1 Introduction The Wulong glomerophyric diorite porphyry has an extremely peculiar texture with plagioclase phenocrysts clustered as flower-like glomerocrysts(Figs.1a&b),which is never discovered elsewhere of the ...1 Introduction The Wulong glomerophyric diorite porphyry has an extremely peculiar texture with plagioclase phenocrysts clustered as flower-like glomerocrysts(Figs.1a&b),which is never discovered elsewhere of the world.The展开更多
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 solu...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.展开更多
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 uterin...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.展开更多
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify sp...Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.展开更多
BACKGROUND Microvascular invasion(MVI)is a significant risk factor for recurrence and metastasis following hepatocellular carcinoma(HCC)surgery.Currently,there is a paucity of preoperative evaluation approaches for MV...BACKGROUND Microvascular invasion(MVI)is a significant risk factor for recurrence and metastasis following hepatocellular carcinoma(HCC)surgery.Currently,there is a paucity of preoperative evaluation approaches for MVI.AIM To investigate the predictive value of texture features and radiological signs based on multiparametric magnetic resonance imaging in the non-invasive preoperative prediction of MVI in HCC.METHODS Clinical data from 97 HCC patients were retrospectively collected from January 2019 to July 2022 at our hospital.Patients were classified into two groups:MVI-positive(n=57)and MVI-negative(n=40),based on postoperative pathological results.The correlation between relevant radiological signs and MVI status was analyzed.MaZda4.6 software and the mutual information method were employed to identify the top 10 dominant texture features,which were combined with radiological signs to construct artificial neural network(ANN)models for MVI prediction.The predictive performance of the ANN models was evaluated using area under the curve,sensitivity,and specificity.ANN models with relatively high predictive performance were screened using the DeLong test,and the regression model of multilayer feedforward ANN with backpropagation and error backpropagation learning method was used to evaluate the models’stability.RESULTS The absence of a pseudocapsule,an incomplete pseudocapsule,and the presence of tumor blood vessels were identified as independent predictors of HCC MVI.The ANN model constructed using the dominant features of the combined group(pseudocapsule status+tumor blood vessels+arterial phase+venous phase)demonstrated the best predictive performance for MVI status and was found to be automated,highly operable,and very stable.CONCLUSION The ANN model constructed using the dominant features of the combined group can be recommended as a noninvasive method for preoperative prediction of HCC MVI status.展开更多
The chemical,physical,thermal and texture properties of iron ores from different regions of Odisha and Chhattisgarh regions,India,have been investigated to understand the compositional variations of Fe,Al2O3,SiO2,S an...The chemical,physical,thermal and texture properties of iron ores from different regions of Odisha and Chhattisgarh regions,India,have been investigated to understand the compositional variations of Fe,Al2O3,SiO2,S and P.They were analyzed for its susceptibility to meet the industrial requirements,for various iron manufacture techniques.Chemical analysis indicated that the majority of the iron ores is rich in hematite(>90wt%),poor in gangue(<4.09wt%SiO2and<3.8wt%Al2O3)and deleterious elements(P<0.065wt%and S<0.016wt%)in all these iron ores found to be low.XRD peaks reviled that the gangue is in the form of kaolinite and quartz,and same was observed in Fourier transform infrared(FTIR)spectroscopy in the range of914to1034cm–1.The iron ores were found to have excellent physical properties exemplify with tumbler index(82wt%–91wt%),abrasion index(1.27wt%–4.87wt%)and shatter index(0.87wt%–1.64wt%).FTIR and thermal analysis were performed to assimilate the analysis interpolations.It was found that these iron ores exhibit three endothermic reactions,which are dehydration below447K with mass loss of0.13wt%to1.7wt%,dehydroxylation at525–609K with mass loss of1.09wt%–4.49wt%and decomposition of aluminosilicates at597–850K with mass loss of0.13wt%–1.15wt%.From this study,we can conclude that due to its excellent physico-chemical characteristics,these iron ores are suitable for BF and DRI operations.展开更多
This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et...This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et al developed a robust predictive model demonstrating high accuracy(area under the curve 0.92 in the training cohort)by integrating venous phase radiomic features with alphafetoprotein levels.This noninvasive approach enables early identification of patients unlikely to benefit from transarterial chemoembolization,allowing a timely transition to alternative therapies such as targeted agents or immunotherapy.Such precision strategies may improve clinical outcomes,optimize resource utilization,and increase survival in advanced hepatocellular carcinoma management.Future studies should emphasize external validation and broader clinical adoption.展开更多
BACKGROUND Early identification of bowel resection risks is crucial for patients with incarcerated inguinal hernia(IIH).However,the prompt detection of these risks remains a significant challenge.Advancements in radio...BACKGROUND Early identification of bowel resection risks is crucial for patients with incarcerated inguinal hernia(IIH).However,the prompt detection of these risks remains a significant challenge.Advancements in radiomic feature extraction and machine learning algorithms have paved the way for innovative diagnostic approaches to assess IIH more effectively.AIM To devise a sophisticated radiomic-clinical model to evaluate bowel resection risks in IIH patients,thereby enhancing clinical decision-making processes.METHODS This single-center retrospective study analyzed 214 IIH patients randomized into training(n=161)and test(n=53)sets(3:1).Radiologists segmented hernia sac-trapped bowel volumes of interest(VOIs)on computed tomography images.Radiomic features extracted from VOIs generated Rad-scores,which were combined with clinical data to construct a nomogram.The nomogram’s performance was evaluated against standalone clinical and radiomic models in both cohorts.RESULTS A total of 1561 radiomic features were extracted from the VOIs.After dimensionality reduction,13 radiomic features were used with eight machine learning algorithms to develop the radiomic model.The logistic regression algorithm was ultimately selected for its effectiveness,showing an area under the curve(AUC)of 0.828[95%confidence interval(CI):0.753-0.902]in the training set and 0.791(95%CI:0.668-0.915)in the test set.The comprehensive nomogram,incorporating clinical indicators showcased strong predictive capabilities for assessing bowel resection risks in IIH patients,with AUCs of 0.864(95%CI:0.800-0.929)and 0.800(95%CI:0.669-0.931)for the training and test sets,respectively.Decision curve analysis revealed the integrated model’s superior performance over standalone clinical and radiomic approaches.CONCLUSION This innovative radiomic-clinical nomogram has proven to be effective in predicting bowel resection risks in IIH patients and has substantially aided clinical decision-making.展开更多
