The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist...The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.展开更多
[Objective] To extract desertification information of Hulun Buir region based on MODIS image data. [Method] Based on MODIS image data with the spatial res- olution of 1 km, 5 indicators which could reflect different d...[Objective] To extract desertification information of Hulun Buir region based on MODIS image data. [Method] Based on MODIS image data with the spatial res- olution of 1 km, 5 indicators which could reflect different desertification features were selected to conduct inversion. The desertification information of Hulun Buir region was extracted by decision tree classification. [Result] The desertification area of Hu- lun Buir region is 33 862 km2, accounting for 24% of the total area, and it is mainly dominated by sandiness desertification. Though field verification and mining point validation of high-resolution interpretation data, the overall accuracy of this evaluation is above 89%. [Conclusion] Evaluation method used in this study is not only effectively for large scale regional desertification monitoring but also has a better evaluation performance.展开更多
Automatic defect detection in X-ray images is currently a focus of much research at home and abroad. The technology requires computerized image processing, image analysis, and pattern recognition. This paper describes...Automatic defect detection in X-ray images is currently a focus of much research at home and abroad. The technology requires computerized image processing, image analysis, and pattern recognition. This paper describes an image processing method for automatic defect detection using image data fusion which synthesizes several methods including edge extraction, wave profile analyses, segmentation with dynamic threshold, and weld district extraction. Test results show that defects that induce an abrupt change over a predefined extent of the image intensity can be segmented regardless of the number, location, shape or size. Thus, the method is more robust and practical than the current methods using only one method.展开更多
Full-parallax light-field is captured by a small-scale 3D image scanning system and applied to holographic display. A vertical camera array is scanned horizontally to capture full-parallax imagery, and the vertical vi...Full-parallax light-field is captured by a small-scale 3D image scanning system and applied to holographic display. A vertical camera array is scanned horizontally to capture full-parallax imagery, and the vertical views between cameras are interpolated by depth image-based rendering technique. An improved technique for depth estimation reduces the estimation error and high-density light-field is obtained. The captured data is employed for the calculation of computer hologram using ray-sampling plane. This technique enables high-resolution display even in deep 3D scene although a hologram is calculated from ray information, and thus it makes use of the important advantage of holographic 3D display.展开更多
Nowadays since the Internet is ubiquitous,the frequency of data transfer through the public network is increasing.Hiding secure data in these transmitted data has emerged broad security issue,such as authentication an...Nowadays since the Internet is ubiquitous,the frequency of data transfer through the public network is increasing.Hiding secure data in these transmitted data has emerged broad security issue,such as authentication and copyright protection.On the other hand,considering the transmission efficiency issue,image transmission usually involves image compression in Internet-based applications.To address both issues,this paper presents a data hiding scheme for the image compression method called absolute moment block truncation coding(AMBTC).First,an image is divided into nonoverlapping blocks through AMBTC compression,the blocks are classified four types,namely smooth,semi-smooth,semi-complex,and complex.The secret data are embedded into the smooth blocks by using a simple replacement strategy.The proposed method respectively embeds nine bits(and five bits)of secret data into the bitmap of the semi-smooth blocks(and semicomplex blocks)through the exclusive-or(XOR)operation.The secret data are embedded into the complex blocks by using a hidden function.After the embedding phase,the direct binary search(DBS)method is performed to improve the image qualitywithout damaging the secret data.The experimental results demonstrate that the proposed method yields higher quality and hiding capacity than other reference methods.展开更多
A recent trend in computer graphics and image processing is to use Iterated Function System (IFS) to generate and describe both man-made graphics and natural images. Jacquin was the first to propose a fully automatic ...A recent trend in computer graphics and image processing is to use Iterated Function System (IFS) to generate and describe both man-made graphics and natural images. Jacquin was the first to propose a fully automatic gray scale image compression algorithm which is referred to as a typical static fractal transform based algorithm in this paper. By using this algorithm, an image can be condensely described as a fractal transform operator which is the combination of a set of fractal mappings. When the fractal transform operator is iteratedly applied to any initial image, a unique attractor (reconstructed image) can be achieved. In this paper) a dynamic fractal transform is presented which is a modification of the static transform. Instead of being fixed, the dynamic transform operator varies in each decoder iteration, thus differs from static transform operators. The new transform has advantages in improving coding efficiency and shows better convergence for the decoder.展开更多
In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the f...In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the first two were invented by other persons and the third one, by ourselves. As a result, the comparison among their compression rates is. given at the end of this paper. Further application of these image compression technique to satellite data and other meteorological data looks promising.展开更多
Remotely sensed spectral data and images are acquired under significant additional effects accompanying their major formation process, which greatly determine measurement accuracy. In order to be used in subsequent qu...Remotely sensed spectral data and images are acquired under significant additional effects accompanying their major formation process, which greatly determine measurement accuracy. In order to be used in subsequent quantitative analysis and assessment, this data should be subject to preliminary processing aiming to improve its accuracy and credibility. The paper considers some major problems related with preliminary processing of remotely sensed spectral data and images. The major factors are analyzed, which affect the occurrence of data noise or uncertainties and the methods for reduction or removal thereof. Assessment is made of the extent to which available equipment and technologies may help reduce measurement errors.展开更多
