Visual data mining is one of important approach of data mining techniques. Most of them are based on computer graphic techniques but few of them exploit image-processing techniques. This paper proposes an image proces...Visual data mining is one of important approach of data mining techniques. Most of them are based on computer graphic techniques but few of them exploit image-processing techniques. This paper proposes an image processing method, named RNAM （resemble neighborhood averaging method）, to facilitate visual data mining, which is used to post-process the data mining result-image and help users to discover significant features and useful patterns effectively. The experiments show that the method is intuitive, easily-understanding and effectiveness. It provides a new approach for visual data mining.展开更多
Colorectal cancer(CRC)with lung oligometastases,particularly in the presence of extrapulmonary disease,poses considerable therapeutic challenges in clinical practice.We have carefully studied the multicenter study by ...Colorectal cancer(CRC)with lung oligometastases,particularly in the presence of extrapulmonary disease,poses considerable therapeutic challenges in clinical practice.We have carefully studied the multicenter study by Hu et al,which evaluated the survival outcomes of patients with metastatic CRC who received image-guided thermal ablation(IGTA).These findings provide valuable clinical evidence supporting IGTA as a feasible,minimally invasive approach and underscore the prognostic significance of metastatic distribution.However,the study by Hu et al has several limitations,including that not all pulmonary lesions were pathologically confirmed,postoperative follow-up mainly relied on dynamic contrast-enhanced computed tomography,no comparative analysis was performed with other local treatments,and the impact of other imaging features on efficacy and prognosis was not evaluated.Future studies should include complete pathological confirmation,integrate functional imaging and radiomics,and use prospective multicenter collaboration to optimize patient selection standards for IGTA treatment,strengthen its clinical evidence base,and ultimately promote individualized decision-making for patients with metastatic CRC.展开更多
Automated segmentation of histopathological images is a challenging task to detect cancerous cells in breast tissue.Recent reviews state high accuracy to segment image,but depends on user input,say window area size,ti...Automated segmentation of histopathological images is a challenging task to detect cancerous cells in breast tissue.Recent reviews state high accuracy to segment image,but depends on user input,say window area size,time steps,level set,magnification factor and so on.To extract the region of interest effectively,the subject expert performs post-processing operations several times on the segmentation results with different input values for different parameters say,area opening,fill holes and selects most appropriate enhanced image required for further analysis.The authors proposed an automated segmentation technique followed by self-driven post-processing operations to detect cancerous cells effectively.The post-processing method itself determines the value of different parameters for different operations based on segmented results obtained.The proposed technique has the following features:(i)technique is context sensitive;(ii)no prior setting of time step,weighted area coefficient parameters is required;(iii)magnification independent;(iv)post-processing operations are self-driven which enhance segmentation results adaptively.The experimental results are compared with four state-of-the-art techniques:fuzzy C-means,spatial fuzzy C-means,spatial neutrosophic distance regularised level set and convolutional neural network-based PangNet.Experimental results obtained on two publicly available data sets show that the proposed technique outperforms effectively.展开更多
Low contrast of Magnetic Resonance(MR)images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis.State-of-the-art contrast boosting techniques intole...Low contrast of Magnetic Resonance(MR)images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis.State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images.Drastic changes in brightness features,induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings.To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well.This method termed as Power-law and Logarithmic Modification-based Histogram Equalization(PLMHE)partitions the histogram of the image into two sub histograms after a power-law transformation and a log compression.After a modification intended for improving the dispersion of the sub-histograms and subsequent normalization,cumulative histograms are computed.Enhanced grey level values are computed from the resultant cumulative histograms.The performance of the PLMHE algorithm is comparedwith traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression,a significant change in mean brightness,and contrast-overshoot.展开更多
