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Infrared and Visible Image Fusion Based on Res2Net-Transformer Automatic Encoding and Decoding
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作者 Chunming Wu Wukai Liu Xin Ma 《Computers, Materials & Continua》 SCIE EI 2024年第4期1441-1461,共21页
A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase thevisual impression of fused images by improving the quality of infrared and visible light picture fusion. The ne... A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase thevisual impression of fused images by improving the quality of infrared and visible light picture fusion. The networkcomprises an encoder module, fusion layer, decoder module, and edge improvementmodule. The encoder moduleutilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformerto achieve deep-level co-extraction of local and global features from the original picture. An edge enhancementmodule (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy isintroduced to enhance the adaptive representation of information in various regions of the source image, therebyenhancing the contrast of the fused image. The encoder and the EEM module extract features, which are thencombined in the fusion layer to create a fused picture using the decoder. Three datasets were chosen to test thealgorithmproposed in this paper. The results of the experiments demonstrate that the network effectively preservesbackground and detail information in both infrared and visible images, yielding superior outcomes in subjectiveand objective evaluations. 展开更多
关键词 image fusion Res2Net-Transformer infrared image visible image
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Sentiment Analysis Using E-Commerce Review Keyword-Generated Image with a Hybrid Machine Learning-Based Model
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作者 Jiawen Li Yuesheng Huang +3 位作者 Yayi Lu Leijun Wang Yongqi Ren Rongjun Chen 《Computers, Materials & Continua》 SCIE EI 2024年第7期1581-1599,共19页
In the context of the accelerated pace of daily life and the development of e-commerce,online shopping is a mainstreamway for consumers to access products and services.To understand their emotional expressions in faci... In the context of the accelerated pace of daily life and the development of e-commerce,online shopping is a mainstreamway for consumers to access products and services.To understand their emotional expressions in facing different shopping experience scenarios,this paper presents a sentiment analysis method that combines the ecommerce reviewkeyword-generated imagewith a hybrid machine learning-basedmodel,inwhich theWord2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence(AI).Subsequently,a hybrid Convolutional Neural Network and Support Vector Machine(CNNSVM)model is applied for sentiment classification of those keyword-generated images.For method validation,the data randomly comprised of 5000 reviews from Amazon have been analyzed.With superior keyword extraction capability,the proposedmethod achieves impressive results on sentiment classification with a remarkable accuracy of up to 97.13%.Such performance demonstrates its advantages by using the text-to-image approach,providing a unique perspective for sentiment analysis in the e-commerce review data compared to the existing works.Thus,the proposed method enhances the reliability and insights of customer feedback surveys,which would also establish a novel direction in similar cases,such as social media monitoring and market trend research. 展开更多
