Automatic area estimation of algal blooms in water bodies from UAV images using texture analysis

Algal blooms,the spread of algae on the surface of water bodies,have adverse effects not only on aquatic ecosystems but also on human life.The adverse effects of harmful algal blooms(HABs)necessitate a convenient solution for detection and monitoring.Unmanned aerial vehicles(UAVs)have recently emerged as a tool for algal bloom detection,efficiently providing on-demand images at high spatiotemporal resolutions.This study developed an image processing method for algal bloom area estimation from the aerial images(obtained from the internet)captured using UAVs.As a remote sensing method of HAB detection,analysis,and monitoring,a combination of histogram and texture analyses was used to efficiently estimate the area of HABs.Statistical features like entropy(using the Kullback-Leibler method)were emphasized with the aid of a gray-level co-occurrence matrix.The results showed that the orthogonal images demonstrated fewer errors,and the morphological filter best detected algal blooms in real time,with a precision of 80%.This study provided efficient image processing approaches using on-board UAVs for HAB monitoring.

The Clinical Value of Ultrasound Image Texture Analysis in the Diagnosis of Uterine Adhesions

Purpose: This review examines the diagnostic value of transvaginal 3D ultrasound image texture analysis for the diagnosis of uterine adhesions. Materials and Methods: The total clinical data of 53 patients with uterine adhesions diagnosed by hysteroscopy and the imaging data of transvaginal three-dimensional ultrasound from the Second Affiliated Hospital of Chongqing Medical University from June 2022 to August 2023 were retrospectively analysed. Based on hysteroscopic surgical records, patients were divided into two independent groups: normal endometrium and uterine adhesion sites. The samples were divided into a training set and a test set, and the transvaginal 3D ultrasound was used to outline the region of interest (ROI) and extract texture features for normal endometrium and uterine adhesions based on hysteroscopic surgical recordings, the training set data were feature screened and modelled using lasso regression and cross-validation, and the diagnostic efficacy of the model was assessed by applying the subjects’ operating characteristic (ROC) curves. Results: For each group, 290 texture feature parameters were extracted and three higher values were screened out, and the area under the curve of the constructed ultrasonographic scoring model was 0.658 and 0.720 in the training and test sets, respectively. Conclusion Relative clinical value of transvaginal three-dimensional ultrasound image texture analysis for the diagnosis of uterine adhesions.

Real-time ore sorting using color and texture analysis

Sensor-based ore sorting is a technology used to classify high-grade mineralized rocks from low-grade waste rocks to reduce operation costs.Many ore-sorting algorithms using color images have been proposed in the past,but only some validate their results using mineral grades or optimize the algorithms to classify rocks in real-time.This paper presents an ore-sorting algorithm based on image processing and machine learning that is able to classify rocks from a gold and silver mine based on their grade.The algorithm is composed of four main stages:(1)image segmentation and partition,(2)color and texture feature extraction,(3)sub-image classification using neural networks,and(4)a voting system to determine the overall class of the rock.The algorithm was trained using images of rocks that a geologist manually classified according to their mineral content and then was validated using a different set of rocks analyzed in a laboratory to determine their gold and silver grades.The proposed method achieved a Matthews correlation coefficient of 0.961 points,higher than other classification algorithms based on support vector machines and convolutional neural networks,and a processing time under 44 ms,promising for real-time ore sorting applications.