This research paper recommends the point spread function(PSF)forecasting technique based on the projection onto convex set(POCS)and regularization to acquire low resolution images.As the environment for the production...This research paper recommends the point spread function(PSF)forecasting technique based on the projection onto convex set(POCS)and regularization to acquire low resolution images.As the environment for the production of user created contents(UCC)videos(one of the contents on the Internet)becomes widespread,resolution reduction and image distortion occurs,failing to satisfy users who desire high quality images.Accordingly,this research neutralizes the coding artifact through POCS and regularization processes by:1)factoring the local characteristics of the image when it comes to the noise that results during the discrete cosine transform(DCT)and quantization process;and 2)removing the blocking and ring phenomena which are problems with the existing video compression.Moreover,this research forecasts the point spread function to obtain low resolution images using the above-mentioned methods.Thus,a method is suggested for minimizing the errors found among the forecasting interpolation pixels.Low-resolution image quality obtained through the experiment demonstrates that significant enhancement was made on the visual level compared to the original image.展开更多
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
Image segmentation is attracting increasing attention in the field of medical image analysis.Since widespread utilization across various medical applications,ensuring and improving segmentation accuracy has become a c...Image segmentation is attracting increasing attention in the field of medical image analysis.Since widespread utilization across various medical applications,ensuring and improving segmentation accuracy has become a crucial topic of research.With advances in deep learning,researchers have developed numerous methods that combine Transformers and convolutional neural networks(CNNs)to create highly accurate models for medical image segmentation.However,efforts to further enhance accuracy by developing larger and more complex models or training with more extensive datasets,significantly increase computational resource consumption.To address this problem,we propose BiCLIP-nnFormer(the prefix"Bi"refers to the use of two distinct CLIP models),a virtual multimodal instrument that leverages CLIP models to enhance the segmentation performance of a medical segmentation model nnFormer.Since two CLIP models(PMC-CLIP and CoCa-CLIP)are pre-trained on large datasets,they do not require additional training,thus conserving computation resources.These models are used offline to extract image and text embeddings from medical images.These embeddings are then processed by the proposed 3D CLIP adapter,which adapts the CLIP knowledge for segmentation tasks by fine-tuning.Finally,the adapted embeddings are fused with feature maps extracted from the nnFormer encoder for generating predicted masks.This process enriches the representation capabilities of the feature maps by integrating global multimodal information,leading to more precise segmentation predictions.We demonstrate the superiority of BiCLIP-nnFormer and the effectiveness of using CLIP models to enhance nnFormer through experiments on two public datasets,namely the Synapse multi-organ segmentation dataset(Synapse)and the Automatic Cardiac Diagnosis Challenge dataset(ACDC),as well as a self-annotated lung multi-category segmentation dataset(LMCS).展开更多
Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time perfor...Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.展开更多
Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventi...Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective.In addition,the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry,which makes it difficult to accurately identify fractures.In this paper,the electrical image logs network(EILnet)da deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion moduledwas created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images.Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model.Various image-processing tools,including the bilateral filter,Laplace operator,and Gaussian low-pass filter,were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures.The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models,such as Fully Convolutional Networks(FCN-8s),U-Net,and SegNet,for both the single-channel dataset and the multi-attribute dataset.The EILnet provided significant advantages for the single-channel dataset,and its mean intersection over union(MIoU)and pixel accuracy(PA)were 81.32%and 89.37%,respectively.In the case of the multi-attribute dataset,the identification capability of all models improved to varying degrees,with the EILnet achieving the highest MIoU and PA of 83.43%and 91.11%,respectively.Further,applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification,thereby indicating its promising potential applications.展开更多