关键词 Sentiment analysis keyword-generated image machine learning Word2Vec-TextRank CNN-SVM
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Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales
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作者 Jie Song Dongsheng Yu +4 位作者 Siwei Wang Yanhe Zhao Xin Wang Lixia Ma Jiangang Li 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2024年第4期1393-1408,共16页
Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to pred... Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images. 展开更多
关键词 soil organic matter Sentinel-2 monthly synthetic images machine learning model spatial prediction
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T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma
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作者 Hui-Da Zheng Qiao-Yi Huang +4 位作者 Qi-Ming Huang Xiao-Ting Ke Kai Ye Shu Lin Jian-Hua Xu 《World Journal of Gastrointestinal Oncology》 SCIE 2024年第3期819-832,共14页
BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation gr... BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy. 展开更多
关键词 Radiomics Colorectal cancer Differentiation grade Machine learning T2-weighted imaging
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A Novel 2D Hyperchaotic with a Complex Dynamic Behavior for Color Image Encryption 被引量:1
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作者 Yongsheng Hu Liyong Nan 《Computers, Materials & Continua》 SCIE EI 2023年第3期6555-6571,共17页
The generation method of the key stream and the structure of the algorithm determine the security of the cryptosystem.The classical chaotic map has simple dynamic behavior and few control parameters,so it is not suita... The generation method of the key stream and the structure of the algorithm determine the security of the cryptosystem.The classical chaotic map has simple dynamic behavior and few control parameters,so it is not suitable for modern cryptography.In this paper,we design a new 2D hyperchaotic system called 2D simple structure and complex dynamic behavior map(2D-SSCDB).The 2D-SSCDB has a simple structure but has complex dynamic behavior.The Lyapunov exponent verifies that the 2D-SSCDB has hyperchaotic behavior,and the parameter space in the hyperchaotic state is extensive and continuous.Trajectory analysis and some randomness tests verify that the 2D-SSCDB can generate random sequences with good performance.Next,to verify the excellent performance of the 2D-SSCDB,we use the 2D-SSCDB to generate a keystream for color image encryption.In the encryption algorithm,the encryption algorithm scrambles and diffuses simultaneously,increasing the cryptographic system’s security.The horizontal correlation,vertical correlation,and diagonal correlation of ciphertext are−0.0004,−0.0004 and 0.0007,respectively.The average information entropy of the ciphertext is 7.9993.In addition,the designed encryption algorithm reduces the correlation between the three channels of the color image.Security analysis shows that the color image encryption algorithm designed using 2DSSCDB has good security,can resist standard attack methods,and has high efficiency. 展开更多