Diagnostic performance of texture analysis in the differential diagnosis of perianal fistulising Crohn’s disease and glandular anal fistula

BACKGROUND Perianal fistulising Crohn's disease(PFCD)and glandular anal fistula have many similarities on conventional magnetic resonance imaging.However,many patients with PFCD show concomitant active proctitis,but only few patients with glandular anal fistula have active proctitis.AIM To explore the value of differential diagnosis of PFCD and glandular anal fistula by comparing the textural feature parameters of the rectum and anal canal in fat suppression T2-weighted imaging(FS-T2WI).METHODS Patients with rectal water sac implantation were screened from the first part of this study(48 patients with PFCD and 22 patients with glandular anal fistula).Open-source software ITK-SNAP(Version 3.6.0,http://www.itksnap.org/)was used to delineate the region of interest(ROI)of the entire rectum and anal canal wall on every axial section,and then the ROIs were input in the Analysis Kit software(version V3.0.0.R,GE Healthcare)to calculate the textural feature parameters.Textural feature parameter differences of the rectum and anal canal wall between the PFCD group vs the glandular anal fistula group were analyzed using Mann-Whitney U test.The redundant textural parameters were screened by bivariate Spearman correlation analysis,and binary logistic regression analysis was used to establish the model of textural feature parameters.Finally,diagnostic accuracy was assessed by receiver operating characteristic-area under the curve(AUC)analysis.RESULTS In all,385 textural parameters were obtained,including 37 parameters with statistically significant differences between the PFCD and glandular anal fistula groups.Then,16 texture feature parameters remained after bivariate Spearman correlation analysis,including one histogram parameter(Histogram energy);four grey level co-occurrence matrix(GLCM)parameters(GLCM energy_all direction_offset1_SD,GLCM entropy_all direction_offset4_SD,GLCM entropy_all direction_offset7_SD,and Haralick correlation_all direction_offset7_SD);four texture parameters(Correlation_all direction_offset1_SD,cluster prominence_angle 90_offset4,Inertia_all direction_offset7_SD,and cluster shade_angle 45_offset7);five grey level run-length matrix parameters(grey level nonuniformity_angle 90_offset1,grey level nonuniformity_all direction_offset4_SD,long run high grey level emphasis_all direction_offset1_SD,long run emphasis_all direction_offset4_SD,and long run high grey level emphasis_all direction_offset4_SD);and two form factor parameters(surface area and maximum 3D diameter).The AUC,sensitivity,and specificity of the model of textural feature parameters were 0.917,85.42%,and 86.36%,respectively.CONCLUSION The model of textural feature parameters showed good diagnostic performance for PFCD.The texture feature parameters of the rectum and anal canal in FS-T2WI are helpful to distinguish PFCD from glandular anal fistula.

Value of Texture Analysis of Intravoxel Incoherent Motion Parameters in Differential Diagnosis of Pancreatic Neuroendocrine Tumor and Pancreatic Adenocarcinoma

Objective To evaluate the value of texture features derived from intravoxel incoherent motion(IVIM) parameters for differentiating pancreatic neuroendocrine tumor(pNET) from pancreatic adenocarcinoma(PAC).Methods Eighteen patients with pNET and 32 patients with PAC were retrospectively enrolled in this study. All patients underwent diffusion-weighted imaging with 10 b values used(from 0 to 800 s/mm2). Based on IVIM model, perfusion-related parameters including perfusion fraction(f), fast component of diffusion(Dfast) and true diffusion parameter slow component of diffusion(Dslow) were calculated on a voxel-by-voxel basis and reorganized into gray-encoded parametric maps. The mean value of each IVIM parameter and texture features [Angular Second Moment(ASM), Inverse Difference Moment(IDM), Correlation, Contrast and Entropy] values of IVIM parameters were measured. Independent sample t-test or Mann-Whitney U test were performed for the betweengroup comparison of quantitative data. Regression model was established by using binary logistic regression analysis, and receiver operating characteristic(ROC) curve was plotted to evaluate the diagnostic efficiency.Results The mean f value of the pNET group were significantly higher than that of the PAC group(27.0% vs. 19.0%, P = 0.001), while the mean values of Dfast and Dslow showed no significant differences between the two groups. All texture features(ASM, IDM, Correlation, Contrast and Entropy) of each IVIM parameter showed significant differences between the pNET and PAC groups(P = 0.000-0.043). Binary logistic regression analysis showed that texture ASM of Dfast and texture Correlation of Dslow were considered as the specific imaging variables for the differential diagnosis of pNET and PAC. ROC analysis revealed that multiple texture features presented better diagnostic performance than IVIM parameters(AUC 0.849-0.899 vs. 0.526-0.776), and texture ASM of Dfast combined with Correlation of Dslow in the model of logistic regression had largest area under ROC curve for distinguishing pNET from PAC(AUC 0.934, cutoff 0.378, sensitivity 0.889, specificity 0.854). Conclusion Texture analysis of IVIM parameters could be an effective and noninvasive tool to differentiate pNET from PAC.