The presence of a positive deep surgical margin in tongue squamous cell carcinoma(TSCC)significantly elevates the risk of local recurrence.Therefore,a prompt and precise intraoperative assessment of margin status is i...The presence of a positive deep surgical margin in tongue squamous cell carcinoma(TSCC)significantly elevates the risk of local recurrence.Therefore,a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection.In this study,we integrate Raman imaging technology with an artificial intelligence(AI)generative model,proposing an innovative approach for intraoperative margin status diagnosis.This method utilizes Raman imaging to swiftly and non-invasively capture tissue Raman images,which are then transformed into hematoxylin-eosin(H&E)-stained histopathological images using an AI generative model for histopathological diagnosis.The generated H&E-stained images clearly illustrate the tissue’s pathological conditions.Independently reviewed by three pathologists,the overall diagnostic accuracy for distinguishing between tumor tissue and normal muscle tissue reaches 86.7%.Notably,it outperforms current clinical practices,especially in TSCC with positive lymph node metastasis or moderately differentiated grades.This advancement highlights the potential of AI-enhanced Raman imaging to significantly improve intraoperative assessments and surgical margin evaluations,promising a versatile diagnostic tool beyond TSCC.展开更多
The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions a...The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed information.So we propose a rendered image denoising method with filtering guided by lighting information.First,we design an image segmentation algorithm based on lighting information to segment the image into different illumination areas.Then,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination areas.For different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area filtering.Finally,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the image.Under the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on average.This shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.展开更多
The application of deep learning for target detection in aerial images captured by Unmanned Aerial Vehicles(UAV)has emerged as a prominent research focus.Due to the considerable distance between UAVs and the photograp...The application of deep learning for target detection in aerial images captured by Unmanned Aerial Vehicles(UAV)has emerged as a prominent research focus.Due to the considerable distance between UAVs and the photographed objects,coupled with complex shooting environments,existing models often struggle to achieve accurate real-time target detection.In this paper,a You Only Look Once v8(YOLOv8)model is modified from four aspects:the detection head,the up-sampling module,the feature extraction module,and the parameter optimization of positive sample screening,and the YOLO-S3DT model is proposed to improve the performance of the model for detecting small targets in aerial images.Experimental results show that all detection indexes of the proposed model are significantly improved without increasing the number of model parameters and with the limited growth of computation.Moreover,this model also has the best performance compared to other detecting models,demonstrating its advancement within this category of tasks.展开更多
Indirect X-ray modulation imaging has been adopted in a number of solar missions and provided reconstructed X-ray images of solar flares that are of great scientific importance.However,the assessment of the image qual...Indirect X-ray modulation imaging has been adopted in a number of solar missions and provided reconstructed X-ray images of solar flares that are of great scientific importance.However,the assessment of the image quality of the reconstruction is still difficult,which is particularly useful for scheme design of X-ray imaging systems,testing and improvement of imaging algorithms,and scientific research of X-ray sources.Currently,there is no specified method to quantitatively evaluate the quality of X-ray image reconstruction and the point-spread function(PSF)of an X-ray imager.In this paper,we propose percentage proximity degree(PPD)by considering the imaging characteristics of X-ray image reconstruction and in particular,sidelobes and their effects on imaging quality.After testing a variety of imaging quality assessments in six aspects,we utilized the technique for order preference by similarity to ideal solution to the indices that meet the requirements.Then we develop the final quality index for X-ray image reconstruction,QuIX,which consists of the selected indices and the new PPD.QuIX performs well in a series of tests,including assessment of instrument PSF and simulation tests under different grid configurations,as well as imaging tests with RHESSI data.It is also a useful tool for testing of imaging algorithms,and determination of imaging parameters for both RHESSI and ASO-S/Hard X-ray Imager,such as field of view,beam width factor,and detector selection.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
Imaging observations of solar X-ray bursts can reveal details of the energy release process and particle acceleration in flares.Most hard X-ray imagers make use of the modulation-based Fourier transform imaging method...Imaging observations of solar X-ray bursts can reveal details of the energy release process and particle acceleration in flares.Most hard X-ray imagers make use of the modulation-based Fourier transform imaging method,an indirect imaging technique that requires algorithms to reconstruct and optimize images.During the last decade,a variety of algorithms have been developed and improved.However,it is difficult to quantitatively evaluate the image quality of different solutions without a true,reference image of observation.How to choose the values of imaging parameters for these algorithms to get the best performance is also an open question.In this study,we present a detailed test of the characteristics of these algorithms,imaging dynamic range and a crucial