关键词 Chaos theory 2D-SSCDB CRYPTOGRAPHY image encryption
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Image to Image Translation Based on Differential Image Pix2Pix Model 被引量:1
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作者 Xi Zhao Haizheng Yu Hong Bian 《Computers, Materials & Continua》 SCIE EI 2023年第10期181-198,共18页
In recent years,Pix2Pix,a model within the domain of GANs,has found widespread application in the field of image-to-image translation.However,traditional Pix2Pix models suffer from significant drawbacks in image gener... In recent years,Pix2Pix,a model within the domain of GANs,has found widespread application in the field of image-to-image translation.However,traditional Pix2Pix models suffer from significant drawbacks in image generation,such as the loss of important information features during the encoding and decoding processes,as well as a lack of constraints during the training process.To address these issues and improve the quality of Pix2Pixgenerated images,this paper introduces two key enhancements.Firstly,to reduce information loss during encoding and decoding,we utilize the U-Net++network as the generator for the Pix2Pix model,incorporating denser skip-connection to minimize information loss.Secondly,to enhance constraints during image generation,we introduce a specialized discriminator designed to distinguish differential images,further enhancing the quality of the generated images.We conducted experiments on the facades dataset and the sketch portrait dataset from the Chinese University of Hong Kong to validate our proposed model.The experimental results demonstrate that our improved Pix2Pix model significantly enhances image quality and outperforms other models in the selected metrics.Notably,the Pix2Pix model incorporating the differential image discriminator exhibits the most substantial improvements across all metrics.An analysis of the experimental results reveals that the use of the U-Net++generator effectively reduces information feature loss,while the Pix2Pix model incorporating the differential image discriminator enhances the supervision of the generator during training.Both of these enhancements collectively improve the quality of Pix2Pix-generated images. 展开更多
关键词 image-to-image translation generative adversarial networks U-Net++ differential image Pix2Pix
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Retrieving chlorophyll content and equivalent water thickness of Moso bamboo(Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress from Sentinel-2A/B images in a multiple LUTs-based PROSAIL framework 被引量:1
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作者 Zhanghua Xu Anqi He +10 位作者 Yiwei Zhang Zhenbang Hao Yifan Li Songyang Xiang Bin Li Lingyan Chen Hui Yu Wanling Shen Xuying Huang Xiaoyu Guo Zenglu Li 《Forest Ecosystems》 SCIE CSCD 2023年第2期252-267,共16页
Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT w... Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT with SAIL(PROSAIL)radiative transfer model is widely used for vegetation biochemical component content inversion.However,the presence of leaf-eating pests,such as Pantana phyllostachysae Chao(PPC),weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered.Therefore,this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables(LUTs)based on the PROSAIL framework by setting different parameter ranges according to infestation levels.Quantitative inversions of leaf area index(LAI),leaf chlorophyll content(LCC),and leaf equivalent water thickness(LEWT)were derived.The scale conversions from LCC to canopy chlorophyll content(CCC)and LEWT to canopy equivalent water thickness(CEWT)were calculated.The results showed that LAI,CCC,and CEWT were inversely related with PPC-induced stress.When applying multiple LUTs,the p-values were<0.01;the R2 values for LAI,CCC,and CEWT were 0.71,0.68,and 0.65 with root mean square error(RMSE)(normalized RMSE,NRMSE)values of 0.38(0.16),17.56μg cm-2(0.20),and 0.02 cm(0.51),respectively.Compared to the values obtained for the traditional PROSAIL model,for October,R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT,respectively and RMSE decreased by 0.35μg cm-2 for CCC.The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model,while establishing multiple LUTs under different pest-induced damage levels,was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems. 展开更多
关键词 Moso bamboo Chlorophyll content Equivalent water thickness PROSAIL model Multiple LUTs Pantana phyllostachysae Chao Sentinel-2A/B images
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A novel water index for urban high-resolution eight-band WorldView-2 imagery 被引量:3
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作者 Cong Xie Xin Huang +1 位作者 Wenxian Zeng Xing Fang 《International Journal of Digital Earth》 SCIE EI CSCD 2016年第10期925-941,共17页
Land surface water mapping is one of the most important remote-sensing applications.However,water areas are spectrally similar and overlapped with shadow,making accurate water extraction from remote-sensing images sti... Land surface water mapping is one of the most important remote-sensing applications.However,water areas are spectrally similar and overlapped with shadow,making accurate water extraction from remote-sensing images still a challenging problem.This paper develops a novel water index named as NDWI-MSI,combining a new normalized difference water index(NDWI)and a recently developed morphological shadow index(MSI),to delineate water bodies from eight-band WorldView-2 imagery.The newly available bands(e.g.coastal,yellow,red-edge,and near-infrared 2)of WorldView-2 imagery provide more potential for constructing new NDWIs derived from various band combinations.Through our testing,a new NDWI is defined in this study.In addition,MSI,a recently developed automatic shadow extraction index from high-resolution imagery can be used to indicate shadow areas.The NDWI-MSI is created by combining NDWI and MSI,which is able to highlight water bodies and simultaneously suppress shadow areas.In experiments,it is shown that the new water index can achieve better performance than traditional NDWI,and even supervised classifiers,for example,maximum likelihood classifier,and support vector machine. 展开更多