Value of Texture Analysis on Gadoxetic Acid-enhanced MR for Detecting Liver Fibrosis in a Rat Model

Objective To explore the ability of texture analysis of gadoxetic acid-enhanced magnetic resonance imaging(MRI) T1 mapping images, as well as T1-weighted(T1 W), T2-weighted(T2 W) and apparent diffusion coefficient(ADC) maps for distinguishing between varying degrees of hepatic fibrosis in an experimental rat model.Methods Liver fibrosis in rats was induced by carbon tetrachloride intraperitoneal injection for 4–12 weeks(n = 30). In the control group(n = 10) normal saline was applied. The MRI protocol contained T2 W, diffusion weighted imaging, pre-and post-contrast image series of T1 W and T1 mapping images. METAVIR score was used to grade liver fibrosis as normal(F0), mild fibrosis(F1–2), and advanced fibrosis(F3–4). Texture parameters including mean gray-level intensity(Mean), standard deviation(SD), Entropy, mean of positive pixels(MPP), Skewness, and Kurtosis were obtained. Nonparametric Mann-Whitney U test was used to compare the average value of each texture parameter in each sequence for assessing the difference between F0 and F≥1 as well as F0–2 and F3–4. Receiver operating characteristic(ROC) curves were obtained to assess the diagnosing accuracy of the parameters for differentiating no liver fibrosis from liver fibrosis and rats with liver fibrosis grading F0–2 from those with grading F3–4. The area under ROC curve(AUC) was calculated to evaluate the diagnostic efficiency of texture parameters.Results Finally, 20 rats completed MR T1 mapping image scan. The pathologic staging of these 20 rats was no fibrosis(F0, n = 6), mild fibrosis(F1–2, n = 5) and advanced fibrosis(F3–4, n = 9). On pre-contrast T1 mapping image, Entropy was seen to be statistically significant higher in the F≥1 group than that in the F0 group at each spatial scaling factor(SSF) setting(P = 0.015, 0.015, 0.015, 0.013, 0.015 and 0.018 respectively to SSF = 0, 2, 3, 4,5, 6), and Mean of the F≥1 rats was statistically significant higher than that of the F0 rats at SSF 4, 5, 6(P = 0.004, 0.006, and 0.013, respectively). Entropy and Mean showed a moderate diagnostic performance in most SSF settings of T1 mapping pre-contrast images for differentiation of normal liver from liver fibrosis.Conclusion Certain texture features of gadoxetic acid-enhanced MR images, especially the Entropy of noncontrast T1 mapping image, was found to be a useful biomarker for the diagnosis of liver fibrosis.

Application of texture signatures based on multiparameter-magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma:Retrospective study