parameter for the CLEAN method,clean beam width factor(CBWF).We first used SDO/AIA EUV images to compute DEM maps and calculate thermal X-ray maps.Then these realistic sources and several types of simulated sources are used as the ground truth in the imaging simulations for both RHESSI and ASO-S/HXI.The different solutions are evaluated quantitatively by a number of means.The overall results suggest that EM,PIXON,and CLEAN are exceptional methods for sidelobe elimination,producing images with clear source details.Although MEM_GE,MEM_NJIT,VIS_WV and VIS_CS possess fast imaging processes and generate good images,they too possess associated imperfections unique to each method.The two forward fit algorithms,VF and FF,perform differently,and VF appears to be more robust and useful.We also demonstrated the imaging capability of HXI and available HXI algorithms.Furthermore,the effect of CBWF on image quality was investigated,and the optimal settings for both RHESSI and HXI were proposed.展开更多
Large language models(LLMs),such as ChatGPT developed by OpenAI,represent a significant advancement in artificial intelligence(AI),designed to understand,generate,and interpret human language by analyzing extensive te...Large language models(LLMs),such as ChatGPT developed by OpenAI,represent a significant advancement in artificial intelligence(AI),designed to understand,generate,and interpret human language by analyzing extensive text data.Their potential integration into clinical settings offers a promising avenue that could transform clinical diagnosis and decision-making processes in the future(Thirunavukarasu et al.,2023).This article aims to provide an in-depth analysis of LLMs’current and potential impact on clinical practices.Their ability to generate differential diagnosis lists underscores their potential as invaluable tools in medical practice and education(Hirosawa et al.,2023;Koga et al.,2023).展开更多
The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method f...The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.展开更多
Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps i...Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps is influenced by the representations of background images and map symbols.Many researchers explored the optimizations for background images and symbolization techniques for symbols to reduce the complexity of image-maps and improve the usability.However,little literature was found for the optimum amount of symbol loading.This study focuses on the effects of background image complexity and map symbol load on the usability(i.e.,effectiveness and efficiency)of image-maps.Experiments were conducted by user studies via eye-tracking equipment and an online questionnaire survey.Experimental data sets included image-maps with ten levels of map symbol load in ten areas.Forty volunteers took part in the target searching experiments.It has been found that the usability,i.e.,average time viewed(efficiency)and average revisits(effectiveness)of targets recorded,is influenced by the complexity of background images,a peak exists for optimum symbol load for an image-map.The optimum levels for symbol load for different image-maps also have a peak when the complexity of the background image/image map increases.The complexity of background images serves as a guideline for optimum map symbol load in image-map design.This study enhanced user experience by optimizing visual clarity and managing cognitive load.Understanding how these factors interact can help create adaptive maps that maintain clarity and usability,guiding AI algorithms to adjust symbol density based on user context.This research establishes the practices for map design,making cartographic tools more innovative and more user-centric.展开更多
The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV imag...The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV images,which is inspired by the Retinex theory and guided by a light weighted map.Firstly,we propose a new network for reflectance component processing to suppress the noise in images.Secondly,we construct an illumination enhancement module that uses a light weighted map to guide the enhancement process.Finally,the processed reflectance and illumination components are recombined to obtain the enhancement results.Experimental results show that our method can suppress the noise in images while enhancing image brightness,and prevent over enhancement in bright regions.Code and data are available at https://gitee.com/baixiaotong2/uav-images.git.展开更多
基金Supported by the National Natural Science Foun-dation of China (60173051) ,the Teaching and Research Award Pro-gramfor Outstanding Young Teachers in Higher Education Institu-tions of Ministry of Education of China ,and Liaoning Province HigherEducation Research Foundation (20040206)
文摘Visual data mining is one of important approach of data mining techniques. Most of them are based on computer graphic techniques but few of them exploit image-processing techniques. This paper proposes an image processing method, named RNAM （resemble neighborhood averaging method）, to facilitate visual data mining, which is used to post-process the data mining result-image and help users to discover significant features and useful patterns effectively. The experiments show that the method is intuitive, easily-understanding and effectiveness. It provides a new approach for visual data mining.