关键词 worldview-2 water extraction water index shadow detection
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Development of a Generic Model for the Detection of Roof Materials Based on an Object-Based Approach Using WorldView-2 Satellite Imagery 被引量:1
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作者 Ebrahim Taherzadeh Helmi Z. M. Shafri 《Advances in Remote Sensing》 2013年第4期312-321,共10页
The detection of impervious surface (IS) in heterogeneous urban areas is one of the most challenging tasks in urban remote sensing. One of the limitations in IS detection at the parcel level is the lack of sufficient ... The detection of impervious surface (IS) in heterogeneous urban areas is one of the most challenging tasks in urban remote sensing. One of the limitations in IS detection at the parcel level is the lack of sufficient training data. In this study, a generic model of spatial distribution of roof materials is considered to overcome this limitation. A generic model that is based on spectral, spatial and textural information which is extracted from available training data is proposed. An object-based approach is used to extract the information inherent in the image. Furthermore, linear discriminant analysis is used for dimensionality reduction and to discriminate between different spatial, spectral and textural attributes. The generic model is composed of a discriminant function based on linear combinations of the predictor variables that provide the best discrimination among the groups. The discriminate analysis result shows that of the 54 attributes extracted from the WorldView-2 image, only 13 attributes related to spatial, spectral and textural information are useful for discriminating different roof materials. Finally, this model is applied to different WorldView-2 images from different areas and proves that this model has good potential to predict roof materials from the WorldView-2 images without using training data. 展开更多
关键词 URBAN Object-Based DISCRIMINANT Analysis ROOF MATERIALS Very High RESOLUTION imageRY worldview-2
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MRI T_2 star mapping、T_1 images与3D DESS融合图在隐匿性膝关节软骨损伤中的应用 被引量:4
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作者 范伟雄 杨志企 +3 位作者 程凤燕 黄健 于昭 侯文忠 《临床医学工程》 2017年第4期437-439,共3页
目的探讨T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨损伤中的诊断价值。方法对26例关节软骨损伤患者行T_2 star mapping、T_1 images和3D DESS扫描,并将T_1 images、T_2 star mapping与3D DESS图像融合,评价患者股骨... 目的探讨T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨损伤中的诊断价值。方法对26例关节软骨损伤患者行T_2 star mapping、T_1 images和3D DESS扫描,并将T_1 images、T_2 star mapping与3D DESS图像融合,评价患者股骨、胫骨、髌骨关节软骨损伤程度并与关节镜结果对比,计算融合伪彩图诊断软骨损伤的特异性、敏感性及与关节镜诊断结果一致性。结果 T_1 images-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为92.8%、93.0%、0.769,T_2 star mapping-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为91.4%、94.2%、0.787。结论 T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨早期损伤评价上优于关节镜。 展开更多
关键词 膝关节 关节软骨 磁共振成像 T2 star mapping T1 imageS 3D DESS
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2型糖尿病患者骨骼肌厚度和弹性变化及其影响因素