BACKGROUND Despite continuous changes in treatment methods,the survival rate for advanced hepatocellular carcinoma(HCC)patients remains low,highlighting the importance of diagnostic methods for HCC.AIM To explore the efficacy of texture analysis based on multi-parametric magnetic resonance(MR)imaging(MRI)in predicting microvascular invasion(MVI)in preoperative HCC.METHODS This study included 105 patients with pathologically confirmed HCC,categorized into MVI-positive and MVI-negative groups.We employed Original Data Analysis,Principal Component Analysis,Linear Discriminant Analysis(LDA),and Non-LDA(NDA)for texture analysis using multi-parametric MR images to predict preoperative MVI.The effectiveness of texture analysis was determined using the B11 program of the MaZda4.6 software,with results expressed as the misjudgment rate(MCR).RESULTS Texture analysis using multi-parametric MRI,particularly the MI+PA+F dimensionality reduction method combined with NDA discrimination,demonstrated the most effective prediction of MVI in HCC.Prediction accuracy in the pulse and equilibrium phases was 83.81%.MCRs for the combination of T2-weighted imaging(T2WI),arterial phase,portal venous phase,and equilibrium phase were 22.86%,16.19%,20.95%,and 20.95%,respectively.The area under the curve for predicting MVI positivity was 0.844,with a sensitivity of 77.19%and specificity of 91.67%.CONCLUSION Texture analysis of arterial phase images demonstrated superior predictive efficacy for MVI in HCC compared to T2WI,portal venous,and equilibrium phases.This study provides an objective,non-invasive method for preoperative prediction of MVI,offering a theoretical foundation for the selection of clinical therapy.

Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques.This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal cases.Texture information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering filters.Machine learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding diagnosis.The ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different pathologies.Furthermore,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased analysis.This analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered images.Notably,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

MRI Histogram Texture Feature Analysis of the Optic Nerve in the Patients with Optic Neuritis

Objective To evaluate the optic nerve impairment using MRI histogram texture analysis in the patients with optic neuritis.Methods The study included 60 patients with optic neuritis and 20 normal controls. The coronal T2 weighted imaging(T2 WI) with fat saturation and enhanced T1 weighted imaging(T1 WI) were performed to evaluate the optic nerve. MRI histogram texture features of the involved optic nerve were measured on the corresponding coronal T2 WI images. The normal optic nerve(NON) was measured in the posterior 1/3 parts of the optic nerve. Kruskal-Wallis one-way ANOVA was used to compare the difference of texture features and receiver operating characteristic(ROC) curve were performed to evaluate the diagnostic value of texture features for the optic nerve impairment among the affected optic nerve with enhancement(ONwEN), affected optic nerve without enhancement(ONwoEN), contralateral normal appearing optic nerve(NAON) and NON. Results The histogram texture Energy and Entropy presented significant differences for ONwEN vs. ONwoEN(both P = 0.000), ONwEN vs. NON(both P = 0.000) and NAON vs. NON(both P < 0.05). ROC analysis demonstrated that the area under the curve(AUC) of histogram texture Energy were 0.758, 0.795 and 0.701 for ONwEN vs. ONwoEN, ONwEN vs. NON and NAON vs. NON, AUC of Entropy were 0.758, 0.795 and 0.707 for ONwEN vs. ONwoEN, ONwEN vs. NON and NAON vs. NON.Conclusion The altered MRI histogram texture Energy and Entropy could be considered as a surrogate for MRI enhancement to evaluate the involved optic nerve and normal-appearing optic nerve in optic neuritis.

Application and Progress of Texture Analysis in the Therapeutic Effect Prediction and Prognosis of Neoadjuvant Chemoradiotherapy for Colorectal Cancer

Colorectal cancer is one of the most common malignant tumors, and the morbidity and mortality are increasing gradually over the last years in China. Neoadjuvant chemoradiotherapy(nCRT) is currently applied to the treatment of colorectal cancer patients, and it is helpful to improve the prognosis. The sensitivity of patients to nCRT is different due to individual differences. Predicting the therapeutic effect of nCRT is of great importance for the further treatment methods. Texture analysis, as an image post-processing technique, has been more and more utilized in the field of oncologic imaging. This article reviews the application and progress of texture analysis in the therapeutic effect prediction and prognosis of nCRT for colorectal cancer.