The reverse design of solid rocket motor(SRM)propellant grain involves determining the grain geometry to closely match a predefined internal ballistic curve.While existing reverse design methods are feasible,they ofte...The reverse design of solid rocket motor(SRM)propellant grain involves determining the grain geometry to closely match a predefined internal ballistic curve.While existing reverse design methods are feasible,they often face challenges such as lengthy computation times and limited accuracy.To achieve rapid and accurate matching between the targeted ballistic curve and complex grain shape,this paper proposes a novel reverse design method for SRM propellant grain based on time-series data imaging and convolutional neural network(CNN).First,a finocyl grain shape-internal ballistic curve dataset is created using parametric modeling techniques to comprehensively cover the design space.Next,the internal ballistic time-series data is encoded into three-channel images,establishing a potential relationship between the ballistic curves and their image representations.A CNN is then constructed and trained using these encoded images.Once trained,the model enables efficient inference of propellant grain dimensions from a target internal ballistic curve.This paper conducts comparative experiments across various neural network models,validating the effectiveness of the feature extraction method that transforms internal ballistic time-series data into images,as well as its generalization capability across different CNN architectures.Ignition tests were performed based on the predicted propellant grain.The results demonstrate that the relative error between the experimental internal ballistic curves and the target curves is less than 5%,confirming the validity and feasibility of the proposed reverse design methodology.展开更多
Objective This study aimed to assess the local staging of bladder tumors in patients utilizing preoperative multiparametric MRI(mpMRI)and to demonstrate the clinical efficacy of this method through a comparative analy...Objective This study aimed to assess the local staging of bladder tumors in patients utilizing preoperative multiparametric MRI(mpMRI)and to demonstrate the clinical efficacy of this method through a comparative analysis with corresponding histopathological findings.Methods Between November 2020 and April 2022,63 patients with a planned cystoscopy and a preliminary or previous diagnosis of bladder tumor were included.All participants underwent mpMRI,and Vesical Imaging Reporting and Data System(VI-RADS)criteria were applied to assess the recorded images.Subsequently,obtained biopsies were histopathologically examined and compared with radiological findings.Results Of the 63 participants,60 were male,and three were female.Categorizing tumors with a VI-RADS score of>3 as muscle invasive,84%were radiologically classified as having an invasive bladder tumor.However,histopathological results indicated invasive bladder tumors in 52%of cases.Sensitivity of the VI-RADS score was 100%;specificity was 23%;the negative predictive value was 100%;and the positive predictive value was 62%.Conclusion The scoring system obtained through mpMRI,VI-RADS,proves to be a successful method,particularly in determining the absence of muscle invasion in bladder cancer.Its efficacy in detecting muscle invasion in bladder tumors could be further enhanced with additional studies,suggesting potential for increased diagnostic efficiency through ongoing research.The VI-RADS could enhance the selection of patients eligible for accurate diagnosis and treatment.展开更多
Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts...Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts of climate change.Remote sensing has become a vital tool for snow monitoring,with the widely used Moderate-resolution Imaging Spectroradiometer(MODIS)snow products from the Terra and Aqua satellites.However,cloud cover often interferes with snow detection,making cloud removal techniques crucial for reliable snow product generation.This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms.Using real-time field camera observations from four stations in the Tianshan Mountains,China,this study assessed the performance of these datasets during three distinct snow periods:the snow accumulation period(September-November),snowmelt period(March-June),and stable snow period(December-February in the following year).The findings showed that cloud-free snow products generated using the Hidden Markov Random Field(HMRF)algorithm consistently outperformed the others,particularly under cloud cover,while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction(STAR)demonstrated varying performance depending on terrain complexity and cloud conditions.This study highlighted the importance of considering terrain features,land cover types,and snow dynamics when selecting cloud removal methods,particularly in areas with rapid snow accumulation and melting.The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning,multi-source data fusion,and advanced remote sensing technologies.By expanding validation efforts and refining cloud removal strategies,more accurate and reliable snow products can be developed,contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.展开更多
China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this pap...China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this paper,by selecting moderateresolution imaging spectroradiometer(MODIS)data as the main information source,on the basis of spectral and biological characteristics mechanism of the crop,and using the freely available advantage of hyperspectral temporal MODIS data,conduct large scale agricultural remote sensing monitoring research,develop applicable model and algorithm,which can achieve large scale remote sensing extraction and yield estimation of major crop type information,and improve the accuracy of crop quantitative remote sensing.Moreover,the present situation of global crop remote sensing monitoring based on MODIS data is analyzed.Meanwhile,the climate and environment grid agriculture information system using large-scale agricultural condition remote sensing monitoring has been attempted preliminary.展开更多