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作者 冯晓蕾 黄丽丽 +1 位作者 周琦 李苗 《中国医学影像技术》 CSCD 北大核心 2024年第2期270-274,共5页
目的观察2型糖尿病患者骨骼肌厚度和弹性变化及其影响因素。方法前瞻性招募62例2型糖尿病患者(T2DM组)及60名健康成年人(HC组),以剪切波弹性成像(SWE)技术检测其放松及收缩状态下腹直肌和腓肠肌最大杨氏模量(E_(max)),比较组间2种状态... 目的观察2型糖尿病患者骨骼肌厚度和弹性变化及其影响因素。方法前瞻性招募62例2型糖尿病患者(T2DM组)及60名健康成年人(HC组),以剪切波弹性成像(SWE)技术检测其放松及收缩状态下腹直肌和腓肠肌最大杨氏模量(E_(max)),比较组间2种状态下骨骼肌厚度及弹性模量,并采用多重线性回归分析影响腹直肌和腓肠肌弹性模量的因素。结果组间放松及收缩状态下腹直肌和腓肠肌厚度差异均无统计学意义(P均>0.05);T2DM组放松及收缩状态下腹直肌及腓肠肌Emax均低于HC组(P均<0.05)。多重线性回归分析显示,T2DM组放松及收缩状态下腹直肌及腓肠肌弹性模量均随病程、空腹血糖(FBG)及糖化血红蛋白(HbA1c)而呈线性降低(P均<0.05)。结论2型糖尿病患者骨骼肌弹性模量降低,且随病程、FBG及HbA1c而呈线性下降。 展开更多
关键词 糖尿病 2 骨骼 弹性成像技术 前瞻性研究
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糖尿病前期及2型糖尿病皮层萎缩与认知功能的相关性研究
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作者 李欣 张雯 +6 位作者 刘佳妮 傅琳清 缪应雯 张鑫 陈玖 毕艳 张冰 《磁共振成像》 CAS CSCD 北大核心 2024年第4期9-14,19,共7页
目的探讨糖尿病前期(pre-diabetes mellitus,PDM)和2型糖尿病(type 2 diabetes mellitus,T2DM)大脑皮层的改变模式及其与认知功能的关系,以期探寻血糖代谢异常被试早期脑损害的影像标志物。材料与方法本研究纳入96例T2DM患者、30例PDM... 目的探讨糖尿病前期(pre-diabetes mellitus,PDM)和2型糖尿病(type 2 diabetes mellitus,T2DM)大脑皮层的改变模式及其与认知功能的关系,以期探寻血糖代谢异常被试早期脑损害的影像标志物。材料与方法本研究纳入96例T2DM患者、30例PDM被试以及48名正常对照(normal control,NC),对被试进行了认知功能测试、临床生化检查及高分辨率3D-T1WI磁共振扫描。使用CAT12软件进行基于体素的形态学分析和基于表面的形态学分析得到全脑灰质体积和皮层厚度、局部回指数等皮层结构参数,并比较3组间的差异,结果均使用P<0.05的阈值和FWE校正进行多重比较校正。进一步提取组间具有差异的参数,与生化指标及认知量表得分进行相关分析。结果与NC相比,PDM被试右侧额下回眶部及左侧中央后回灰质萎缩(P<0.05,FWE校正),T2DM患者出现更多灰质萎缩,特别是右侧颞上回、右侧额下回眶部、右侧颞中回及左侧中央后回,右侧前额叶皮层厚度减小(P<0.05,FWE校正)。在血糖代谢异常被试中全脑灰质体积与胰岛素抵抗指数(r=−0.227,P=0.012,未校正)及连线测试A得分(r=−0.250,P=0.001,FDR校正)呈负相关,与数字广度-倒背得分呈正相关(r=0.267,P=0.003,FDR校正);皮层厚度与糖化血红蛋白(r=−0.181,P=0.040,未校正)及餐后2 h血糖(r=−0.272,P=0.020,未校正)呈负相关,与餐后2 h胰岛素(r=0.236,P=0.010,未校正)及胰岛β细胞功能指数(r=0.207,P=0.022,未校正)呈正相关。结论本研究发现PDM人群已存在脑区灰质萎缩,T2DM患者出现更多的灰质萎缩,且与注意和工作记忆功能相关,因此皮层萎缩有可能是糖尿病相关脑损伤早期的影像标志物。 展开更多
关键词 2型糖尿病 糖尿病前期 磁共振成像 皮层萎缩 认知功能
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基于Sentinel-2影像的巴尔托洛冰川冰面湖研究
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作者 刘晓 孙永玲 +1 位作者 孙世金 李敏 《测绘通报》 CSCD 北大核心 2024年第3期49-53,80,共6页
冰面湖是冰川的重要组成部分,是冰川消融的指示器,不仅对全球气候变化响应迅速,而且对了解和掌握区域水资源信息意义重大。本文基于Sentinel-2遥感数据,利用随机森林算法,对巴尔托洛冰川冰面湖进行识别提取,并基于提取结果分析研究区冰... 冰面湖是冰川的重要组成部分,是冰川消融的指示器,不仅对全球气候变化响应迅速,而且对了解和掌握区域水资源信息意义重大。本文基于Sentinel-2遥感数据,利用随机森林算法,对巴尔托洛冰川冰面湖进行识别提取,并基于提取结果分析研究区冰面湖的空间分布特征,以及冰面湖面积、数量与冰川高程的关系。本文冰面湖提取的准确率达96.07%,完整率达92.18%,错误率为11.59%;识别出巴尔托洛冰川冰面湖567个,面积为249.46~37134 m^(2);冰面湖多分布在距冰川末端3~26 km处,其中海拔3800~4300 m之间冰面湖数量最多,面积普遍较大,平均面积为1922 m^(2);随着高程的升高,冰面湖的数量和面积逐渐减少,在高程5300 m以上冰面湖数量仅为15个,平均面积为356 m^(2);高程升高导致冰面温度降低,是冰面湖数量和面积骤减的主要原因。 展开更多
关键词 巴尔托洛冰川 冰面湖 Sentinel-2影像 随机森林算法
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基于恒定梯度编码一维选层T_(2)谱的胶结砂岩层析成像方法
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作者 吴土荣 陈金定 +3 位作者 张群 赵希春 郭敏灵 程芳桂 《测井技术》 CAS 2024年第2期190-197,共8页
核磁共振成像是探究岩心尺度非均质性的先进技术,受岩石复杂性和编码技术影响,现有方法效果不佳。基于梯度场的二维/三维核磁共振技术成像容易但信噪比低,无梯度场的传统选层T_(2)谱(频率编码/相位编码)方法效率低。该研究提出恒定梯度... 核磁共振成像是探究岩心尺度非均质性的先进技术,受岩石复杂性和编码技术影响,现有方法效果不佳。基于梯度场的二维/三维核磁共振技术成像容易但信噪比低,无梯度场的传统选层T_(2)谱(频率编码/相位编码)方法效率低。该研究提出恒定梯度编码一维选层T_(2)谱层析成像方法,做到了成像数据体量小、信噪比高且信号更完整。层析T_(2)谱图像及其热度分布,能够有效表征毛细管压力和成岩作用对流体非均匀分布的影响。饱和油状态时,T_(2)谱层间差异性反映成岩作用影响。高饱和油阶段,轴向热度图反映了低毛细管压力游离油的脱出规律。完全驱替后毛细管压力进入稳定状态,轴向热度图反映了微孔中吸附油赋存状态的差异。该技术在胶结砂岩气驱油实验中取得了良好效果,并对解析强非均质岩石渗流规律有重要作用。 展开更多