Differential Diagnostic Value of Texture Feature Analysis of Magnetic Resonance T2 Weighted Imaging between Glioblastoma and Primary Central Neural System Lymphoma

Objective To investigate the difference in tumor conventional imaging findings and texture features on T2 weighted images between glioblastoma and primary central neural system(CNS) lymphoma. Methods The pre-operative MRI data of 81 patients with glioblastoma and 28 patients with primary CNS lymphoma admitted to the Chinese PLA General Hospital and Hainan Hospital of Chinese PLA General Hospital were retrospectively collected. All patients underwent plain MR imaging and enhanced T1 weighted imaging to visualize imaging features of lesions. Texture analysis of T2 weighted imaging(T2 WI) was performed by use of GLCM texture plugin of ImageJ software, and the texture parameters including Angular Second Moment(ASM), Contrast, Correlation, Inverse Difference Moment(IDM), and Entropy were measured. Independent sample t-test and Mann-Whitney U test were performed for the between-group comparisons, regression model was established by Binary Logistic regression analysis, and receiver operating characteristic(ROC) curve was plotted to compare the diagnostic efficacy. Results The conventional imaging features including cystic and necrosis changes(P = 0.000), ‘Rosette' changes(P = 0.000) and ‘incision sign'(P = 0.000), except ‘flame-like edema'(P = 0.635), presented significantly statistical difference between glioblastoma and primary CNS lymphoma. The texture features, ASM, Contrast, Correlation, IDM and Entropy, showed significant differences between glioblastoma and primary CNS lympoma(P = 0.006,0.000, 0.002, 0.000, and 0.015 respectively). The area under the ROC curve was 0.671, 0.752, 0.695, 0.720 and 0.646 respectively, and the area under the ROC curve was 0.917 for the combined texture variables(Contrast, cystic and necrosis, ‘Rosette' changes, and ‘incision sign') in the model of Logistic regression. Binary Logistic regression analysis demonstrated that cystic and necrosis changes, ‘Rosette' changes and ‘incision sign' and texture Contrast could be considered as the specific texture variables for the differential diagnosis of glioblastoma and primary CNS lymphoma. Conclusion The texture features of T2 WI and conventional imaging findings may be used to distinguish glioblastoma from primary CNS lymphoma.

Value of Magnetic Resonance Imaging Texture Analysis in the Differential Diagnosis of Benign and Malignant Breast Tumors

Objective To investigate the difference in texture features on diffusion weighted imaging(DWI) images between breast benign and malignant tumors.Methods Patients including 56 with mass-like breast cancer, 16 with breast fibroadenoma, and 4 with intraductal papilloma of breast treated in the Hainan Hospital of Chinese PLA General Hospital were retrospectively enrolled in this study, and allocated to the benign group(20 patients) and the malignant group(56 patients) according to the post-surgically pathological results. Texture analysis was performed on axial DWI images, and five characteristic parameters including Angular Second Moment(ASM), Contrast, Correlation, Inverse Difference Moment(IDM), and Entropy were calculated. Independent sample t-test and Mann-Whitney U test were performed for intergroup comparison. Regression model was established by using Binary Logistic regression analysis, and receiver operating characteristic curve(ROC) analysis was carried out to evaluate the diagnostic efficiency. Results The texture features ASM, Contrast, Correlation and Entropy showed significant differences between the benign and malignant breast tumor groups(PASM= 0.014, Pcontrast= 0.019, Pcorrelation= 0.010, Pentropy= 0.007). The area under the ROC curve was 0.685, 0.681, 0.754, and 0.683 respectively for the positive texture variables mentioned above, and that for the combined variables(ASM, Contrast, and Entropy) was 0.802 in the model of Logistic regression. Binary Logistic regression analysis demonstrated that ASM, Contrast and Entropy were considered as thespecific imaging variables for the differential diagnosis of breast benign and malignant tumors.Conclusion The texture analysis of DWI may be a simple and effective tool in the differential diagnosis between breast benign and malignant tumors.