文摘Colorectal cancer(CRC)with lung oligometastases,particularly in the presence of extrapulmonary disease,poses considerable therapeutic challenges in clinical practice.We have carefully studied the multicenter study by Hu et al,which evaluated the survival outcomes of patients with metastatic CRC who received image-guided thermal ablation(IGTA).These findings provide valuable clinical evidence supporting IGTA as a feasible,minimally invasive approach and underscore the prognostic significance of metastatic distribution.However,the study by Hu et al has several limitations,including that not all pulmonary lesions were pathologically confirmed,postoperative follow-up mainly relied on dynamic contrast-enhanced computed tomography,no comparative analysis was performed with other local treatments,and the impact of other imaging features on efficacy and prognosis was not evaluated.Future studies should include complete pathological confirmation,integrate functional imaging and radiomics,and use prospective multicenter collaboration to optimize patient selection standards for IGTA treatment,strengthen its clinical evidence base,and ultimately promote individualized decision-making for patients with metastatic CRC.
文摘Automated segmentation of histopathological images is a challenging task to detect cancerous cells in breast tissue.Recent reviews state high accuracy to segment image,but depends on user input,say window area size,time steps,level set,magnification factor and so on.To extract the region of interest effectively,the subject expert performs post-processing operations several times on the segmentation results with different input values for different parameters say,area opening,fill holes and selects most appropriate enhanced image required for further analysis.The authors proposed an automated segmentation technique followed by self-driven post-processing operations to detect cancerous cells effectively.The post-processing method itself determines the value of different parameters for different operations based on segmented results obtained.The proposed technique has the following features:(i)technique is context sensitive;(ii)no prior setting of time step,weighted area coefficient parameters is required;(iii)magnification independent;(iv)post-processing operations are self-driven which enhance segmentation results adaptively.The experimental results are compared with four state-of-the-art techniques:fuzzy C-means,spatial fuzzy C-means,spatial neutrosophic distance regularised level set and convolutional neural network-based PangNet.Experimental results obtained on two publicly available data sets show that the proposed technique outperforms effectively.
基金This work was supported by Taif university Researchers Supporting Project Number(TURSP-2020/114),Taif University,Taif,Saudi Arabia.
文摘Low contrast of Magnetic Resonance(MR)images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis.State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images.Drastic changes in brightness features,induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings.To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well.This method termed as Power-law and Logarithmic Modification-based Histogram Equalization(PLMHE)partitions the histogram of the image into two sub histograms after a power-law transformation and a log compression.After a modification intended for improving the dispersion of the sub-histograms and subsequent normalization,cumulative histograms are computed.Enhanced grey level values are computed from the resultant cumulative histograms.The performance of the PLMHE algorithm is comparedwith traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression,a significant change in mean brightness,and contrast-overshoot.
基金The MKE(the Ministry of Knowledge Economy),Korea,under the ITRC(Information Technology Research Center)support program supervised by the NIPA(National IT Industry Promotion Agency) (NIPA-2012-H0301-12-2006)
文摘This research paper recommends the point spread function(PSF)forecasting technique based on the projection onto convex set(POCS)and regularization to acquire low resolution images.As the environment for the production of user created contents(UCC)videos(one of the contents on the Internet)becomes widespread,resolution reduction and image distortion occurs,failing to satisfy users who desire high quality images.Accordingly,this research neutralizes the coding artifact through POCS and regularization processes by:1)factoring the local characteristics of the image when it comes to the noise that results during the discrete cosine transform(DCT)and quantization process;and 2)removing the blocking and ring phenomena which are problems with the existing video compression.Moreover,this research forecasts the point spread function to obtain low resolution images using the above-mentioned methods.Thus,a method is suggested for minimizing the errors found among the forecasting interpolation pixels.Low-resolution image quality obtained through the experiment demonstrates that significant enhancement was made on the visual level compared to the original image.
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funded by the National Natural Science Foundation of China(Grant No.6240072655)the Hubei Provincial Key Research and Development Program(Grant No.2023BCB151)+1 种基金the Wuhan Natural Science Foundation Exploration Program(Chenguang Program,Grant No.2024040801020202)the Natural Science Foundation of Hubei Province of China(Grant No.2025AFB148).