关键词 岩心核磁共振 T_(2)谱成像 梯度编码 毛细管压力 非均质性
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基于时序Sentinel-2影像的引黄灌区作物结构提取和供需水分析
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作者 孙斌 毕春宁 +4 位作者 薛建春 毕华军 孙力 许建辉 李斌 《人民黄河》 CAS 北大核心 2024年第7期131-137,共7页
在黄河流域用水指标严格控制的背景下,以山东省东营市垦利区引黄灌区为例,利用2022年时序Sentinel-2遥感影像构建作物生育期的NDVI时间序列,采用决策树分类方法提取灌区作物种植结构,基于垦利站气象资料和Penman-Monteith公式,分析了197... 在黄河流域用水指标严格控制的背景下,以山东省东营市垦利区引黄灌区为例,利用2022年时序Sentinel-2遥感影像构建作物生育期的NDVI时间序列,采用决策树分类方法提取灌区作物种植结构,基于垦利站气象资料和Penman-Monteith公式,分析了1973—2022年各作物的需水特性,利用遥感影像解译的各作物种植面积,计算了2022年灌区作物在不同降水保证率(5%、25%、50%、75%、95%)条件下的灌溉总需水量,结合2023年分配给灌区作物的灌溉水指标探究了灌溉水资源供需之间的平衡。结果表明:基于NDVI时间序列构建决策树分类方法可有效提取作物的种植结构,总体分类精度为85.07%,Kappa系数为0.819,能够满足作物灌溉需水量的研究。作物净灌溉需水量年际波动较大,水稻和冬小麦补充灌溉水量在所有作物中位列前两位,均值分别为913 mm和410 mm;处于雨季生长的夏玉米、夏大豆补充灌溉水量较小且灌溉需求均值较小。研究区2023年分配的灌溉水指标在降水保证率为50%时研究区灌溉水亏缺量为235.5万m^(3),在降水保证率为75%和95%时灌溉水亏缺量分别为1 754.5万m^(3)和2 261.5万m^(3)。 展开更多
关键词 Sentinel-2影像 种植结构 需水特性 灌溉水供需 引黄灌区
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基于规则的面向对象分类与监督分类对比研究——以WorldView-2影像为例
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作者 薛永福 刘炜 +1 位作者 樊瑶 赵尔平 《四川轻化工大学学报(自然科学版)》 CAS 2023年第4期43-51,共9页
针对传统监督分类方法在处理高分辨率影像时会产生“同物异谱,同谱异物”的现象,本文以杭州市WorldView-2遥感影像数据作为基础数据源,设计了一种基于规则的面向对象分类方法(ROOC)以提取研究区域地物,提高地物分类精度。首先,针对不同... 针对传统监督分类方法在处理高分辨率影像时会产生“同物异谱,同谱异物”的现象,本文以杭州市WorldView-2遥感影像数据作为基础数据源,设计了一种基于规则的面向对象分类方法(ROOC)以提取研究区域地物,提高地物分类精度。首先,针对不同地物的光谱特征差异构建分类规则函数;其次,依据地物图斑在不同分割层下的大小及形状不一致的特征,引入特征波段并参与多尺度分割;最后,将光谱特征分类规则函数与多尺度分割结果相结合构建ROOC方法,通过目视评价和定量分析的方法与MLC、SVM和NNC 3种监督分类结果进行对比。结果表明,ROOC方法的总体分类精度为81.5%,较MLC、SVM、NNC分别提高了10.0%、10.5%、8.0%;总体Kappa系数为0.7685,较MLC、SVM、NNC分别提高了0.1278、0.1295、0.1057。因此,ROOC方法通过将地物光谱特征差异与多尺度分割相结合,能够更准确地识别WorldView-2影像中光谱特征相近的地物,能有效减少地物混分现象,提高分类精度。 展开更多
关键词 worldview-2影像 分类规则函数 多尺度分割 特征增强
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2型糖尿病Gd-EOB-DTPA增强MRI肝肾影像改变
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作者 孙昊洋 张文玉 张文龙 《放射学实践》 CSCD 北大核心 2024年第2期213-217,共5页
目的:探讨2型糖尿病(T2D)患者钆塞酸二钠(Gd-EOB-DTPA)增强MRI肝胆期的肝肾影像改变。方法:搜集82例接受Gd-EOB-DTPA增强MRI腹部检查的患者(肝肾功能正常),其中T2D患者34例(观察组),非T2D患者48例(对照组)。两位医师分别计算肝胆期肝脏... 目的:探讨2型糖尿病(T2D)患者钆塞酸二钠(Gd-EOB-DTPA)增强MRI肝胆期的肝肾影像改变。方法:搜集82例接受Gd-EOB-DTPA增强MRI腹部检查的患者(肝肾功能正常),其中T2D患者34例(观察组),非T2D患者48例(对照组)。两位医师分别计算肝胆期肝脏相对信号强度(RL)、肾皮质和髓质相对信号强度(Rrc、Rrm)、肾皮髓质信号强度的相对差异(Rc-m)。采用独立样本t检验或Mann-Whitney U检验比较观察组与对照组的相关参数差异。选择组间差异有统计学意义的变量,使用受试者工作特征(ROC)曲线分析其鉴别T2D的效能;采用组内相关系数(ICC)评估其可重复性。结果:观察组的RL、Rc-m均较对照组降低,差异有统计学意义(P<0.05),两组的Rrc、Rrm差异均无统计学意义(P>0.05)。RL和Rc-m鉴别T2D的ROC曲线下面积分别为0.78和0.72,诊断敏感度分别为77.10%和64.60%,特异度分别为70.60%和79.40%。RL和Rc-m的观察者间一致性均较好,RL和Rc-m的ICC分别为0.91和0.89。结论:T2D患者Gd-EOB-DTPA增强MRI肝胆期肝肾RL和Rc-m降低。RL和Rc-m在一定条件下可能有助于鉴别T2D,其测量可重复性较好。 展开更多
关键词 2型糖尿病 GD-EOB-DTPA 磁共振成像 肝脏 肾脏
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3D-STI参数评价抗HER-2药物对乳腺癌患者心室功能变化的价值
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作者 勉丽 张茜 王霞 《中国医疗设备》 2024年第3期129-133,145,共6页
