCM homogeneity (P = 0.01), GLCMcontrast (P < 0.01), GLCMentropy (P < 0.01) and GLCMdissimilarity (P < 0.01). Multivariate Cox regression analysis showed a significant difference in reduced prognosis for GLCMentropy positive (P < 0.01). Stratification was performed based on the GLCMentropy cutoff value, determined using ROCs analysis, with smaller groups showing better OS. Despite previous difficulties in predicting prognosis in patients with unknown primary origin, F-FDG PET/CT texture features may enable stratification of prognosis. This could be useful for appropriate patient selection and management and help identify a subset of patients with favourable outcomes. These novel findings may be helpful for prognostication and improving patient care.

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.

Radiomics of cholangiocarcinoma on pretreatment CT can identify patients who would best respond to radioembolisation.

Results after trans-arterial radioembolisation (TARE) for intrahepatic cholangiocarcinoma (iCC) depend on the architecture of the tumour. This latter can be quantified through computed tomography (CT) texture analysis. The aims of the present study were to analyse relationships between CT textural features prior to TARE and objective response (OR), progression-free survival (PFS), and overall survival (OS). Texture analysis was retrospectively applied to 55 pre-TARE CT scans of iCCs, focusing attention on the histogram-based features and the grey-level co-occurrence matrix (GLCM). Texture features were harmonised using the ComBat procedure. Objective response was assessed using the Response Evaluation Criteria In Solid Tumours 1.1. The least absolute shrinkage and selection operator (LASSO) method was applied to select the most useful textural features related to OR. Of the 55 patients, 53 had post-TARE imaging available, showing OR in 56.6% of cases. Texture analysis showed that iCCs showing OR after TARE had a higher uptake of iodine contrast in the arterial phase (higher mean histogram values, p< 0.001) and more homogeneous distribution (lower kurtosis, p= 0.043; GLCM contrast, p= 0.004; GLCM dissimilarity, p= 0.005, and higher GLCM homogeneity, p= 0.005; and GLCM correlation p= 0.030) at the pre-TARE CT scan. A favourable radiomic signature was calculated and observed in 15 of the 55 patients. The median PFS of these 15 patients was 12.1months and that of the remaining 40 patients was 5.1months (p= 0.008). Texture analysis of pre-TARE CT scans can quantify vascularisation and homogeneity of iCC architecture, providing clinical information useful in identifying ideal TARE candidates. • Hypervascular tumours with a more homogeneous uptake of iodine contrast in the arterial phase were those most likely to be effectively treated by TARE. • The arterial phase was observed to be the best acquisition phase for providing information regarding the "sensitivity" of the tumour to TARE. • Patients with favourable radiomic signature showed a median progression-free survival of 12.1months versus 5.1months of patients with an unfavourable signature (p = 0.008).

Texture Analysis of T1-Weighted Contrast-Enhanced Magnetic Resonance Imaging Potentially Predicts Outcomes of Patients with Non–Wingless-Type/Non–Sonic Hedgehog Medulloblastoma

To investigate whether tumor texture features derived from preoperative T1-weighted magnetic resonance imaging (MRI) are associated with overall survival (OS) of patients with non-wingless-type (WNT)/non-sonic hedgehog (SHH) medulloblastoma. We retrospectively reviewed 38 patients with non-WNT/non-SHH (encompassing group 3 and group 4) medulloblastoma treated with surgery in our institution from 2013 to 2016. All patients were followed-up for at least 2 years or until death. Primary tumor traditional parameters were evaluated, and texture features were extracted from preoperative T1-weighted MRI, including 4 features from the histogram matrix and 6 textures from the gray-level co-occurrence matrix (GLCM). Texture features were dichotomized into 2 subgroups based on their optimal cutoff values obtained from receiver operating characteristics curve analysis. Two-year OS was compared between the dichotomized subgroups using the Kaplan-Meier analysis and log-rank test. Multivariate Cox regression analysis was performed to determine independent prognostic factors. The therapy regimen was the only basic characteristic significantly related to 2-year OS (P=0.015). Two features of the GLCM were shown to be significantly associated with 24-month OS. Multivariate Cox regression analysis revealed that GLCM homogeneity (adjusted hazard ratio, 0.145; P= 0.013) was an independent prognostic predictor for patients. Texture analysis on T1-weighted contrast-enhanced MRI potentially serves as a prognostic predictor of survival for patients with non-WNT/non-SHH medulloblastoma.