文摘Image segmentation is attracting increasing attention in the field of medical image analysis.Since widespread utilization across various medical applications,ensuring and improving segmentation accuracy has become a crucial topic of research.With advances in deep learning,researchers have developed numerous methods that combine Transformers and convolutional neural networks(CNNs)to create highly accurate models for medical image segmentation.However,efforts to further enhance accuracy by developing larger and more complex models or training with more extensive datasets,significantly increase computational resource consumption.To address this problem,we propose BiCLIP-nnFormer(the prefix"Bi"refers to the use of two distinct CLIP models),a virtual multimodal instrument that leverages CLIP models to enhance the segmentation performance of a medical segmentation model nnFormer.Since two CLIP models(PMC-CLIP and CoCa-CLIP)are pre-trained on large datasets,they do not require additional training,thus conserving computation resources.These models are used offline to extract image and text embeddings from medical images.These embeddings are then processed by the proposed 3D CLIP adapter,which adapts the CLIP knowledge for segmentation tasks by fine-tuning.Finally,the adapted embeddings are fused with feature maps extracted from the nnFormer encoder for generating predicted masks.This process enriches the representation capabilities of the feature maps by integrating global multimodal information,leading to more precise segmentation predictions.We demonstrate the superiority of BiCLIP-nnFormer and the effectiveness of using CLIP models to enhance nnFormer through experiments on two public datasets,namely the Synapse multi-organ segmentation dataset(Synapse)and the Automatic Cardiac Diagnosis Challenge dataset(ACDC),as well as a self-annotated lung multi-category segmentation dataset(LMCS).
基金supported by the National Key Research and Development Project of China(No.2023YFB3709605)the National Natural Science Foundation of China(No.62073193)the National College Student Innovation Training Program(No.202310422122)。
文摘Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.
基金the National Natural Science Foundation of China(42472194,42302153,and 42002144)the Fundamental Research Funds for the Central Univer-sities(22CX06002A).
文摘Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective.In addition,the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry,which makes it difficult to accurately identify fractures.In this paper,the electrical image logs network(EILnet)da deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion moduledwas created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images.Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model.Various image-processing tools,including the bilateral filter,Laplace operator,and Gaussian low-pass filter,were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures.The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models,such as Fully Convolutional Networks(FCN-8s),U-Net,and SegNet,for both the single-channel dataset and the multi-attribute dataset.The EILnet provided significant advantages for the single-channel dataset,and its mean intersection over union(MIoU)and pixel accuracy(PA)were 81.32%and 89.37%,respectively.In the case of the multi-attribute dataset,the identification capability of all models improved to varying degrees,with the EILnet achieving the highest MIoU and PA of 83.43%and 91.11%,respectively.Further,applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification,thereby indicating its promising potential applications.
基金supported by the National Natural Science Foundation of China(Grant Nos.82272955 and 22203057)the Natural Science Foundation of Fujian Province(Grant No.2021J011361).
文摘The presence of a positive deep surgical margin in tongue squamous cell carcinoma(TSCC)significantly elevates the risk of local recurrence.Therefore,a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection.In this study,we integrate Raman imaging technology with an artificial intelligence(AI)generative model,proposing an innovative approach for intraoperative margin status diagnosis.This method utilizes Raman imaging to swiftly and non-invasively capture tissue Raman images,which are then transformed into hematoxylin-eosin(H&E)-stained histopathological images using an AI generative model for histopathological diagnosis.The generated H&E-stained images clearly illustrate the tissue’s pathological conditions.Independently reviewed by three pathologists,the overall diagnostic accuracy for distinguishing between tumor tissue and normal muscle tissue reaches 86.7%.Notably,it outperforms current clinical practices,especially in TSCC with positive lymph node metastasis or moderately differentiated grades.This advancement highlights the potential of AI-enhanced Raman imaging to significantly improve intraoperative assessments and surgical margin evaluations,promising a versatile diagnostic tool beyond TSCC.