The present study deals with the textural characteristics of the sediments from the exposed palaeochannel situated at Dhyangla village near Ladwa, district of Kurukshetra in north-western Haryana. It is a part of vast...The present study deals with the textural characteristics of the sediments from the exposed palaeochannel situated at Dhyangla village near Ladwa, district of Kurukshetra in north-western Haryana. It is a part of vast alluvial plains of India. Samples were taken from a 3 m thick exposed section. Grain size distribution and Palynofacies analyses of these sediments were carried out to study their textural parameters and the organic matter content respectively. Sediments are mostly fine to medium grained sand with silt percentage increasing upwards in the section. These sediments are mostly unimodal showing the grain size population controlled by a single type of grain size, mostly sand. Further the sand samples are moderately well sorted and mesokurtic in nature. Samples with silt are poorly sorted and leptokurtic with positive skewness, denoting those sediments have already sorted elsewhere in the high energy environment and are now transported and modified by the low energy environment. Palynofacies analysis also shows the presence of amorphous particles in the silt samples which indicate low energy environment of the sequence while presence of black debris in the sand samples indicates high energy depositions environment.展开更多
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...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.展开更多
Objective Texture analysis is deemed to reflect intratumor heterogeneity invisible to the naked eyes.The aim of this study was to evaluate the feasibility of assessing the KRAS mutational status in colorectal cancer(C...Objective Texture analysis is deemed to reflect intratumor heterogeneity invisible to the naked eyes.The aim of this study was to evaluate the feasibility of assessing the KRAS mutational status in colorectal cancer(CRC)patients using CT texture analysis.Methods This retrospective study included 92 patients who had histopathologically confirmed CRC and underwent preoperative contrast-enhanced CT examinations.The patients were assigned into a training cohort(n=51)and a validation cohort(n=41).We placed the region of interest in the tumour regions on the selected axial images using software of lexRad to extract a series of quantitative parameters based on the spatial scaling factors(SSFs),including mean,standard deviation(SD),entropy,mean of positive pixels(MPP),skewness,and kurtosis.The texture parameters and clinical characteristics(age,gender,tumour location,histopathology;tumour size,1 N,M stages)were compared between the mutated and wild-type KRAS patient groups in training cohort and validation cohort.Before building the multiple feature classifier,we calculated the correlations of the features using Pearsons correlation coefficient,and if any two features were significantly correlated,the one with lower AUC was removed.Ultimately,only the most discriminative isolated features were combined to train a supporting vector machine(SVM)classifier.The receiver operating characteristic(ROC)curve was processed for evaluating the diagnostic efficiency of texture parameters in differentiating CRC patients with mutated KRAS from those with wild-type KRAS.Results None of the clinical characteristics were significant different between CRC patients with wild-type KRAS and mutated KRAS in both cohorts.For predicting the expression of mutated KRAS in CRC patients,the perfect model which combined skewness on SSF 5 by unenhanced CT,entropy on SSF 2,skewness and kurtosis on SSF 0,and kurtosis and mean on SSF 3 by enhanced CT,showed a desirable AUC of 0.951(95%CI:0.895-1,P<0.001),with a sensitivity of 88.9%and a specificity of 91.7%,when the cut-off value was 0.46 in the training cohort;while in the validation cohort,the AUC value was 0.995(95%CI:0.982-1,P<0.001),the sensitivity was 100%,and the specificity was 93.7%when the cut-off value was 0.28.Conclusion It is feasible to evaluate the KRAS mutational status in CRC using CT texture analysis.展开更多
Background:Radiofrequency ablation(RFA)is one of the effective therapeutic modalities in patients with hepatocellular carcinoma(HCC).However,there is no proper method to evaluate the HCC response to RFA.This study aim...Background:Radiofrequency ablation(RFA)is one of the effective therapeutic modalities in patients with hepatocellular carcinoma(HCC).However,there is no proper method to evaluate the HCC response to RFA.This study aimed to establish and validate a clinical prediction model based on dual-energy com-puted tomography(DECT)quantitative-imaging parameters,clinical variables,and CT texture parameters.Methods:We enrolled 63 patients with small HCC.Two to four weeks after RFA,we performed DECT scanning to obtain DECT-quantitative parameters and to record the patients’clinical baseline variables.DECT images were manually segmented,and 56 CT texture features were extracted.We used LASSO al-gorithm for feature selection and data dimensionality reduction;logistic regression analysis was used to build a clinical model with clinical variables and DECT-quantitative parameters;we then added texture features to build a clinical-texture model based on clinical model.Results:A total of six optimal CT texture analysis(CTTA)features were selected,which were statis-tically different between patients with or without tumor progression(P<0.05).When clinical vari-ables and DECT-quantitative parameters were included,the clinical models showed that albumin-bilirubin grade(ALBI)[odds ratio(OR)=2.77,95%confidence interval(CI):1.35-6.65,P=0.010],λAP(40-100 keV)(OR=3.21,95%CI:3.16-5.65,P=0.045)and IC AP(OR=1.25,95%CI:1.01-1.62,P=0.028)were asso-ciated with tumor progression,while the clinical-texture models showed that ALBI(OR=2.40,95%CI:1.19-5.68,P=0.024),λAP(40-100 keV)(OR=1.43,95%CI:1.10-2.07,P=0.019),and CTTA-score(OR=2.98,95%CI:1.68-6.66,P=0.001)were independent risk factors for tumor progression.The clinical model,clinical-texture model,and CTTA-score all performed well in predicting tumor progression within 12 months after RFA(AUC=0.917,0.962,and 0.906,respectively),and the C-indexes of the clinical and clinical-texture models were 0.917 and 0.957,respectively.Conclusions:DECT-quantitative parameters,CTTA,and clinical variables were helpful in predicting HCC progression after RFA.The constructed clinical prediction model can provide early warning of potential tumor progression risk for patients after RFA.展开更多