Multispectral time delay and integration charge coupled device （TDICCD） image compression requires a low- complexity encoder because it is usually completed on board where the energy and memory are limited. The Cons...Multispectral time delay and integration charge coupled device （TDICCD） image compression requires a low- complexity encoder because it is usually completed on board where the energy and memory are limited. The Consultative Committee for Space Data Systems （CCSDS） has proposed an image data compression （CCSDS-IDC） algorithm which is so far most widely implemented in hardware. However, it cannot reduce spectral redundancy in mukispectral images. In this paper, we propose a low-complexity improved CCSDS-IDC （ICCSDS-IDC）-based distributed source coding （DSC） scheme for multispectral TDICCD image consisting of a few bands. Our scheme is based on an ICCSDS-IDC approach that uses a bit plane extractor to parse the differences in the original image and its wavelet transformed coefficient. The output of bit plane extractor will be encoded by a first order entropy coder. Low-density parity-check-based Slepian-Wolf （SW） coder is adopted to implement the DSC strategy. Experimental results on space multispectral TDICCD images show that the proposed scheme significantly outperforms the CCSDS-IDC-based coder in each band.展开更多
In the area of pattern recognition and machine learning,features play a key role in prediction.The famous applications of features are medical imaging,image classification,and name a few more.With the exponential grow...In the area of pattern recognition and machine learning,features play a key role in prediction.The famous applications of features are medical imaging,image classification,and name a few more.With the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of data.These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality.On the other hand,this growth also made it difficult to comprehend and utilize data for various purposes.The results of imaging data can become biased because of extraneous features present in larger datasets.Feature selection gives a chance to decrease the number of components in such large datasets.Through selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization precision.The correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast control.This paper presents a review of feature selection techniques and attributes selection measures for medical imaging.This review is meant to describe feature selection techniques in a medical domainwith their pros and cons and to signify its application in imaging data and data mining algorithms.The review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced data.Moreover,this review provides the importance of feature selection for correct classification of medical infections.In the end,critical analysis and future directions are provided.展开更多
Prostate cancer （PCa） is one of the most common cancers among men globally. The authors aimed to evaluate the ability of the Prostate Imaging Reporting and Data System version 2 （PI-RADS v2） to classify men with P...Prostate cancer （PCa） is one of the most common cancers among men globally. The authors aimed to evaluate the ability of the Prostate Imaging Reporting and Data System version 2 （PI-RADS v2） to classify men with PCa, clinically significant PCa （CSPCa）, or no PCa, especially among those with serum total prostate-specific antigen （tPSA） levels in the ＂gray zone＂ （4-10 ng ml-1）. A total of 308 patients （355 lesions） were enrolled in this study. Diagnostic efficiency was determined. Univariate and multivariate analyses, receiver operating characteristic curve analysis, and decision curve analysis were performed to determine and compare the predictors of PCa and CSPCa. The results suggested that PI-RADS v2, tPSA, and prostate-specific antigen density （PSAD） were independent predictors of PCa and CSPCa. A PI-RADS v2 score L≥4 provided high negative predictive values （91.39% for PCa and 95.69% for CSPCa）. A model of PI-RADS combined with PSA and PSAD helped to define a high-risk group （PI-RADS score = 5 and PSAD L≥0 15 ng ml-1 cm-3, with tPSA in the gray zone, or PI-RADS score L≥4 with high tPSA level） with a detection rate of 96.1% for PCa and 93.0% for CSPCa while a low-risk group with a detection rate of 6.1% for PCa and 2.2% for CSPCa. It was concluded that the PI-RADS v2 could be used as a reliable and independent predictor of PCa and CSPCa. The combination of PI-RADS v2 score with PSA and PSAD could be helpful in the prediction and diagnosis of PCa and CSPCa and, thus, may help in preventing unnecessary invasive procedures.展开更多
BACKGROUND Contrast-enhanced ultrasound(CEUS)is considered a secondary examination compared to computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of hepatocellular carcinoma(HCC),due to the ris...BACKGROUND Contrast-enhanced ultrasound(CEUS)is considered a secondary examination compared to computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of hepatocellular carcinoma(HCC),due to the risk of misdiagnosing intrahepatic cholangiocarcinoma(ICC).The introduction of CEUS Liver Imaging Reporting and Data System(CEUS LI-RADS)might overcome this limitation.Even though data from the literature seems promising,its reliability in real-life context has not been well-established yet.AIM To test the accuracy of CEUS LI-RADS for correctly diagnosing HCC and ICC in cirrhosis.METHODS CEUS LI-RADS class was retrospectively assigned to 511 nodules identified in 269 patients suffering from liver cirrhosis.The diagnostic standard for all nodules was either biopsy(102 nodules)or CT/MRI(409 nodules).Common diagnostic accuracy indexes such as sensitivity,specificity,positive predictive value(PPV),and negative predictive value(NPV)were assessed for the following associations:CEUS LR-5 and HCC;CEUS LR-4 and 5 merged class and HCC;CEUS LR-M and ICC;and CEUS LR-3 and malignancy.The frequency of malignant lesions in CEUS LR-3 subgroups with different CEUS patterns was also determined.Inter-rater agreement for CEUS LI-RADS class assignment and for major CEUS pattern identification was evaluated.RESULTS CEUS LR-5 predicted HCC with a 67.6%sensitivity,97.7%specificity,and 99.3%PPV(P<0.001).The merging of LR-4 and 5 offered an improved 93.9%sensitivity in HCC diagnosis with a 94.3%specificity and 98.8%PPV(P<0.001).CEUS LR-M predicted ICC with a 91.3%sensitivity,96.7%specificity,and 99.6%NPV(P<0.001).CEUS LR-3 predominantly included benign lesions(only 28.8%of malignancies).In this class,the hypo-hypo pattern showed a much higher rate of malignant lesions(73.3%)than the iso-iso pattern(2.6%).Inter-rater agreement between internal raters for CEUS-LR class assignment was almost perfect(n=511,k=0.94,P<0.001),while the agreement among raters from separate centres was substantial(n=50,k=0.67,P<0.001).Agreement was stronger for arterial phase hyperenhancement(internal k=0.86,P<2.7×10-214;external k=0.8,P<0.001)than washout(internal k=0.79,P<1.6×10-202;external k=0.71,P<0.001).CONCLUSION CEUS LI-RADS is effective but can be improved by merging LR-4 and 5 to diagnose HCC and by splitting LR-3 into two subgroups to differentiate iso-iso nodules from other patterns.展开更多