Value renal CT volumetric texture analysis with machine learning radiomics in assessment of pathological grade of clear cell renal cell carcinoma

Objective To investigate the value of renal CT volumetric texture analysis with machine learning radiomics in assessment of pathological grade of clear cell renal cell carcinoma (ccRCC). Methods Thirty-four biopsy-confirmed ccRCC subjects who had four-phase CT scanning (NC:non-contrast, CM: Corticomedullary, N: Nephrographic, E: Excretory) were collected retrospectively from June 2013 to October 2017 for the study. Non-rigid registration was performed on multi-phase CT images in reference to CM-phase. Each lesion was segmented on CM-phase CT images using our in-house volumetric image analysis platform, 3DQI . A set of fifty-nine volumetric textures, including histogram, gradient, gray level co-occurrence matrix (GLCM), run-length (RL), moments, and shape, was calculated for each segment lesion in each phase as parameters for the training/testing of Random Forest (RF) classifier. Four groups according to pathological Fuhrman grade on a scale Ⅰ to Ⅳ, these tumors were then divided into low (Ⅰ+Ⅱ) and high grade (Ⅲ+ Ⅳ) groups. Feature selection was performed by Boruta algorithm. A 10-fold cross-validation method was applied to validate the RF performance by receiver operating characteristic (ROC) curves analysis to determine the diagnostic accuracy of the model. Results Subjects were divided into four groups by Fuhrman grade on a scale Ⅰ to Ⅳ: 3 cases grade Ⅰ, 19 cases grade Ⅱ, 8 cases grade Ⅲ and 4 cases grade Ⅳ. In CM-phase, kurtosis and long-run-emphasis (RLE) were selected the most important textures for ccRCC staging among 59 features. The area under curve (AUC) of ROC was 0.88 (79% sensitivity and 82% specificity) by using kurtosis and RLE textures. The mean values of kurtosis and RLE were (-20.00±22.00)×10-2 and (3.00±0.40) ×10-2 for low group, whereas (31.00±32.00) ×10-2 and (5.00±0.02) ×10-2 for high group. Within the mean±SD range of statistics, radiomics can distinguish between low and high grade tumors.In multi-phase analysis, three most important features were selected among 236 (59×4) textures: kurtosis (CM-phase), GLCM homogeneity I (HOMO 1) (E-phase), and GLCM homogeneity 2 (HOMO2) (E-phase). The mean values of HOMO 1 (E-phase) and HOMO 2 (E-phase) were (19.00±0.03) ×10-2 and (11.00±0.02)×10-2 for low group, whereas (22.00±0.03) ×10-2 and (14.00±0.02) ×10-2 for high group.The AUC was 0.92 (93% sensitivity and 87% specificity) by using these three textures. Conclusion This study has demonstrated that renal CT volumetric texture analysis with machine learning radiomics could preoperative accurately perform cancer staging for ccRCC. Key words: Kidney neoplasms; Radiomics; Cear cell renal cell carcinoma; Pathological grade; Texture analysis

Signature Analysis of Building Damage With TerraSAR-X New Staring SpotLight Mode Data

In 2013, the TerraSAR-X (TSX) mission was extended by implementing two new modes, including Staring SpotLight (ST). The azimuth resolution of this mode is significantly increased to approximately 0.24 m by widening the azimuth beam steering angle range. In this letter, five types of damaged buildings due to earthquake are analyzed using TSX images in the new ST mode. In particular, the characteristics of the individual damaged buildings are investigated using the new very high-resolution images. The old Beichuan County is selected as the research area, where most of the buildings damaged by the earthquake on May 12, 2008 have been preserved. Moreover, buildings with different types of damage can be found in the study area. Features such as backscattering and texture measurements of the damaged buildings in descending and ascending post-earthquake HH and VV polarization synthetic aperture radar (SAR) images are analyzed. Visual interpretation, statistical comparison, and classification experiments are performed for the damaged building analysis. Based on the analysis, the TSX ST mode images show potential for building damage detection and for discrimination of basic damage classes. In addition, the classification results show that the gray-level co-occurrence matrix (GLCM) second moment, the variance of backscattering, and the GLCM homogeneity are the three best features for the discrimination of damage type.