Background:Liver fibrosis is a common pathological change caused by a variety of etiologies.Early diagnosis and timely treatment can reverse or delay disease progression and improve the prognosis.This study aimed to a...Background:Liver fibrosis is a common pathological change caused by a variety of etiologies.Early diagnosis and timely treatment can reverse or delay disease progression and improve the prognosis.This study aimed to assess the potential utility of two-dimensional shear wave elastography and texture analysis in dynamic monitoring of the progression of liver fibrosis in rat model.Methods:Twenty rats were divided into control group(n=4)and experimental groups(n=4 per group)with carbon tetrachloride administration for 2,3,4,and 6 weeks.The liver stiffness measurement was performed by two-dimensional shear wave elastography,while the optimal texture analysis subsets to distinguish fibrosis stage were generated by MaZda.The results of elastography and texture analysis were validated through comparing with histopathology.Results:Liver stiffness measurement was 6.09±0.31 kPa in the control group and 7.10±0.41 kPa,7.80±0.93 kPa,8.64±0.93 kPa,9.91±1.13 kPa in the carbon tetrachloride induced groups for 2,3,4,6 weeks,respectively(P<0.05).By texture analysis,histogram and co-occurrence matrix had the most frequency texture parameters in staging liver fibrosis.Receiver operating characteristic curve of liver elasticity showed that the sensitivity and specificity were 95.0%and 92.5%to discriminate liver fibrosis and non-fibrosis,respectively.In texture analysis,five optimal parameters were selected to classify liver fibrosis and non-fibrosis.Conclusions:Two-dimensional shear wave elastography showed potential applications for noninvasive monitoring of the progression of hepatic fibrosis,even in mild fibrosis.Texture analysis can further extract and quantify the texture features in ultrasonic image,which was a supplementary to further visual information and acquired high diagnostic accuracy for severe fibrosis.展开更多
Objective:The objective of this study was to investigate the application of unenhanced computed tomography(CT)texture analysis in differentiating pancreatic adenosquamous carcinoma(PASC)from pancreatic ductal adenocar...Objective:The objective of this study was to investigate the application of unenhanced computed tomography(CT)texture analysis in differentiating pancreatic adenosquamous carcinoma(PASC)from pancreatic ductal adenocarcinoma(PDAC).Methods:Preoperative CT images of 112 patients(31 with PASC,81 with PDAC)were retrospectively reviewed.A total of 396 texture parameters were extracted from AnalysisKit software for further texture analysis.Texture features were selected for the differentiation of PASC and PDAC by the Mann-Whitney U test,univariate logistic regression analysis,and the minimum redundancy maximum relevance algorithm.Furthermore,receiver operating characteristic(ROC)curve analysis was performed to evaluate the diagnostic performance of the texture feature-based model by the random forest(RF)method.Finally,the robustness and reproducibility of the predictive model were assessed by the 10-times leave-group-out cross-validation(LGOCV)method.展开更多
Computer forensics and identification for traditional Chinese painting arts have caught the attention of various fields. Rice paper＇s feature extraction and analysis methods are of high significance for the rice pape...Computer forensics and identification for traditional Chinese painting arts have caught the attention of various fields. Rice paper＇s feature extraction and analysis methods are of high significance for the rice paper is an important carrier of traditional Chinese painting arts. In this paper, rice paper＇s morphological feature analysis is done using multi spectral imaging technology. The multispectral imaging system is utilized to acquire rice paper＇s spectral images in different wave- length channels, and then those spectral images are measured using texture parameter statistics to acquire sensitive bands for rice paper＇s feature. The mathematical morphology and grayscale statistical principle are utilized to establish a rice paper＇s morphological feature analytical model which is used to acquire rice paper＇ s one-dimensional vector. For the purpose of eval- uating these feature vectors＇ accuracy, they are entered into the support vector machine（SVM） classifier for detection and classification. The results show that the rice paper＇s feature is out loud in the spectral band 550 nm, and the average classifi- cation accuracy of feature vectors output from the analytical model is 96 %. The results indicate that the rice paper＇s feature analytical model can extract most of rice paper＇s features with accuracy and efficiency.展开更多
This paper deals with an optimization design method for the Gabor filters based on the analysis of an iris texture model. By means of analyzing the properties of an iris texture image, the energy distribution regulari...This paper deals with an optimization design method for the Gabor filters based on the analysis of an iris texture model. By means of analyzing the properties of an iris texture image, the energy distribution regularity of the iris texture image measured by the average power spectrum density is exploited, and the theoretical ranges of the efficient valued frequency and orientation parameters can also be deduced. The analysis shows that the energy distribution of the iris texture is generally centralized around lower frequencies in the spatial frequency domain. Accordingly, an iterative algorithm is designed to optimize the Gabor parameter field. The experimental results indicate the validity of the theory and efficiency of the algorithm.展开更多
BACKGROUND The accuracy of discriminating pT3a from pT3b-c rectal cancer using highresolution magnetic resonance imaging(MRI)remains unsatisfactory,although texture analysis(TA)could improve such discrimination.AIM To...BACKGROUND The accuracy of discriminating pT3a from pT3b-c rectal cancer using highresolution magnetic resonance imaging(MRI)remains unsatisfactory,although texture analysis(TA)could improve such discrimination.AIM To investigate the value of TA on apparent diffusion coefficient(ADC)maps in differentiating pT3a rectal adenocarcinomas from pT3b-c tumors.METHODS This was a case-control study of 59 patients with pT3 rectal adenocarcinoma,who underwent diffusion-weighted imaging(DWI)between October 2016 and December 2018.The inclusion criteria were:(1)Proven pT3 rectal adenocarcinoma;(2)Primary MRI including high-resolution T2-weighted image(T2WI)and DWI;and(3)Availability of pathological reports for surgical specimens.The exclusion criteria were:(1)Poor image quality;(2)Preoperative chemoradiation therapy;and(3)A different pathological type.First-order(ADC values,skewness,kurtosis,and uniformity)and second-order(energy,entropy,inertia,and correlation)texture features were derived from whole-lesion ADC maps.Receiver operating characteristic curves were used to determine the diagnostic value for pT3b-c tumors.RESULTS The final study population consisted of 59 patients(34 men and 25 women),with a median age of 66 years(range,41-85 years).Thirty patients had pT3a,24 had pT3b,and five had pT3c.Among the ADC first-order textural differences between pT3a and pT3b-c rectal adenocarcinomas,only skewness was significantly lower in the pT3a tumors than in pT3b-c tumors.Among the ADC second-order textural differences,energy and entropy were significantly different between pT3a and pT3b-c rectal adenocarcinomas.For differentiating pT3a rectal adenocarcinomas from pT3b-c tumors,the areas under the curves(AUCs)of skewness,energy,and entropy were 0.686,0.657,and 0.747,respectively.Logistic regression analysis of all three features yielded a greater AUC(0.775)in differentiating pT3a rectal adenocarcinomas from pT3b-c tumors(69.0%sensitivity and 83.3%specificity).CONCLUSION TA features derived from ADC maps might potentially differentiate pT3a rectal adenocarcinomas from pT3b-c tumors.展开更多