The impact of assimilating radiance data from the advanced satellite sensor GMI(GPM microwave imager)for typhoon analyses and forecasts was investigated using both a three-dimensional variational(3DVAR)and a hybrid en...The impact of assimilating radiance data from the advanced satellite sensor GMI(GPM microwave imager)for typhoon analyses and forecasts was investigated using both a three-dimensional variational(3DVAR)and a hybrid ensemble-3DVAR method.The interface of assimilating the radiance for the sensor GMI was established in the Weather Research and Forecasting(WRF)model.The GMI radiance data are assimilated for Typhoon Matmo(2014),Typhoon Chan-hom(2015),Typhoon Meranti(2016),and Typhoon Mangkhut(2018)in the Pacific before their landing.The results show that after assimilating the GMI radiance data under clear sky condition with the 3DVAR method,the wind,temperature,and humidity fields are effectively adjusted,leading to improved forecast skills of the typhoon track with GMI radiance assimilation.The hybrid DA method is able to further adjust the location of the typhoon systematically.The improvement of the track forecast is even more obvious for later forecast periods.In addition,water vapor and hydrometeors are enhanced to some extent,especially with the hybrid method.展开更多
Hepatocellular carcinoma(HCC)is the sixth most common cancer.The main risk factors associated with HCC development include hepatitis B virus,hepatitis C virus,alcohol consumption,aflatoxin B1,and nonalcoholic fatty li...Hepatocellular carcinoma(HCC)is the sixth most common cancer.The main risk factors associated with HCC development include hepatitis B virus,hepatitis C virus,alcohol consumption,aflatoxin B1,and nonalcoholic fatty liver disease.However,hepatocarcinogenesis is a complex multistep process.Various factors lead to hepatocyte malignant transformation and HCC development.Diagnosis and surveillance of HCC can be made with the use of liver ultrasound(US)every 6 mo.However,the sensitivity of this imaging method to detect HCC in a cirrhotic liver is limited,due to the abnormal liver parenchyma.Computed tomography(CT)and magnetic resonance imaging(MRI)are considered to be most useful tools for at-risk patients or patients with inadequate US.Liver biopsy is still used for diagnosis and prognosis of HCC in specific nodules that cannot be definitely characterized as HCC by imaging.Recently the American College of Radiology designed the Liver Imaging Reporting and Data System(LI-RADS),which is a comprehensive system for standardized interpretation of CT and MRI liver examinations that was first proposed in 2011.In 2018,it was integrated into the American Association for the Study of Liver Diseases guidance statement for HCC.LI-RADS is designed to ensure high sensitivity,precise categorization,and high positive predictive value for the diagnosis of HCC and is applied to“highrisk populations”according to specific criteria.Most importantly LI-RADS criteria achieved international collaboration and consensus among liver experts around the world on the best practices for caring for patients with or at risk for HCC.展开更多
BACKGROUND Hepatocellular carcinoma is the most common primary liver malignancy.From the results of previous studies,Liver Imaging Reporting and Data System(LIRADS)on contrast-enhanced ultrasound(CEUS)has shown satisf...BACKGROUND Hepatocellular carcinoma is the most common primary liver malignancy.From the results of previous studies,Liver Imaging Reporting and Data System(LIRADS)on contrast-enhanced ultrasound(CEUS)has shown satisfactory diagnostic value.However,a unified conclusion on the interobserver stability of this innovative ultrasound imaging has not been determined.The present metaanalysis examined the interobserver agreement of CEUS LI-RADS to provide some reference for subsequent related research.AIM To evaluate the interobserver agreement of LI-RADS on CEUS and analyze the sources of heterogeneity between studies.METHODS Relevant papers on the subject of interobserver agreement on CEUS LI-RADS published before March 1,2020 in China and other countries were analyzed.The studies were filtered,and the diagnostic criteria were evaluated.The selected references were analyzed using the“meta”and“metafor”packages of R software version 3.6.2.RESULTS Eight studies were ultimately included in the present analysis.Meta-analysis results revealed that the summary Kappa value of included studies was 0.76[95%confidence interval,0.67-0.83],which shows substantial agreement.Higgins I2 statistics also confirmed the substantial heterogeneity(I2=91.30%,95%confidence interval,85.3%-94.9%,P<0.01).Meta-regression identified the variables,including the method of patient enrollment,method of consistency testing,and patient race,which explained the substantial study heterogeneity.CONCLUSION CEUS LI-RADS demonstrated overall substantial interobserver agreement,but heterogeneous results between studies were also obvious.Further clinical investigations should consider a modified recommendation about the experimental design.展开更多
Objective:Vesical Imaging Reporting and Data System(VIRADS)score was developed to standardize the reporting and staging of bladder tumors on pre-operative multiparametric magnetic resonance imaging.It helps in avoidin...Objective:Vesical Imaging Reporting and Data System(VIRADS)score was developed to standardize the reporting and staging of bladder tumors on pre-operative multiparametric magnetic resonance imaging.It helps in avoiding unnecessary repeat transurethral resection of bladder tumor in high-risk non-muscle-invasive bladder cancer patients.This study was done to determine the validity of VIRADS score prospectively for the diagnosis of muscleinvasive bladder cancer.Methods:This study was conducted from March 2019 to March 2020 at Sawai Man Singh Medical College and Hospital,Jaipur,Rajasthan,India.Patients admitted with the provisional diagnosis of bladder tumor were included as participants.All these patients underwent a 3 Tesla mpMRI to obtain a VIRADS score before they underwent transurethral resection of bladder tumor and these data were analyzed to evaluate the correlation of pre-operative VIRADS score with mus-cle invasiveness of the tumor in final biopsy report.Results:A cut-off of VIRADS≥4 for prediction of detrusor muscle invasion yielded a sensitivity of 79.4%,specificity of 94.2%,positive predictive value of 90.0%,negative predictive value of 87.5%,and diagnostic accuracy of 86.4%.A cut off of VIRADS≥3 for prediction of detrusor muscle invasion yielded a sensitivity of 91.2%,specificity of 78.8%,positive predictive value of 73.8%,negative predictive value of 93.2%,and accuracy of 83.7%.The receiver operating curve showed the area under the curve to be 0.922(95%confidence interval:0.862e0.983).Conclusion:VIRADS score appears to be an excellent and effective pre-operative radiological tool for the prediction of detrusor muscle invasion in bladder cancer.展开更多
文摘The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.