Pre-stack-texture-based reservoir characteristics and seismic facies analysis

Seismic texture attributes are closely related to seismic facies and reservoir characteristics and are thus widely used in seismic data interpretation.However,information is mislaid in the stacking process when traditional texture attributes are extracted from poststack data,which is detrimental to complex reservoir description.In this study,pre-stack texture attributes are introduced,these attributes can not only capable of precisely depicting the lateral continuity of waveforms between different reflection points but also reflect amplitude versus offset,anisotropy,and heterogeneity in the medium.Due to its strong ability to represent stratigraphies,a pre-stack-data-based seismic facies analysis method is proposed using the selforganizing map algorithm.This method is tested on wide azimuth seismic data from China,and the advantages of pre-stack texture attributes in the description of stratum lateral changes are verified,in addition to the method＇s ability to reveal anisotropy and heterogeneity characteristics.The pre-stack texture classification results effectively distinguish different seismic reflection patterns,thereby providing reliable evidence for use in seismic facies analysis.

Texture analysis on gadoxetic acid enhanced-MRI for predicting Ki-67 status in hepatocellular carcinoma: A prospective study

Objective: To investigate the value of whole-lesion texture analysis on preoperative gadoxetic acid enhanced magnetic resonance imaging(MRI) for predicting tumor Ki-67 status after curative resection in patients with hepatocellular carcinoma(HCC).Methods: This study consisted of 89 consecutive patients with surgically confirmed HCC. Texture features were extracted from multiparametric MRI based on whole-lesion regions of interest. The Ki-67 status was immunohistochemical determined and classified into low Ki-67(labeling index ≤15%) and high Ki-67(labeling index >15%) groups. Least absolute shrinkage and selection operator(LASSO) and multivariate logistic regression were applied for generating the texture signature, clinical nomogram and combined nomogram. The discrimination power, calibration and clinical usefulness of the three models were evaluated accordingly. Recurrence-free survival(RFS) rates after curative hepatectomy were also compared between groups.Results: A total of 13 texture features were selected to construct a texture signature for predicting Ki-67 status in HCC patients(C-index: 0.878, 95% confidence interval: 0.791-0.937). After incorporating texture signature to the clinical nomogram which included significant clinical variates(AFP, BCLC-stage, capsule integrity, tumor margin,enhancing capsule), the combined nomogram showed higher discrimination ability(C-index: 0.936 vs. 0.795,P<0.001), good calibration(P>0.05 in Hosmer-Lemeshow test) and higher clinical usefulness by decision curve analysis. RFS rate was significantly lower in the high Ki-67 group compared with the low Ki-67 group after curative surgery(63.27% vs. 85.00%, P<0.05).Conclusions: Texture analysis on gadoxetic acid enhanced MRI can serve as a noninvasive approach to preoperatively predict Ki-67 status of HCC after curative resection. The combination of texture signature and clinical factors demonstrated the potential to further improve the prediction performance.

Principal Component Analysis of Cooked Rice Texture Qualities

Texture qualities of cooked rice are comprised of many indexes with the complex relationship, so it is difficult to analyze and evaluate cooked rice. In this paper, the related indexes of texture properties were conversed into the independent indexes of principal component based on the principal component analysis method. The results showed that the rice kernel types influenced the meanings of principal components indexes. For long and short rice, the first principal component was comprehensive index. But the second principal component was springiness for the short rice, while it was adhesiveness for long rice. Therefore, the first principal component can be used to express the quality of cooked rice with a few of indexes, and the rice type can be recognized according to the second principal component.