BACKGROUND Artificial intelligence in radiology has the potential to assist with the diagnosis,prognostication and therapeutic response prediction of various cancers.A few studies have reported that texture analysis c...BACKGROUND Artificial intelligence in radiology has the potential to assist with the diagnosis,prognostication and therapeutic response prediction of various cancers.A few studies have reported that texture analysis can be helpful in predicting the response to chemotherapy for colorectal liver metastases,however,the results have varied.Necrotic metastases were not clearly excluded in these studies and in most studies the full range of texture analysis features were not evaluated.This study was designed to determine if the computed tomography(CT)texture analysis results of non-necrotic colorectal liver metastases differ from previous reports.A larger range of texture features were also evaluated to identify potential new biomarkers.AIM To identify potential new imaging biomarkers with CT texture analysis which can predict the response to first-line cytotoxic chemotherapy in non-necrotic colorectal liver metastases(CRLMs).METHODS Patients who presented with CRLMs from 2012 to 2020 were retrospectively selected on the institutional radiology information system of our private radiology practice.The inclusion criteria were non-necrotic CRLMs with a minimum size of 10 mm(diagnosed on archived 1.25 mm portal venous phase CT(FOLFOX,FOLFIRI,FOLFOXIRI,CAPE-OX,CAPE-IRI or capecitabine).The final study cohort consisted of 29 patients.The treatment response of the CRLMs was classified according to the RECIST 1.1 criteria.By means of CT texture analysis,various first and second order texture features were extracted from a single nonnecrotic target CRLM in each responding and non-responding patient.Associations between features and response to chemotherapy were assessed by logistic regression models.The prognostic accuracy of selected features was evaluated by using the area under the curve.RESULTS There were 15 responders(partial response)and 14 non-responders(7 stable and 7 with progressive disease).The responders presented with a higher number of CRLMs(P=0.05).In univariable analysis,eight texture features of the responding CRLMs were associated with treatment response,but due to strong correlations among some of the features,only two features,namely minimum histogram gradient intensity and long run low grey level emphasis,were included in the multiple analysis.The area under the receiver operating characteristic curve of the multiple model was 0.80(95%CI:0.64 to 0.96),with a sensitivity of 0.73(95%CI:0.48 to 0.89)and a specificity of 0.79(95%CI:0.52 to 0.92).CONCLUSION Eight first and second order texture features,but particularly minimum histogram gradient intensity and long run low grey level emphasis are significantly correlated with treatment response in non-necrotic CRLMs.展开更多
The present work focuses on the development of a novel computer-based approach for tear ferning(TF)featuring.The original TF images of the recently developedfive-point grading scale have been used to assign a grade fo...The present work focuses on the development of a novel computer-based approach for tear ferning(TF)featuring.The original TF images of the recently developedfive-point grading scale have been used to assign a grade for any TF image automatically.A vector characteristic(VC)representing each grade was built using the reference images.A weighted combination between features selected from textures analysis using gray level co-occurrence matrix(GLCM),power spectrum(PS)analysis and linear specificity of the image were used to build the VC of each grade.A total of 14 features from texture analysis were used.PS at di®erent frequency points and number of line segments in each image were also used.Five features from GLCM have shown significant di®erences between the recently developed grading scale images which are:angular second moment at 0and 45,contrast,and correlation at 0and 45;thesefive features were all included in the characteristic vector.Three specific power frequencies were used in the VC because of the discrimination power.Number of line segments was also chosen because of dissimilarities between images.A VC for each grade of TF reference images was constructed and was found to be significantly different from each other's.This is a basic and fundamental step toward an automatic grading for computer-based diagnosis for dry eye.展开更多
This research article proposes an automatic frame work for detectingCOVID -19 at the early stage using chest X-ray image. It is an undeniable factthat coronovirus is a serious disease but the early detection of the vi...This research article proposes an automatic frame work for detectingCOVID -19 at the early stage using chest X-ray image. It is an undeniable factthat coronovirus is a serious disease but the early detection of the virus presentin human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in needof right and even rich technology for its early detection. The proposed deeplearning model analysis the pixels of every image and adjudges the presence ofvirus. The classifier is designed in such a way so that, it automatically detectsthe virus present in lungs using chest image. This approach uses an imagetexture analysis technique called granulometric mathematical model. Selectedfeatures are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling(LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting majorlevel of image features. Moreover, the corona virus has been detected usinghigh resolution output. In the framework, atrous spatial pyramid pooling(ASPP) method is employed at its bottom level for incorporating the deepmulti scale features in to the discriminative mode. The architectural workingstarts from the selecting the features from the image using granulometricmathematical model and the selected features are optimized using LightRESASPP-Unet. ASPP in the analysis of images has performed better than theexisting Unet model. The proposed algorithm has achieved 99.6% of accuracyin detecting the virus at its early stage.展开更多
基金financially supported by the National Natural Science Foundation of China(No.41502046,41530211 and 41272079)
文摘1 Introduction The Wulong glomerophyric diorite porphyry has an extremely peculiar texture with plagioclase phenocrysts clustered as flower-like glomerocrysts(Figs.1a&b),which is never discovered elsewhere of the world.The
文摘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.