基金Supported by the Special Fundation of China Geological Survey(1212010911084)~~
文摘[Objective] To extract desertification information of Hulun Buir region based on MODIS image data. [Method] Based on MODIS image data with the spatial res- olution of 1 km, 5 indicators which could reflect different desertification features were selected to conduct inversion. The desertification information of Hulun Buir region was extracted by decision tree classification. [Result] The desertification area of Hu- lun Buir region is 33 862 km2, accounting for 24% of the total area, and it is mainly dominated by sandiness desertification. Though field verification and mining point validation of high-resolution interpretation data, the overall accuracy of this evaluation is above 89%. [Conclusion] Evaluation method used in this study is not only effectively for large scale regional desertification monitoring but also has a better evaluation performance.
基金Supported by the Specialized Research Fund for the Doctoral Pro-gram of Higher Education of MOE, P.R.C. (No. 20050003041) and the National Natural Science Foundation of China (No. 50275083)
文摘Automatic defect detection in X-ray images is currently a focus of much research at home and abroad. The technology requires computerized image processing, image analysis, and pattern recognition. This paper describes an image processing method for automatic defect detection using image data fusion which synthesizes several methods including edge extraction, wave profile analyses, segmentation with dynamic threshold, and weld district extraction. Test results show that defects that induce an abrupt change over a predefined extent of the image intensity can be segmented regardless of the number, location, shape or size. Thus, the method is more robust and practical than the current methods using only one method.
基金partly supported by the JSPS Grant-in-Aid for Scientific Research #17300032
文摘Full-parallax light-field is captured by a small-scale 3D image scanning system and applied to holographic display. A vertical camera array is scanned horizontally to capture full-parallax imagery, and the vertical views between cameras are interpolated by depth image-based rendering technique. An improved technique for depth estimation reduces the estimation error and high-density light-field is obtained. The captured data is employed for the calculation of computer hologram using ray-sampling plane. This technique enables high-resolution display even in deep 3D scene although a hologram is calculated from ray information, and thus it makes use of the important advantage of holographic 3D display.
基金This work is funded in part by the Ministry of Science and Technology,Taiwan,under grant MOST 108-2221-E-011-162-MY2.
文摘Nowadays since the Internet is ubiquitous,the frequency of data transfer through the public network is increasing.Hiding secure data in these transmitted data has emerged broad security issue,such as authentication and copyright protection.On the other hand,considering the transmission efficiency issue,image transmission usually involves image compression in Internet-based applications.To address both issues,this paper presents a data hiding scheme for the image compression method called absolute moment block truncation coding(AMBTC).First,an image is divided into nonoverlapping blocks through AMBTC compression,the blocks are classified four types,namely smooth,semi-smooth,semi-complex,and complex.The secret data are embedded into the smooth blocks by using a simple replacement strategy.The proposed method respectively embeds nine bits(and five bits)of secret data into the bitmap of the semi-smooth blocks(and semicomplex blocks)through the exclusive-or(XOR)operation.The secret data are embedded into the complex blocks by using a hidden function.After the embedding phase,the direct binary search(DBS)method is performed to improve the image qualitywithout damaging the secret data.The experimental results demonstrate that the proposed method yields higher quality and hiding capacity than other reference methods.
文摘A recent trend in computer graphics and image processing is to use Iterated Function System (IFS) to generate and describe both man-made graphics and natural images. Jacquin was the first to propose a fully automatic gray scale image compression algorithm which is referred to as a typical static fractal transform based algorithm in this paper. By using this algorithm, an image can be condensely described as a fractal transform operator which is the combination of a set of fractal mappings. When the fractal transform operator is iteratedly applied to any initial image, a unique attractor (reconstructed image) can be achieved. In this paper) a dynamic fractal transform is presented which is a modification of the static transform. Instead of being fixed, the dynamic transform operator varies in each decoder iteration, thus differs from static transform operators. The new transform has advantages in improving coding efficiency and shows better convergence for the decoder.
文摘In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the first two were invented by other persons and the third one, by ourselves. As a result, the comparison among their compression rates is. given at the end of this paper. Further application of these image compression technique to satellite data and other meteorological data looks promising.
文摘Remotely sensed spectral data and images are acquired under significant additional effects accompanying their major formation process, which greatly determine measurement accuracy. In order to be used in subsequent quantitative analysis and assessment, this data should be subject to preliminary processing aiming to improve its accuracy and credibility. The paper considers some major problems related with preliminary processing of remotely sensed spectral data and images. The major factors are analyzed, which affect the occurrence of data noise or uncertainties and the methods for reduction or removal thereof. Assessment is made of the extent to which available equipment and technologies may help reduce measurement errors.
文摘The reverse design of solid rocket motor(SRM)propellant grain involves determining the grain geometry to closely match a predefined internal ballistic curve.While existing reverse design methods are feasible,they often face challenges such as lengthy computation times and limited accuracy.To achieve rapid and accurate matching between the targeted ballistic curve and complex grain shape,this paper proposes a novel reverse design method for SRM propellant grain based on time-series data imaging and convolutional neural network(CNN).First,a finocyl grain shape-internal ballistic curve dataset is created using parametric modeling techniques to comprehensively cover the design space.Next,the internal ballistic time-series data is encoded into three-channel images,establishing a potential relationship between the ballistic curves and their image representations.A CNN is then constructed and trained using these encoded images.Once trained,the model enables efficient inference of propellant grain dimensions from a target internal ballistic curve.This paper conducts comparative experiments across various neural network models,validating the effectiveness of the feature extraction method that transforms internal ballistic time-series data into images,as well as its generalization capability across different CNN architectures.Ignition tests were performed based on the predicted propellant grain.The results demonstrate that the relative error between the experimental internal ballistic curves and the target curves is less than 5%,confirming the validity and feasibility of the proposed reverse design methodology.