Automatic Buildings Extraction From LiDAR Data in Urban Area by Neural Oscillator Network of Visual Cortex

Researchers have extensively applied Locally Excitatory Globally Inhibitory Oscillator Networks (LEGION) for segmentation. These networks are neural oscillator networks based on biological frameworks, in which each oscillator has excitatory lateral connections to the oscillators in its local neighbourhood, as well as a connection to a global inhibitor. In this paper, we develop a modified LEGION segmentation to extract buildings from high-quality digital surface models (DSMs). The extraction is implemented without assumptions on the underlying structures in the DSM data and without prior knowledge of the number of regions. For complex information hidden in the generated DSM of an urban area, grey level co-occurrence matrix homogeneity is used to measure DSM height texture. We then use this homogeneity to distinguish buildings from trees and identify major oscillator blocks in target buildings, instead of using lateral potential. To segment pixels into different groups, we calculate the weight of the global inhibitor (Wz) from DSM complexity. Building boundaries are traced and regularised after extraction from the segmented DSM. A least squares solution with perpendicular constraints for determining regularised rectilinear building boundaries is proposed, and arc line fitting is performed. This paper presents the concept, algorithms, and procedures of the proposed approach. Experimental results on the Vaihingen region studied in the ISPRS test project are also discussed.

Gray Level Co-Occurrence Matrix Texture Analysis of Germinal Center Light Zone Lymphocyte Nuclei: Physiology Viewpoint with Focus on Apoptosis

In our study we investigated the relationship between conventional morphometric indicators of nuclear size and shape (area and circularity) and the parameters of gray level co-occurrence matrix texture analysis (entropy, homogeneity, and angular second moment) in cells committed to apoptosis. A total of 432 lymphocyte nuclei images from the spleen germinal center light zones (cells in early stages of apoptosis) were obtained from eight healthy male guinea pigs previously immunized with sheep red blood cells (antigen). For each nucleus, area, circularity, entropy, homogeneity, and angular second moment were determined. All measured parameters of gray level co-occurrence matrix (GLCM) were significantly correlated with morphometric indicators of nuclear size and shape. The strongest correlation was observed between GLCM homogeneity and nuclear area (p < 0.0001, r(s) = 0.61). Angular second moment values were also highly significantly correlated with nuclear area (r(s)= 0.39, p < 0.0001). These results indicate that the GLCM method may be a powerful tool in evaluation of ultrastructural nuclear changes during early stages of the apoptotic process.

Analysis of wavelet packet and statistical textures for object-oriented classification of forest-agriculture ecotones using SPOT 5 imagery

Textural features of high-resolution remote sensing imagery are a powerful data source for improving classification accuracy because using only spectral information is not sufficient for the classification of objects with within-field spectral variability. This study presents the methods of using an object-oriented texture analysis algorithm for improving high-resolution remote sensing imagery classification, including wavelet packet transform texture analysis, the grey-level co-occurrence matrix (GLCM) and local spatial statistics. Wavelet packet transform texture analysis, with the method of optimization and selection of wavelet texture for feature extraction, is a good candidate for object-oriented classification. Feature optimization is used to reduce the data dimensions in combinations of textural sub-bands and spectral bands. The result of the classification accuracy assessment indicates the improvement of texture analysis for object-oriented classification in this study. Compared with the traditional method that uses only spectral bands, the combination of GLCM homogeneity and spectral bands increases the overall accuracy from 0.7431 to 0.9192. Furthermore, wavelet packet transform texture analysis is the optimal method, increasing the overall accuracy to 0.9216 using a smaller data dimension. Local spatial statistical measures also increase the classification total accuracy, but only from 0.7431 to 0.8088. This study demonstrates that wavelet packet and statistical textures can be used to improve object-oriented classification; specifically, the texture analysis based on the multiscale wavelet packet transform is optimal for increasing the classification accuracy using a smaller data dimension.

Estimation of Tree Size Diversity Using Object Oriented Texture Analysis and Aster Imagery.

This study investigates the potential of object-based texture parameters extracted from 15m spatial resolution ASTER imagery for estimating tree size diversity in a Mediterranean forested landscape in Turkey. Tree size diversity based on tree basal area was determined using the Shannon index and Gini Coefficient at the sampling plot level. Image texture parameters were calculated based on the grey level co-occurrence matrix (GLCM) for various image segmentation levels. Analyses of relationships between tree size diversity and texture parameters found that relationships between the Gini Coefficient and the GLCM values were the most statistically significant, with the highest correlation (r=0.69) being with GLCM Homogeneity values. In contrast, Shannon Index values were weakly correlated with image derived texture parameters. The results suggest that 15m resolution Aster imagery has considerable potential in estimating tree size diversity based on the Gini Coefficient for heterogeneous Mediterranean forests.