基金supported by the National Natural Science(No.U19A2063)the Jilin Provincial Development Program of Science and Technology (No.20230201080GX)the Jilin Province Education Department Scientific Research Project (No.JJKH20230851KJ)。
文摘The visual noise of each light intensity area is different when the image is drawn by Monte Carlo method.However,the existing denoising algorithms have limited denoising performance under complex lighting conditions and are easy to lose detailed information.So we propose a rendered image denoising method with filtering guided by lighting information.First,we design an image segmentation algorithm based on lighting information to segment the image into different illumination areas.Then,we establish the parameter prediction model guided by lighting information for filtering(PGLF)to predict the filtering parameters of different illumination areas.For different illumination areas,we use these filtering parameters to construct area filters,and the filters are guided by the lighting information to perform sub-area filtering.Finally,the filtering results are fused with auxiliary features to output denoised images for improving the overall denoising effect of the image.Under the physically based rendering tool(PBRT)scene and Tungsten dataset,the experimental results show that compared with other guided filtering denoising methods,our method improves the peak signal-to-noise ratio(PSNR)metrics by 4.2164 dB on average and the structural similarity index(SSIM)metrics by 7.8%on average.This shows that our method can better reduce the noise in complex lighting scenesand improvethe imagequality.
文摘The application of deep learning for target detection in aerial images captured by Unmanned Aerial Vehicles(UAV)has emerged as a prominent research focus.Due to the considerable distance between UAVs and the photographed objects,coupled with complex shooting environments,existing models often struggle to achieve accurate real-time target detection.In this paper,a You Only Look Once v8(YOLOv8)model is modified from four aspects:the detection head,the up-sampling module,the feature extraction module,and the parameter optimization of positive sample screening,and the YOLO-S3DT model is proposed to improve the performance of the model for detecting small targets in aerial images.Experimental results show that all detection indexes of the proposed model are significantly improved without increasing the number of model parameters and with the limited growth of computation.Moreover,this model also has the best performance compared to other detecting models,demonstrating its advancement within this category of tasks.
基金supported by the National Natural Science Foundation of China(NSFC)12333010the National Key R&D Program of China 2022YFF0503002+3 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(grant No.XDB0560000)the NSFC 11921003supported by the Prominent Postdoctoral Project of Jiangsu Province(2023ZB304)supported by the Strategic Priority Research Program on Space Science,the Chinese Academy of Sciences,grant No.XDA15320000.
文摘Indirect X-ray modulation imaging has been adopted in a number of solar missions and provided reconstructed X-ray images of solar flares that are of great scientific importance.However,the assessment of the image quality of the reconstruction is still difficult,which is particularly useful for scheme design of X-ray imaging systems,testing and improvement of imaging algorithms,and scientific research of X-ray sources.Currently,there is no specified method to quantitatively evaluate the quality of X-ray image reconstruction and the point-spread function(PSF)of an X-ray imager.In this paper,we propose percentage proximity degree(PPD)by considering the imaging characteristics of X-ray image reconstruction and in particular,sidelobes and their effects on imaging quality.After testing a variety of imaging quality assessments in six aspects,we utilized the technique for order preference by similarity to ideal solution to the indices that meet the requirements.Then we develop the final quality index for X-ray image reconstruction,QuIX,which consists of the selected indices and the new PPD.QuIX performs well in a series of tests,including assessment of instrument PSF and simulation tests under different grid configurations,as well as imaging tests with RHESSI data.It is also a useful tool for testing of imaging algorithms,and determination of imaging parameters for both RHESSI and ASO-S/Hard X-ray Imager,such as field of view,beam width factor,and detector selection.
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 Key R&D Program of China 2022YFF0503002the National Natural Science Foundation of China(NSFC,Grant Nos.12333010 and 12233012)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(grant No.XDB0560000)supported by the Prominent Postdoctoral Project of Jiangsu Province(2023ZB304)supported by the Strategic Priority Research Program on Space Science,the Chinese Academy of Sciences,grant No.XDA15320000.