文摘Objective This study aimed to assess the local staging of bladder tumors in patients utilizing preoperative multiparametric MRI(mpMRI)and to demonstrate the clinical efficacy of this method through a comparative analysis with corresponding histopathological findings.Methods Between November 2020 and April 2022,63 patients with a planned cystoscopy and a preliminary or previous diagnosis of bladder tumor were included.All participants underwent mpMRI,and Vesical Imaging Reporting and Data System(VI-RADS)criteria were applied to assess the recorded images.Subsequently,obtained biopsies were histopathologically examined and compared with radiological findings.Results Of the 63 participants,60 were male,and three were female.Categorizing tumors with a VI-RADS score of>3 as muscle invasive,84%were radiologically classified as having an invasive bladder tumor.However,histopathological results indicated invasive bladder tumors in 52%of cases.Sensitivity of the VI-RADS score was 100%;specificity was 23%;the negative predictive value was 100%;and the positive predictive value was 62%.Conclusion The scoring system obtained through mpMRI,VI-RADS,proves to be a successful method,particularly in determining the absence of muscle invasion in bladder cancer.Its efficacy in detecting muscle invasion in bladder tumors could be further enhanced with additional studies,suggesting potential for increased diagnostic efficiency through ongoing research.The VI-RADS could enhance the selection of patients eligible for accurate diagnosis and treatment.
基金funded by the Third Xinjiang Scientific Expedition Program(2021xjkk1400)the National Natural Science Foundation of China(42071049)+2 种基金the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2019D01C022)the Xinjiang Uygur Autonomous Region Innovation Environment Construction Special Project&Science and Technology Innovation Base Construction Project(PT2107)the Tianshan Talent-Science and Technology Innovation Team(2022TSYCTD0006).
文摘Snow cover plays a critical role in global climate regulation and hydrological processes.Accurate monitoring is essential for understanding snow distribution patterns,managing water resources,and assessing the impacts of climate change.Remote sensing has become a vital tool for snow monitoring,with the widely used Moderate-resolution Imaging Spectroradiometer(MODIS)snow products from the Terra and Aqua satellites.However,cloud cover often interferes with snow detection,making cloud removal techniques crucial for reliable snow product generation.This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms.Using real-time field camera observations from four stations in the Tianshan Mountains,China,this study assessed the performance of these datasets during three distinct snow periods:the snow accumulation period(September-November),snowmelt period(March-June),and stable snow period(December-February in the following year).The findings showed that cloud-free snow products generated using the Hidden Markov Random Field(HMRF)algorithm consistently outperformed the others,particularly under cloud cover,while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction(STAR)demonstrated varying performance depending on terrain complexity and cloud conditions.This study highlighted the importance of considering terrain features,land cover types,and snow dynamics when selecting cloud removal methods,particularly in areas with rapid snow accumulation and melting.The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning,multi-source data fusion,and advanced remote sensing technologies.By expanding validation efforts and refining cloud removal strategies,more accurate and reliable snow products can be developed,contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.
文摘China has a vast territory with abundant crops,and how to collect crop information in China timely,objectively and accurately,is of great significance to the scientific guidance of agricultural development.In this paper,by selecting moderateresolution imaging spectroradiometer(MODIS)data as the main information source,on the basis of spectral and biological characteristics mechanism of the crop,and using the freely available advantage of hyperspectral temporal MODIS data,conduct large scale agricultural remote sensing monitoring research,develop applicable model and algorithm,which can achieve large scale remote sensing extraction and yield estimation of major crop type information,and improve the accuracy of crop quantitative remote sensing.Moreover,the present situation of global crop remote sensing monitoring based on MODIS data is analyzed.Meanwhile,the climate and environment grid agriculture information system using large-scale agricultural condition remote sensing monitoring has been attempted preliminary.
基金supported by the National High Technology Research and Development Program of China (Grant No. 863-2-5-1-13B)
文摘Multispectral time delay and integration charge coupled device （TDICCD） image compression requires a low- complexity encoder because it is usually completed on board where the energy and memory are limited. The Consultative Committee for Space Data Systems （CCSDS） has proposed an image data compression （CCSDS-IDC） algorithm which is so far most widely implemented in hardware. However, it cannot reduce spectral redundancy in mukispectral images. In this paper, we propose a low-complexity improved CCSDS-IDC （ICCSDS-IDC）-based distributed source coding （DSC） scheme for multispectral TDICCD image consisting of a few bands. Our scheme is based on an ICCSDS-IDC approach that uses a bit plane extractor to parse the differences in the original image and its wavelet transformed coefficient. The output of bit plane extractor will be encoded by a first order entropy coder. Low-density parity-check-based Slepian-Wolf （SW） coder is adopted to implement the DSC strategy. Experimental results on space multispectral TDICCD images show that the proposed scheme significantly outperforms the CCSDS-IDC-based coder in each band.