文摘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.
基金the Deanship of Scientifc Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/421/45supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2024/R/1446)+1 种基金supported by theResearchers Supporting Project Number(UM-DSR-IG-2023-07)Almaarefa University,Riyadh,Saudi Arabia.supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1F1A1055408).
文摘Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
基金Supported by the National Natural Science Foundation of China,No.81560278the Health Commission of Guangxi Zhuang Autonomous Region,No.Z20200953,No.G201903023,and No.Z-A20221157Scientific Research and Technology Development Project of Nanning,No.20213122.
文摘BACKGROUND Microvascular invasion(MVI)is a significant risk factor for recurrence and metastasis following hepatocellular carcinoma(HCC)surgery.Currently,there is a paucity of preoperative evaluation approaches for MVI.AIM To investigate the predictive value of texture features and radiological signs based on multiparametric magnetic resonance imaging in the non-invasive preoperative prediction of MVI in HCC.METHODS Clinical data from 97 HCC patients were retrospectively collected from January 2019 to July 2022 at our hospital.Patients were classified into two groups:MVI-positive(n=57)and MVI-negative(n=40),based on postoperative pathological results.The correlation between relevant radiological signs and MVI status was analyzed.MaZda4.6 software and the mutual information method were employed to identify the top 10 dominant texture features,which were combined with radiological signs to construct artificial neural network(ANN)models for MVI prediction.The predictive performance of the ANN models was evaluated using area under the curve,sensitivity,and specificity.ANN models with relatively high predictive performance were screened using the DeLong test,and the regression model of multilayer feedforward ANN with backpropagation and error backpropagation learning method was used to evaluate the models’stability.RESULTS The absence of a pseudocapsule,an incomplete pseudocapsule,and the presence of tumor blood vessels were identified as independent predictors of HCC MVI.The ANN model constructed using the dominant features of the combined group(pseudocapsule status+tumor blood vessels+arterial phase+venous phase)demonstrated the best predictive performance for MVI status and was found to be automated,highly operable,and very stable.CONCLUSION The ANN model constructed using the dominant features of the combined group can be recommended as a noninvasive method for preoperative prediction of HCC MVI status.
基金Project supported by the National Institute of Technology,Rourkela,India
文摘The chemical,physical,thermal and texture properties of iron ores from different regions of Odisha and Chhattisgarh regions,India,have been investigated to understand the compositional variations of Fe,Al2O3,SiO2,S and P.They were analyzed for its susceptibility to meet the industrial requirements,for various iron manufacture techniques.Chemical analysis indicated that the majority of the iron ores is rich in hematite(>90wt%),poor in gangue(<4.09wt%SiO2and<3.8wt%Al2O3)and deleterious elements(P<0.065wt%and S<0.016wt%)in all these iron ores found to be low.XRD peaks reviled that the gangue is in the form of kaolinite and quartz,and same was observed in Fourier transform infrared(FTIR)spectroscopy in the range of914to1034cm–1.The iron ores were found to have excellent physical properties exemplify with tumbler index(82wt%–91wt%),abrasion index(1.27wt%–4.87wt%)and shatter index(0.87wt%–1.64wt%).FTIR and thermal analysis were performed to assimilate the analysis interpolations.It was found that these iron ores exhibit three endothermic reactions,which are dehydration below447K with mass loss of0.13wt%to1.7wt%,dehydroxylation at525–609K with mass loss of1.09wt%–4.49wt%and decomposition of aluminosilicates at597–850K with mass loss of0.13wt%–1.15wt%.From this study,we can conclude that due to its excellent physico-chemical characteristics,these iron ores are suitable for BF and DRI operations.
文摘This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et al developed a robust predictive model demonstrating high accuracy(area under the curve 0.92 in the training cohort)by integrating venous phase radiomic features with alphafetoprotein levels.This noninvasive approach enables early identification of patients unlikely to benefit from transarterial chemoembolization,allowing a timely transition to alternative therapies such as targeted agents or immunotherapy.Such precision strategies may improve clinical outcomes,optimize resource utilization,and increase survival in advanced hepatocellular carcinoma management.Future studies should emphasize external validation and broader clinical adoption.
基金Supported by the National Natural Science Foundation of China,No.82000482China Postdoctoral Science Foundation funded,No.2023M741858China Crohn’s and Colitis Foundation,No.CCCF-QF-2023C18-3.
文摘BACKGROUND Early identification of bowel resection risks is crucial for patients with incarcerated inguinal hernia(IIH).However,the prompt detection of these risks remains a significant challenge.Advancements in radiomic feature extraction and machine learning algorithms have paved the way for innovative diagnostic approaches to assess IIH more effectively.AIM To devise a sophisticated radiomic-clinical model to evaluate bowel resection risks in IIH patients,thereby enhancing clinical decision-making processes.METHODS This single-center retrospective study analyzed 214 IIH patients randomized into training(n=161)and test(n=53)sets(3:1).Radiologists segmented hernia sac-trapped bowel volumes of interest(VOIs)on computed tomography images.Radiomic features extracted from VOIs generated Rad-scores,which were combined with clinical data to construct a nomogram.The nomogram’s performance was evaluated against standalone clinical and radiomic models in both cohorts.RESULTS A total of 1561 radiomic features were extracted from the VOIs.After dimensionality reduction,13 radiomic features were used with eight machine learning algorithms to develop the radiomic model.The logistic regression algorithm was ultimately selected for its effectiveness,showing an area under the curve(AUC)of 0.828[95%confidence interval(CI):0.753-0.902]in the training set and 0.791(95%CI:0.668-0.915)in the test set.The comprehensive nomogram,incorporating clinical indicators showcased strong predictive capabilities for assessing bowel resection risks in IIH patients,with AUCs of 0.864(95%CI:0.800-0.929)and 0.800(95%CI:0.669-0.931)for the training and test sets,respectively.Decision curve analysis revealed the integrated model’s superior performance over standalone clinical and radiomic approaches.CONCLUSION This innovative radiomic-clinical nomogram has proven to be effective in predicting bowel resection risks in IIH patients and has substantially aided clinical decision-making.