文摘Imaging observations of solar X-ray bursts can reveal details of the energy release process and particle acceleration in flares.Most hard X-ray imagers make use of the modulation-based Fourier transform imaging method,an indirect imaging technique that requires algorithms to reconstruct and optimize images.During the last decade,a variety of algorithms have been developed and improved.However,it is difficult to quantitatively evaluate the image quality of different solutions without a true,reference image of observation.How to choose the values of imaging parameters for these algorithms to get the best performance is also an open question.In this study,we present a detailed test of the characteristics of these algorithms,imaging dynamic range and a crucial parameter for the CLEAN method,clean beam width factor(CBWF).We first used SDO/AIA EUV images to compute DEM maps and calculate thermal X-ray maps.Then these realistic sources and several types of simulated sources are used as the ground truth in the imaging simulations for both RHESSI and ASO-S/HXI.The different solutions are evaluated quantitatively by a number of means.The overall results suggest that EM,PIXON,and CLEAN are exceptional methods for sidelobe elimination,producing images with clear source details.Although MEM_GE,MEM_NJIT,VIS_WV and VIS_CS possess fast imaging processes and generate good images,they too possess associated imperfections unique to each method.The two forward fit algorithms,VF and FF,perform differently,and VF appears to be more robust and useful.We also demonstrated the imaging capability of HXI and available HXI algorithms.Furthermore,the effect of CBWF on image quality was investigated,and the optimal settings for both RHESSI and HXI were proposed.
文摘Large language models(LLMs),such as ChatGPT developed by OpenAI,represent a significant advancement in artificial intelligence(AI),designed to understand,generate,and interpret human language by analyzing extensive text data.Their potential integration into clinical settings offers a promising avenue that could transform clinical diagnosis and decision-making processes in the future(Thirunavukarasu et al.,2023).This article aims to provide an in-depth analysis of LLMs’current and potential impact on clinical practices.Their ability to generate differential diagnosis lists underscores their potential as invaluable tools in medical practice and education(Hirosawa et al.,2023;Koga et al.,2023).
基金Supported by the Henan Province Key Research and Development Project(231111211300)the Central Government of Henan Province Guides Local Science and Technology Development Funds(Z20231811005)+2 种基金Henan Province Key Research and Development Project(231111110100)Henan Provincial Outstanding Foreign Scientist Studio(GZS2024006)Henan Provincial Joint Fund for Scientific and Technological Research and Development Plan(Application and Overcoming Technical Barriers)(242103810028)。
文摘The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.
基金National Natural Science Foundation of China(No.42301518)Hubei Key Laboratory of Regional Development and Environmental Response(No.2023(A)002)Key Laboratory of the Evaluation and Monitoring of Southwest Land Resources(Ministry of Education)(No.TDSYS202304).
文摘Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps is influenced by the representations of background images and map symbols.Many researchers explored the optimizations for background images and symbolization techniques for symbols to reduce the complexity of image-maps and improve the usability.However,little literature was found for the optimum amount of symbol loading.This study focuses on the effects of background image complexity and map symbol load on the usability(i.e.,effectiveness and efficiency)of image-maps.Experiments were conducted by user studies via eye-tracking equipment and an online questionnaire survey.Experimental data sets included image-maps with ten levels of map symbol load in ten areas.Forty volunteers took part in the target searching experiments.It has been found that the usability,i.e.,average time viewed(efficiency)and average revisits(effectiveness)of targets recorded,is influenced by the complexity of background images,a peak exists for optimum symbol load for an image-map.The optimum levels for symbol load for different image-maps also have a peak when the complexity of the background image/image map increases.The complexity of background images serves as a guideline for optimum map symbol load in image-map design.This study enhanced user experience by optimizing visual clarity and managing cognitive load.Understanding how these factors interact can help create adaptive maps that maintain clarity and usability,guiding AI algorithms to adjust symbol density based on user context.This research establishes the practices for map design,making cartographic tools more innovative and more user-centric.
基金supported by the National Natural Science Foundation of China(Nos.62201454 and 62306235)the Xi’an Science and Technology Program of Xi’an Science and Technology Bureau(No.23SFSF0004)。
文摘The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV images,which is inspired by the Retinex theory and guided by a light weighted map.Firstly,we propose a new network for reflectance component processing to suppress the noise in images.Secondly,we construct an illumination enhancement module that uses a light weighted map to guide the enhancement process.Finally,the processed reflectance and illumination components are recombined to obtain the enhancement results.Experimental results show that our method can suppress the noise in images while enhancing image brightness,and prevent over enhancement in bright regions.Code and data are available at https://gitee.com/baixiaotong2/uav-images.git.