文摘In the area of pattern recognition and machine learning,features play a key role in prediction.The famous applications of features are medical imaging,image classification,and name a few more.With the exponential growth of information investments in medical data repositories and health service provision,medical institutions are collecting large volumes of data.These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality.On the other hand,this growth also made it difficult to comprehend and utilize data for various purposes.The results of imaging data can become biased because of extraneous features present in larger datasets.Feature selection gives a chance to decrease the number of components in such large datasets.Through selection techniques,ousting the unimportant features and selecting a subset of components that produces prevalent characterization precision.The correct decision to find a good attribute produces a precise grouping model,which enhances learning pace and forecast control.This paper presents a review of feature selection techniques and attributes selection measures for medical imaging.This review is meant to describe feature selection techniques in a medical domainwith their pros and cons and to signify its application in imaging data and data mining algorithms.The review reveals the shortcomings of the existing feature and attributes selection techniques to multi-sourced data.Moreover,this review provides the importance of feature selection for correct classification of medical infections.In the end,critical analysis and future directions are provided.
文摘Prostate cancer （PCa） is one of the most common cancers among men globally. The authors aimed to evaluate the ability of the Prostate Imaging Reporting and Data System version 2 （PI-RADS v2） to classify men with PCa, clinically significant PCa （CSPCa）, or no PCa, especially among those with serum total prostate-specific antigen （tPSA） levels in the ＂gray zone＂ （4-10 ng ml-1）. A total of 308 patients （355 lesions） were enrolled in this study. Diagnostic efficiency was determined. Univariate and multivariate analyses, receiver operating characteristic curve analysis, and decision curve analysis were performed to determine and compare the predictors of PCa and CSPCa. The results suggested that PI-RADS v2, tPSA, and prostate-specific antigen density （PSAD） were independent predictors of PCa and CSPCa. A PI-RADS v2 score L≥4 provided high negative predictive values （91.39% for PCa and 95.69% for CSPCa）. A model of PI-RADS combined with PSA and PSAD helped to define a high-risk group （PI-RADS score = 5 and PSAD L≥0 15 ng ml-1 cm-3, with tPSA in the gray zone, or PI-RADS score L≥4 with high tPSA level） with a detection rate of 96.1% for PCa and 93.0% for CSPCa while a low-risk group with a detection rate of 6.1% for PCa and 2.2% for CSPCa. It was concluded that the PI-RADS v2 could be used as a reliable and independent predictor of PCa and CSPCa. The combination of PI-RADS v2 score with PSA and PSAD could be helpful in the prediction and diagnosis of PCa and CSPCa and, thus, may help in preventing unnecessary invasive procedures.
基金Supported by the Fondazione di Sardegna,No.FDS2019VIDILIthe University of Sassari,No.FAR2019.
文摘BACKGROUND Contrast-enhanced ultrasound(CEUS)is considered a secondary examination compared to computed tomography(CT)and magnetic resonance imaging(MRI)in the diagnosis of hepatocellular carcinoma(HCC),due to the risk of misdiagnosing intrahepatic cholangiocarcinoma(ICC).The introduction of CEUS Liver Imaging Reporting and Data System(CEUS LI-RADS)might overcome this limitation.Even though data from the literature seems promising,its reliability in real-life context has not been well-established yet.AIM To test the accuracy of CEUS LI-RADS for correctly diagnosing HCC and ICC in cirrhosis.METHODS CEUS LI-RADS class was retrospectively assigned to 511 nodules identified in 269 patients suffering from liver cirrhosis.The diagnostic standard for all nodules was either biopsy(102 nodules)or CT/MRI(409 nodules).Common diagnostic accuracy indexes such as sensitivity,specificity,positive predictive value(PPV),and negative predictive value(NPV)were assessed for the following associations:CEUS LR-5 and HCC;CEUS LR-4 and 5 merged class and HCC;CEUS LR-M and ICC;and CEUS LR-3 and malignancy.The frequency of malignant lesions in CEUS LR-3 subgroups with different CEUS patterns was also determined.Inter-rater agreement for CEUS LI-RADS class assignment and for major CEUS pattern identification was evaluated.RESULTS CEUS LR-5 predicted HCC with a 67.6%sensitivity,97.7%specificity,and 99.3%PPV(P<0.001).The merging of LR-4 and 5 offered an improved 93.9%sensitivity in HCC diagnosis with a 94.3%specificity and 98.8%PPV(P<0.001).CEUS LR-M predicted ICC with a 91.3%sensitivity,96.7%specificity,and 99.6%NPV(P<0.001).CEUS LR-3 predominantly included benign lesions(only 28.8%of malignancies).In this class,the hypo-hypo pattern showed a much higher rate of malignant lesions(73.3%)than the iso-iso pattern(2.6%).Inter-rater agreement between internal raters for CEUS-LR class assignment was almost perfect(n=511,k=0.94,P<0.001),while the agreement among raters from separate centres was substantial(n=50,k=0.67,P<0.001).Agreement was stronger for arterial phase hyperenhancement(internal k=0.86,P<2.7×10-214;external k=0.8,P<0.001)than washout(internal k=0.79,P<1.6×10-202;external k=0.71,P<0.001).CONCLUSION CEUS LI-RADS is effective but can be improved by merging LR-4 and 5 to diagnose HCC and by splitting LR-3 into two subgroups to differentiate iso-iso nodules from other patterns.