Diagnostic value of two dimensional shear wave elastography combined with texture analysis in early liver fibrosis

BACKGROUND Staging diagnosis of liver fibrosis is a prerequisite for timely diagnosis and therapy in patients with chronic hepatitis B.In recent years,ultrasound elastography has become an important method for clinical noninvasive assessment of liver fibrosis stage,but its diagnostic value for early liver fibrosis still needs to be further improved.In this study,the texture analysis was carried out on the basis of two dimensional shear wave elastography(2D-SWE),and the feasibility of 2D-SWE plus texture analysis in the diagnosis of early liver fibrosis was discussed.AIM To assess the diagnostic value of 2D-SWE combined with textural analysis in liver fibrosis staging.METHODS This study recruited 46 patients with chronic hepatitis B.Patients underwent 2DSWE and texture analysis;Young's modulus values and textural patterns were obtained,respectively.Textural pattern was analyzed with regard to contrast,correlation,angular second moment(ASM),and homogeneity.Pathological results of biopsy specimens were the gold standard;comparison and assessment of the diagnosis efficiency were conducted for 2D-SWE,texture analysis and their combination.RESULTS 2D-SWE displayed diagnosis efficiency in early fibrosis,significant fibrosis,severe fibrosis,and early cirrhosis(AUC>0.7,P<0.05)with respective AUC values of 0.823(0.678-0.921),0.808(0.662-0.911),0.920(0.798-0.980),and 0.855(0.716-0.943).Contrast and homogeneity displayed independent diagnosis efficiency in liver fibrosis stage(AUC>0.7,P<0.05),whereas correlation and ASM showed limited values.AUC of contrast and homogeneity were respectively 0.906(0.779-0.973),0.835(0.693-0.930),0.807(0.660-0.910)and 0.925(0.805-0.983),0.789(0.639-0.897),0.736(0.582-0.858),0.705(0.549-0.883)and 0.798(0.650-0.904)in four liver fibrosis stages,which exhibited equivalence to 2D-SWE in diagnostic efficiency(P>0.05).Combined diagnosis(PRE)displayed diagnostic efficiency(AUC>0.7,P<0.01)for all fibrosis stages with respective AUC of 0.952(0.841-0.994),0.896(0.766-0.967),0.978(0.881-0.999),0.947(0.835-0.992).The combined diagnosis showed higher diagnosis efficiency over 2D-SWE in early liver fibrosis(P<0.05),whereas no significant differences were observed in other comparisons(P>0.05).CONCLUSION Texture analysis was capable of diagnosing liver fibrosis stage,combined diagnosis had obvious advantages in early liver fibrosis,liver fibrosis stage might be related to the hepatic tissue hardness distribution.

Prediction of different stages of rectal cancer: Texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps

BACKGROUND It is evident that an accurate evaluation of T and N stage rectal cancer is essential for treatment planning.It has not been extensively investigated whether texture features derived from diffusion-weighted imaging(DWI)images and apparent diffusion coefficient(ADC)maps are associated with the extent of local invasion(pathological stage T1-2 vs T3-4)and nodal involvement(pathological stage N0 vs N1-2)in rectal cancer.AIM To predict different stages of rectal cancer using texture analysis based on DWI images and ADC maps.METHODS One hundred and fifteen patients with pathologically proven rectal cancer,who underwent preoperative magnetic resonance imaging,including DWI,were enrolled,retrospectively.The ADC measurements(ADCmean,ADCmin,ADCmax)as well as texture features,including the gray level co-occurrence matrix parameters,the gray level run-length matrix parameters and wavelet parameters were calculated based on DWI(b=0 and b=1000)images and the ADC maps.Independent sample t-tests or Mann-Whitney U tests were used for statistical analysis.Multivariate logistic regression analysis was conducted to establish the models.The predictive performance was validated by receiver operating characteristic curve analysis.RESULTS Dissimilarity,sum average,information correlation and run-length nonuniformity from DWIb=0 images,gray level nonuniformity,run percentage and run-length nonuniformity from DWIb=1000 images,and dissimilarity and run percentage from ADC maps were found to be independent predictors of local invasion(stage T3-4).The area under the operating characteristic curve of the model reached 0.793 with a sensitivity of 78.57%and a specificity of 74.19%.Sum average,gray level nonuniformity and the horizontal components of symlet transform(SymletH)from DWIb=0 images,sum average,information correlation,long run low gray level emphasis and SymletH from DWIb=1000 images,and ADCmax,ADCmean and information correlation from ADC maps were identified as independent predictors of nodal involvement.The area under the operating characteristic curve of the model reached 0.802 with a sensitivity of 80.77%and a specificity of 68.25%.CONCLUSION Texture features extracted from DWI images and ADC maps are useful clues for predicting pathological T and N stages in rectal cancer.