文摘The present study deals with the textural characteristics of the sediments from the exposed palaeochannel situated at Dhyangla village near Ladwa, district of Kurukshetra in north-western Haryana. It is a part of vast alluvial plains of India. Samples were taken from a 3 m thick exposed section. Grain size distribution and Palynofacies analyses of these sediments were carried out to study their textural parameters and the organic matter content respectively. Sediments are mostly fine to medium grained sand with silt percentage increasing upwards in the section. These sediments are mostly unimodal showing the grain size population controlled by a single type of grain size, mostly sand. Further the sand samples are moderately well sorted and mesokurtic in nature. Samples with silt are poorly sorted and leptokurtic with positive skewness, denoting those sediments have already sorted elsewhere in the high energy environment and are now transported and modified by the low energy environment. Palynofacies analysis also shows the presence of amorphous particles in the silt samples which indicate low energy environment of the sequence while presence of black debris in the sand samples indicates high energy depositions environment.
文摘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.
基金Funded by the National Public Welfare Basic Scientific Research Program of Chinese Academy of Medical Sciences(2018PT32003).
文摘Objective Texture analysis is deemed to reflect intratumor heterogeneity invisible to the naked eyes.The aim of this study was to evaluate the feasibility of assessing the KRAS mutational status in colorectal cancer(CRC)patients using CT texture analysis.Methods This retrospective study included 92 patients who had histopathologically confirmed CRC and underwent preoperative contrast-enhanced CT examinations.The patients were assigned into a training cohort(n=51)and a validation cohort(n=41).We placed the region of interest in the tumour regions on the selected axial images using software of lexRad to extract a series of quantitative parameters based on the spatial scaling factors(SSFs),including mean,standard deviation(SD),entropy,mean of positive pixels(MPP),skewness,and kurtosis.The texture parameters and clinical characteristics(age,gender,tumour location,histopathology;tumour size,1 N,M stages)were compared between the mutated and wild-type KRAS patient groups in training cohort and validation cohort.Before building the multiple feature classifier,we calculated the correlations of the features using Pearsons correlation coefficient,and if any two features were significantly correlated,the one with lower AUC was removed.Ultimately,only the most discriminative isolated features were combined to train a supporting vector machine(SVM)classifier.The receiver operating characteristic(ROC)curve was processed for evaluating the diagnostic efficiency of texture parameters in differentiating CRC patients with mutated KRAS from those with wild-type KRAS.Results None of the clinical characteristics were significant different between CRC patients with wild-type KRAS and mutated KRAS in both cohorts.For predicting the expression of mutated KRAS in CRC patients,the perfect model which combined skewness on SSF 5 by unenhanced CT,entropy on SSF 2,skewness and kurtosis on SSF 0,and kurtosis and mean on SSF 3 by enhanced CT,showed a desirable AUC of 0.951(95%CI:0.895-1,P<0.001),with a sensitivity of 88.9%and a specificity of 91.7%,when the cut-off value was 0.46 in the training cohort;while in the validation cohort,the AUC value was 0.995(95%CI:0.982-1,P<0.001),the sensitivity was 100%,and the specificity was 93.7%when the cut-off value was 0.28.Conclusion It is feasible to evaluate the KRAS mutational status in CRC using CT texture analysis.
基金the National Key Research and Development Program of China(2019YFC0118100)the National Natural Science Foundation of China(81671760,81873910 and 62171167)the Natural Science Foundation of Shang-hai(19ZR1457800).
文摘Background:Radiofrequency ablation(RFA)is one of the effective therapeutic modalities in patients with hepatocellular carcinoma(HCC).However,there is no proper method to evaluate the HCC response to RFA.This study aimed to establish and validate a clinical prediction model based on dual-energy com-puted tomography(DECT)quantitative-imaging parameters,clinical variables,and CT texture parameters.Methods:We enrolled 63 patients with small HCC.Two to four weeks after RFA,we performed DECT scanning to obtain DECT-quantitative parameters and to record the patients’clinical baseline variables.DECT images were manually segmented,and 56 CT texture features were extracted.We used LASSO al-gorithm for feature selection and data dimensionality reduction;logistic regression analysis was used to build a clinical model with clinical variables and DECT-quantitative parameters;we then added texture features to build a clinical-texture model based on clinical model.Results:A total of six optimal CT texture analysis(CTTA)features were selected,which were statis-tically different between patients with or without tumor progression(P<0.05).When clinical vari-ables and DECT-quantitative parameters were included,the clinical models showed that albumin-bilirubin grade(ALBI)[odds ratio(OR)=2.77,95%confidence interval(CI):1.35-6.65,P=0.010],λAP(40-100 keV)(OR=3.21,95%CI:3.16-5.65,P=0.045)and IC AP(OR=1.25,95%CI:1.01-1.62,P=0.028)were asso-ciated with tumor progression,while the clinical-texture models showed that ALBI(OR=2.40,95%CI:1.19-5.68,P=0.024),λAP(40-100 keV)(OR=1.43,95%CI:1.10-2.07,P=0.019),and CTTA-score(OR=2.98,95%CI:1.68-6.66,P=0.001)were independent risk factors for tumor progression.The clinical model,clinical-texture model,and CTTA-score all performed well in predicting tumor progression within 12 months after RFA(AUC=0.917,0.962,and 0.906,respectively),and the C-indexes of the clinical and clinical-texture models were 0.917 and 0.957,respectively.Conclusions:DECT-quantitative parameters,CTTA,and clinical variables were helpful in predicting HCC progression after RFA.The constructed clinical prediction model can provide early warning of potential tumor progression risk for patients after RFA.