基金the Chinese National Natural Science Foundation of China(G41805016)the Chinese National Key R&D Program of China(2018YFC1506404)+3 种基金the Chinese National Natural Science Founda-tion of China(G41805070)the Chinese National Key R&D Program of China(2018YFC1506603)the research project of Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province in China(SZKT201901,SZKT201904)the research project of the Institute of Atmospheric Environment,China Meteorological Administration,Shenyang in China(2020SYIAE07,2020SYIAE02)。
文摘The impact of assimilating radiance data from the advanced satellite sensor GMI(GPM microwave imager)for typhoon analyses and forecasts was investigated using both a three-dimensional variational(3DVAR)and a hybrid ensemble-3DVAR method.The interface of assimilating the radiance for the sensor GMI was established in the Weather Research and Forecasting(WRF)model.The GMI radiance data are assimilated for Typhoon Matmo(2014),Typhoon Chan-hom(2015),Typhoon Meranti(2016),and Typhoon Mangkhut(2018)in the Pacific before their landing.The results show that after assimilating the GMI radiance data under clear sky condition with the 3DVAR method,the wind,temperature,and humidity fields are effectively adjusted,leading to improved forecast skills of the typhoon track with GMI radiance assimilation.The hybrid DA method is able to further adjust the location of the typhoon systematically.The improvement of the track forecast is even more obvious for later forecast periods.In addition,water vapor and hydrometeors are enhanced to some extent,especially with the hybrid method.
文摘Hepatocellular carcinoma(HCC)is the sixth most common cancer.The main risk factors associated with HCC development include hepatitis B virus,hepatitis C virus,alcohol consumption,aflatoxin B1,and nonalcoholic fatty liver disease.However,hepatocarcinogenesis is a complex multistep process.Various factors lead to hepatocyte malignant transformation and HCC development.Diagnosis and surveillance of HCC can be made with the use of liver ultrasound(US)every 6 mo.However,the sensitivity of this imaging method to detect HCC in a cirrhotic liver is limited,due to the abnormal liver parenchyma.Computed tomography(CT)and magnetic resonance imaging(MRI)are considered to be most useful tools for at-risk patients or patients with inadequate US.Liver biopsy is still used for diagnosis and prognosis of HCC in specific nodules that cannot be definitely characterized as HCC by imaging.Recently the American College of Radiology designed the Liver Imaging Reporting and Data System(LI-RADS),which is a comprehensive system for standardized interpretation of CT and MRI liver examinations that was first proposed in 2011.In 2018,it was integrated into the American Association for the Study of Liver Diseases guidance statement for HCC.LI-RADS is designed to ensure high sensitivity,precise categorization,and high positive predictive value for the diagnosis of HCC and is applied to“highrisk populations”according to specific criteria.Most importantly LI-RADS criteria achieved international collaboration and consensus among liver experts around the world on the best practices for caring for patients with or at risk for HCC.
基金Supported by Health Commission of Hubei Province,China No.WJ2019M077 and No.WJ2019H227Natural Science Foundation of Hubei Province,China No.2019CFB286and Science and Technology Bureau of Shihezi,China No.2019ZH11.
文摘BACKGROUND Hepatocellular carcinoma is the most common primary liver malignancy.From the results of previous studies,Liver Imaging Reporting and Data System(LIRADS)on contrast-enhanced ultrasound(CEUS)has shown satisfactory diagnostic value.However,a unified conclusion on the interobserver stability of this innovative ultrasound imaging has not been determined.The present metaanalysis examined the interobserver agreement of CEUS LI-RADS to provide some reference for subsequent related research.AIM To evaluate the interobserver agreement of LI-RADS on CEUS and analyze the sources of heterogeneity between studies.METHODS Relevant papers on the subject of interobserver agreement on CEUS LI-RADS published before March 1,2020 in China and other countries were analyzed.The studies were filtered,and the diagnostic criteria were evaluated.The selected references were analyzed using the“meta”and“metafor”packages of R software version 3.6.2.RESULTS Eight studies were ultimately included in the present analysis.Meta-analysis results revealed that the summary Kappa value of included studies was 0.76[95%confidence interval,0.67-0.83],which shows substantial agreement.Higgins I2 statistics also confirmed the substantial heterogeneity(I2=91.30%,95%confidence interval,85.3%-94.9%,P<0.01).Meta-regression identified the variables,including the method of patient enrollment,method of consistency testing,and patient race,which explained the substantial study heterogeneity.CONCLUSION CEUS LI-RADS demonstrated overall substantial interobserver agreement,but heterogeneous results between studies were also obvious.Further clinical investigations should consider a modified recommendation about the experimental design.
文摘Objective:Vesical Imaging Reporting and Data System(VIRADS)score was developed to standardize the reporting and staging of bladder tumors on pre-operative multiparametric magnetic resonance imaging.It helps in avoiding unnecessary repeat transurethral resection of bladder tumor in high-risk non-muscle-invasive bladder cancer patients.This study was done to determine the validity of VIRADS score prospectively for the diagnosis of muscleinvasive bladder cancer.Methods:This study was conducted from March 2019 to March 2020 at Sawai Man Singh Medical College and Hospital,Jaipur,Rajasthan,India.Patients admitted with the provisional diagnosis of bladder tumor were included as participants.All these patients underwent a 3 Tesla mpMRI to obtain a VIRADS score before they underwent transurethral resection of bladder tumor and these data were analyzed to evaluate the correlation of pre-operative VIRADS score with mus-cle invasiveness of the tumor in final biopsy report.Results:A cut-off of VIRADS≥4 for prediction of detrusor muscle invasion yielded a sensitivity of 79.4%,specificity of 94.2%,positive predictive value of 90.0%,negative predictive value of 87.5%,and diagnostic accuracy of 86.4%.A cut off of VIRADS≥3 for prediction of detrusor muscle invasion yielded a sensitivity of 91.2%,specificity of 78.8%,positive predictive value of 73.8%,negative predictive value of 93.2%,and accuracy of 83.7%.The receiver operating curve showed the area under the curve to be 0.922(95%confidence interval:0.862e0.983).Conclusion:VIRADS score appears to be an excellent and effective pre-operative radiological tool for the prediction of detrusor muscle invasion in bladder cancer.