目的 探讨三维斑点追踪成像(Three Dimensional Speckle Tracking Imaging,3D-STI)参数评价抗人表皮生长因子受体-2(Human Epidermal Growth Factor Receptor-2,HER-2)药物对乳腺癌患者心室功能变化的价值。方法 选取我院2021年6月至202... 目的 探讨三维斑点追踪成像(Three Dimensional Speckle Tracking Imaging,3D-STI)参数评价抗人表皮生长因子受体-2(Human Epidermal Growth Factor Receptor-2,HER-2)药物对乳腺癌患者心室功能变化的价值。方法 选取我院2021年6月至2022年6月收治的50例需行辅助化疗乳腺癌患者作为观察组,选择同期30名健康志愿者作为对照组。比较对照组与观察组化疗前,化疗第2、4、6周期常规超声心动图指标与3D-STI参数,以临床病理诊断为“金标准”,并绘制3D-STI参数受试者工作特征(Receiver Operating Characteristic,ROC)曲线。结果 观察组化疗第6周期E/A值低于对照组和化疗前(P<0.05),观察组化疗第2、4、6周期左心室扭转、心肌综合指数值低于对照组和化疗前(P<0.05),观察组化疗第4、6周期整体纵向应变(GlobalLongitudinalStrain,GLS)低于对照组和化疗前(P<0.05);ROC曲线显示GLS敏感度最高,为96.00%,整体圆周应变特异性最高,为96.67%。结论 3D-STI参数可评估乳腺癌患者抗HER-2药物化疗后心室功能变化,为临床早期干预提供诊断依据。 展开更多
关键词 乳腺癌 人表皮生长因子受体-2 三维斑点追踪成像 心室功能
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IMAGE 2型糖尿病预防指南要点与点评
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作者 郭艺芳 《中国医学前沿杂志(电子版)》 2010年第3期56-58,共3页
新近,欧洲糖尿病预防指南与培训标准工作组(Development and Implementation of a European Guideline and Training Standards for Diabetes Prevention,IMAGE)颁布了2型糖尿病(T2DM)预防指南,其要点摘译如下:
关键词 预防指南 发病风险 型糖尿病 image 2 IGT IFG OGTT 血糖水平 体质指数 高危人群 易患因素 DM
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Spatiotemporal pharmacometabolomics based on ambient mass spectrometry imaging to evaluate the metabolism and hepatotoxicity of amiodarone in HepG2 spheroids 被引量:3
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作者 Limei Li Qingce Zang +5 位作者 Xinzhu Li Ying Zhu Shanjing Wen Jiuming He Ruiping Zhang Zeper Abliz 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2023年第5期483-493,共11页
Three-dimensional(3D)cell spheroid models combined with mass spectrometry imaging(MSI)enables innovative investigation of in vivo-like biological processes under different physiological and pathological conditions.Her... Three-dimensional(3D)cell spheroid models combined with mass spectrometry imaging(MSI)enables innovative investigation of in vivo-like biological processes under different physiological and pathological conditions.Herein,airflow-assisted desorption electrospray ionization-MSI(AFADESI-MSI)was coupled with 3D HepG2 spheroids to assess the metabolism and hepatotoxicity of amiodarone(AMI).High-coverage imaging of>1100 endogenous metabolites in hepatocyte spheroids was achieved using AFADESI-MSI.Following AMI treatment at different times,15 metabolites of AMI involved in Ndesethylation,hydroxylation,deiodination,and desaturation metabolic reactions were identified,and according to their spatiotemporal dynamics features,the metabolic pathways of AMI were proposed.Subsequently,the temporal and spatial changes in metabolic disturbance within spheroids caused by drug exposure were obtained via metabolomic analysis.The main dysregulated metabolic pathways included arachidonic acid and glycerophospholipid metabolism,providing considerable evidence for the mechanism of AMI hepatotoxicity.In addition,a biomarker group of eight fatty acids was selected that provided improved indication of cell viability and could characterize the hepatotoxicity of AMI.The combination of AFADESI-MSI and HepG2 spheroids can simultaneously obtain spatiotemporal information for drugs,drug metabolites,and endogenous metabolites after AMI treatment,providing an effective tool for in vitro drug hepatotoxicity evaluation. 展开更多
关键词 Mass spectrometry imaging HepG2 spheroids HEPATOTOXICITY Drug metabolism AMIODARONE
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