基金the National Natural Science Foundation of China(81900594)Clinical Research Plan of Shanghai Hospital Development Center(Grant 16CR3109B)Shanghai Youth Sci&Tech Talent Jump starting Program(18YF1412700).
文摘Background:Liver fibrosis is a common pathological change caused by a variety of etiologies.Early diagnosis and timely treatment can reverse or delay disease progression and improve the prognosis.This study aimed to assess the potential utility of two-dimensional shear wave elastography and texture analysis in dynamic monitoring of the progression of liver fibrosis in rat model.Methods:Twenty rats were divided into control group(n=4)and experimental groups(n=4 per group)with carbon tetrachloride administration for 2,3,4,and 6 weeks.The liver stiffness measurement was performed by two-dimensional shear wave elastography,while the optimal texture analysis subsets to distinguish fibrosis stage were generated by MaZda.The results of elastography and texture analysis were validated through comparing with histopathology.Results:Liver stiffness measurement was 6.09±0.31 kPa in the control group and 7.10±0.41 kPa,7.80±0.93 kPa,8.64±0.93 kPa,9.91±1.13 kPa in the carbon tetrachloride induced groups for 2,3,4,6 weeks,respectively(P<0.05).By texture analysis,histogram and co-occurrence matrix had the most frequency texture parameters in staging liver fibrosis.Receiver operating characteristic curve of liver elasticity showed that the sensitivity and specificity were 95.0%and 92.5%to discriminate liver fibrosis and non-fibrosis,respectively.In texture analysis,five optimal parameters were selected to classify liver fibrosis and non-fibrosis.Conclusions:Two-dimensional shear wave elastography showed potential applications for noninvasive monitoring of the progression of hepatic fibrosis,even in mild fibrosis.Texture analysis can further extract and quantify the texture features in ultrasonic image,which was a supplementary to further visual information and acquired high diagnostic accuracy for severe fibrosis.
基金This study was supported by grants from the Administration of Traditional Chinese Medicine of Jiangsu Province(No.ZD201907),the National Natural Science Foundation of China(No.82171925 and No.81771899)。
文摘Objective:The objective of this study was to investigate the application of unenhanced computed tomography(CT)texture analysis in differentiating pancreatic adenosquamous carcinoma(PASC)from pancreatic ductal adenocarcinoma(PDAC).Methods:Preoperative CT images of 112 patients(31 with PASC,81 with PDAC)were retrospectively reviewed.A total of 396 texture parameters were extracted from AnalysisKit software for further texture analysis.Texture features were selected for the differentiation of PASC and PDAC by the Mann-Whitney U test,univariate logistic regression analysis,and the minimum redundancy maximum relevance algorithm.Furthermore,receiver operating characteristic(ROC)curve analysis was performed to evaluate the diagnostic performance of the texture feature-based model by the random forest(RF)method.Finally,the robustness and reproducibility of the predictive model were assessed by the 10-times leave-group-out cross-validation(LGOCV)method.
基金University-Industry-Science Partnership Project of Guangdong Province and Ministry of Education,China(No.2012B091000155)Strategic Emerging Industries Project of Guangdong Province(No.2011912027)
文摘Computer forensics and identification for traditional Chinese painting arts have caught the attention of various fields. Rice paper＇s feature extraction and analysis methods are of high significance for the rice paper is an important carrier of traditional Chinese painting arts. In this paper, rice paper＇s morphological feature analysis is done using multi spectral imaging technology. The multispectral imaging system is utilized to acquire rice paper＇s spectral images in different wave- length channels, and then those spectral images are measured using texture parameter statistics to acquire sensitive bands for rice paper＇s feature. The mathematical morphology and grayscale statistical principle are utilized to establish a rice paper＇s morphological feature analytical model which is used to acquire rice paper＇ s one-dimensional vector. For the purpose of eval- uating these feature vectors＇ accuracy, they are entered into the support vector machine（SVM） classifier for detection and classification. The results show that the rice paper＇s feature is out loud in the spectral band 550 nm, and the average classifi- cation accuracy of feature vectors output from the analytical model is 96 %. The results indicate that the rice paper＇s feature analytical model can extract most of rice paper＇s features with accuracy and efficiency.
文摘This paper deals with an optimization design method for the Gabor filters based on the analysis of an iris texture model. By means of analyzing the properties of an iris texture image, the energy distribution regularity of the iris texture image measured by the average power spectrum density is exploited, and the theoretical ranges of the efficient valued frequency and orientation parameters can also be deduced. The analysis shows that the energy distribution of the iris texture is generally centralized around lower frequencies in the spatial frequency domain. Accordingly, an iterative algorithm is designed to optimize the Gabor parameter field. The experimental results indicate the validity of the theory and efficiency of the algorithm.
基金Jiangsu Provincial Medical Youth Talent,No.QNRC2016212Suzhou Clinical Special Disease Diagnosis and Treatment Program,No.LCZX201823+2 种基金Suzhou GuSu Medical Talent Project,No.GSWS2019077The Science and Technology Bureau of Changshu,No.CS201624(to Lu ZH)Jiangsu Committee of Health,No.H2018071(to Xia KJ).