Texture analysis on parametric maps derived from dynamic contrast-enhanced magnetic resonance imaging in head and neck cancer

AIM: To investigate the merits of texture analysis on parametric maps derived from pharmacokinetic modeling with dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI) as imaging biomarkers for the prediction of treatment response in patients with head and neck squamous cell carcinoma(HNSCC). METHODS: In this retrospective study,19 HNSCC patients underwent pre- and intra-treatment DCEMRI scans at a 1.5T MRI scanner. All patients had chemo-radiation treatment. Pharmacokinetic modeling was performed on the acquired DCE-MRI images,generating maps of volume transfer rate(Ktrans) and volume fraction of the extravascular extracellular space(ve). Image texture analysis was then employed on maps of Ktrans and ve,generating two texture measures: Energy(E) and homogeneity.RESULTS: No significant changes were found for the mean and standard deviation for Ktrans and ve between pre- and intra-treatment(P > 0.09). Texture analysis revealed that the imaging biomarker E of ve was significantly higher in intra-treatment scans,relative to pretreatment scans(P < 0.04). CONCLUSION: Chemo-radiation treatment in HNSCC significantly reduces the heterogeneity of tumors.

Texture features analysis on micro-structure of paste backfill based on image analysis technology

The strength of cement-based materials,such as mortar,concrete and cement paste backfill(CPB),depends on its microstructures(e.g.pore structure and arrangement of particles and skeleton).Numerous studies on the relationship between strength and pore structure(e.g.,pore size and its distribution)were performed,but the micro-morphology characteristics have been rarely concerned.Texture describing the surface properties of the sample is a global feature,which is an effective way to quantify the micro-morphological properties.In statistical analysis,GLCM features and Tamura texture are the most representative methods for characterizing the texture features.The mechanical strength and section image of the backfill sample prepared from three different solid concentrations of paste were obtained by uniaxial compressive strength test and scanning electron microscope,respectively.The texture features of different SEM images were calculated based on image analysis technology,and then the correlation between these parameters and the strength was analyzed.It was proved that the method is effective in the quantitative analysis on the micro-morphology characteristics of CPB.There is a significant correlation between the texture features and the unconfined compressive strength,and the prediction of strength is feasible using texture parameters of the CPB microstructure.

Use of digital image analysis combined with fractal theory to determine particle morphology and surface texture of quartz sands

The particle morphology and surface texture play a major role in influencing mechanical and hydraulic behaviors of sandy soils. This paper presents the use of digital image analysis combined with fractal theory as a tool to quantify the particle morphology and surface texture of two types of quartz sands widely used in the region of Vitória, Espírito Santo, southeast of Brazil. The two investigated sands are sampled from different locations. The purpose of this paper is to present a simple, straightforward,reliable and reproducible methodology that can identify representative sandy soil texture parameters.The test results of the soil samples of the two sands separated by sieving into six size fractions are presented and discussed. The main advantages of the adopted methodology are its simplicity, reliability of the results, and relatively low cost. The results show that sands from the coastal spit(BS) have a greater degree of roundness and a smoother surface texture than river sands(RS). The values obtained in the test are statistically analyzed, and again it is confirmed that the BS sand has a slightly greater degree of sphericity than that of the RS sand. Moreover, the RS sand with rough surface texture has larger specific surface area values than the similar BS sand, which agree with the obtained roughness fractal dimensions. The consistent experimental results demonstrate that image analysis combined with fractal theory is an accurate and efficient method to quantify the differences in particle morphology and surface texture of quartz sands.