文摘BACKGROUND The accuracy of discriminating pT3a from pT3b-c rectal cancer using highresolution magnetic resonance imaging(MRI)remains unsatisfactory,although texture analysis(TA)could improve such discrimination.AIM To investigate the value of TA on apparent diffusion coefficient(ADC)maps in differentiating pT3a rectal adenocarcinomas from pT3b-c tumors.METHODS This was a case-control study of 59 patients with pT3 rectal adenocarcinoma,who underwent diffusion-weighted imaging(DWI)between October 2016 and December 2018.The inclusion criteria were:(1)Proven pT3 rectal adenocarcinoma;(2)Primary MRI including high-resolution T2-weighted image(T2WI)and DWI;and(3)Availability of pathological reports for surgical specimens.The exclusion criteria were:(1)Poor image quality;(2)Preoperative chemoradiation therapy;and(3)A different pathological type.First-order(ADC values,skewness,kurtosis,and uniformity)and second-order(energy,entropy,inertia,and correlation)texture features were derived from whole-lesion ADC maps.Receiver operating characteristic curves were used to determine the diagnostic value for pT3b-c tumors.RESULTS The final study population consisted of 59 patients(34 men and 25 women),with a median age of 66 years(range,41-85 years).Thirty patients had pT3a,24 had pT3b,and five had pT3c.Among the ADC first-order textural differences between pT3a and pT3b-c rectal adenocarcinomas,only skewness was significantly lower in the pT3a tumors than in pT3b-c tumors.Among the ADC second-order textural differences,energy and entropy were significantly different between pT3a and pT3b-c rectal adenocarcinomas.For differentiating pT3a rectal adenocarcinomas from pT3b-c tumors,the areas under the curves(AUCs)of skewness,energy,and entropy were 0.686,0.657,and 0.747,respectively.Logistic regression analysis of all three features yielded a greater AUC(0.775)in differentiating pT3a rectal adenocarcinomas from pT3b-c tumors(69.0%sensitivity and 83.3%specificity).CONCLUSION TA features derived from ADC maps might potentially differentiate pT3a rectal adenocarcinomas from pT3b-c tumors.
文摘BACKGROUND Artificial intelligence in radiology has the potential to assist with the diagnosis,prognostication and therapeutic response prediction of various cancers.A few studies have reported that texture analysis can be helpful in predicting the response to chemotherapy for colorectal liver metastases,however,the results have varied.Necrotic metastases were not clearly excluded in these studies and in most studies the full range of texture analysis features were not evaluated.This study was designed to determine if the computed tomography(CT)texture analysis results of non-necrotic colorectal liver metastases differ from previous reports.A larger range of texture features were also evaluated to identify potential new biomarkers.AIM To identify potential new imaging biomarkers with CT texture analysis which can predict the response to first-line cytotoxic chemotherapy in non-necrotic colorectal liver metastases(CRLMs).METHODS Patients who presented with CRLMs from 2012 to 2020 were retrospectively selected on the institutional radiology information system of our private radiology practice.The inclusion criteria were non-necrotic CRLMs with a minimum size of 10 mm(diagnosed on archived 1.25 mm portal venous phase CT(FOLFOX,FOLFIRI,FOLFOXIRI,CAPE-OX,CAPE-IRI or capecitabine).The final study cohort consisted of 29 patients.The treatment response of the CRLMs was classified according to the RECIST 1.1 criteria.By means of CT texture analysis,various first and second order texture features were extracted from a single nonnecrotic target CRLM in each responding and non-responding patient.Associations between features and response to chemotherapy were assessed by logistic regression models.The prognostic accuracy of selected features was evaluated by using the area under the curve.RESULTS There were 15 responders(partial response)and 14 non-responders(7 stable and 7 with progressive disease).The responders presented with a higher number of CRLMs(P=0.05).In univariable analysis,eight texture features of the responding CRLMs were associated with treatment response,but due to strong correlations among some of the features,only two features,namely minimum histogram gradient intensity and long run low grey level emphasis,were included in the multiple analysis.The area under the receiver operating characteristic curve of the multiple model was 0.80(95%CI:0.64 to 0.96),with a sensitivity of 0.73(95%CI:0.48 to 0.89)and a specificity of 0.79(95%CI:0.52 to 0.92).CONCLUSION Eight first and second order texture features,but particularly minimum histogram gradient intensity and long run low grey level emphasis are significantly correlated with treatment response in non-necrotic CRLMs.
文摘The present work focuses on the development of a novel computer-based approach for tear ferning(TF)featuring.The original TF images of the recently developedfive-point grading scale have been used to assign a grade for any TF image automatically.A vector characteristic(VC)representing each grade was built using the reference images.A weighted combination between features selected from textures analysis using gray level co-occurrence matrix(GLCM),power spectrum(PS)analysis and linear specificity of the image were used to build the VC of each grade.A total of 14 features from texture analysis were used.PS at di®erent frequency points and number of line segments in each image were also used.Five features from GLCM have shown significant di®erences between the recently developed grading scale images which are:angular second moment at 0and 45,contrast,and correlation at 0and 45;thesefive features were all included in the characteristic vector.Three specific power frequencies were used in the VC because of the discrimination power.Number of line segments was also chosen because of dissimilarities between images.A VC for each grade of TF reference images was constructed and was found to be significantly different from each other's.This is a basic and fundamental step toward an automatic grading for computer-based diagnosis for dry eye.
文摘This research article proposes an automatic frame work for detectingCOVID -19 at the early stage using chest X-ray image. It is an undeniable factthat coronovirus is a serious disease but the early detection of the virus presentin human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in needof right and even rich technology for its early detection. The proposed deeplearning model analysis the pixels of every image and adjudges the presence ofvirus. The classifier is designed in such a way so that, it automatically detectsthe virus present in lungs using chest image. This approach uses an imagetexture analysis technique called granulometric mathematical model. Selectedfeatures are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling(LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting majorlevel of image features. Moreover, the corona virus has been detected usinghigh resolution output. In the framework, atrous spatial pyramid pooling(ASPP) method is employed at its bottom level for incorporating the deepmulti scale features in to the discriminative mode. The architectural workingstarts from the selecting the features from the image using granulometricmathematical model and the selected features are optimized using LightRESASPP-Unet. ASPP in the analysis of images has performed better than theexisting Unet model. The proposed algorithm has achieved 99.6% of accuracyin detecting the virus at its early stage.
