Important in many different sectors of the industry, the determination of stream velocity has become more and more important due to measurements precision necessity, in order to determine the right production rates, d...Important in many different sectors of the industry, the determination of stream velocity has become more and more important due to measurements precision necessity, in order to determine the right production rates, determine the volumetric production of undesired fluid, establish automated controls based on these measurements avoiding over-flooding or over-production, guaranteeing accurate predictive maintenance, etc. Difficulties being faced have been the determination of the velocity of specific fluids embedded in some others, for example, determining the gas bubbles stream velocity flowing throughout liquid fluid phase. Although different and already applicable methods have been researched and already implemented within the industry, a non-intrusive automated way of providing those stream velocities has its importance, and may have a huge impact in projects budget. Knowing the importance of its determination, this developed script uses a methodology of breaking-down real-time videos media into frame images, analyzing by pixel correlations possible superposition matches for further gas bubbles stream velocity estimation. In raw sense, the script bases itself in functions and procedures already available in MatLab, which can be used for image processing and treatments, allowing the methodology to be implemented. Its accuracy after the running test was of around 97% (ninety-seven percent);the raw source code with comments had almost 3000 (three thousand) characters;and the hardware placed for running the code was an Intel Core Duo 2.13 [Ghz] and 2 [Gb] RAM memory capable workstation. Even showing good results, it could be stated that just the end point correlations were actually getting to the final solution. So that, making use of self-learning functions or neural network, one could surely enhance the capability of the application to be run in real-time without getting exhaust by iterative loops.展开更多
Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometri...Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometric observations,outliers may exist in the obtained light curves due to various reasons.Therefore,preprocessing is required to remove these outliers to obtain high quality light curves.Through statistical analysis,the reasons leading to outliers can be categorized into two main types:first,the brightness of the object significantly increases due to the passage of a star nearby,referred to as“stellar contamination,”and second,the brightness markedly decreases due to cloudy cover,referred to as“cloudy contamination.”The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive.However,we propose the utilization of machine learning methods as a substitute.Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination,achieving F1 scores of 1.00 and 0.98 on a test set,respectively.We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine,then conduct comparative analyses of the results.展开更多
In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But...In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But they still face challenges in terms of computational requirements,overfitting and generalization issues,etc.To resolve such issues,optimization algorithms provide greater control and transparency in designing digital filters for image enhancement and denoising.Therefore,this paper presented a novel denoising approach for medical applications using an Optimized Learning⁃based Multi⁃level discrete Wavelet Cascaded Convolutional Neural Network(OLMWCNN).In this approach,the optimal filter parameters are identified to preserve the image quality after denoising.The performance and efficiency of the OLMWCNN filter are evaluated,demonstrating significant progress in denoising medical images while overcoming the limitations of conventional methods.展开更多
Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to ...Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone;however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with tremendous accuracy. Therefore, in this study, we proposed a novel feature extractor framework associated with a supervised three-class XGBoost algorithm for the detection of osteosarcoma in whole slide histopathology images. This method allows for quicker and more effective data analysis. The first step involves preprocessing the imbalanced histopathology dataset, followed by augmentation and balancing utilizing two techniques: SMOTE and ADASYN. Next, a unique feature extraction framework is used to extract features, which are then inputted into the supervised three-class XGBoost algorithm for classification into three categories: non-tumor, viable tumor, and non-viable tumor. The experimental findings indicate that the proposed model exhibits superior efficiency, accuracy, and a more lightweight design in comparison to other current models for osteosarcoma detection.展开更多
Hematoxylin and Eosin(H&E)images,popularly used in the field of digital pathology,often pose challenges due to their limited color richness,hindering the differentiation of subtle cell features crucial for accurat...Hematoxylin and Eosin(H&E)images,popularly used in the field of digital pathology,often pose challenges due to their limited color richness,hindering the differentiation of subtle cell features crucial for accurate classification.Enhancing the visibility of these elusive cell features helps train robust deep-learning models.However,the selection and application of image processing techniques for such enhancement have not been systematically explored in the research community.To address this challenge,we introduce Salient Features Guided Augmentation(SFGA),an approach that strategically integrates machine learning and image processing.SFGA utilizes machine learning algorithms to identify crucial features within cell images,subsequently mapping these features to appropriate image processing techniques to enhance training images.By emphasizing salient features and aligning them with corresponding image processing methods,SFGA is designed to enhance the discriminating power of deep learning models in cell classification tasks.Our research undertakes a series of experiments,each exploring the performance of different datasets and data enhancement techniques in classifying cell types,highlighting the significance of data quality and enhancement in mitigating overfitting and distinguishing cell characteristics.Specifically,SFGA focuses on identifying tumor cells from tissue for extranodal extension detection,with the SFGA-enhanced dataset showing notable advantages in accuracy.We conducted a preliminary study of five experiments,among which the accuracy of the pleomorphism experiment improved significantly from 50.81%to 95.15%.The accuracy of the other four experiments also increased,with improvements ranging from 3 to 43 percentage points.Our preliminary study shows the possibilities to enhance the diagnostic accuracy of deep learning models and proposes a systematic approach that could enhance cancer diagnosis,contributing as a first step in using SFGA in medical image enhancement.展开更多
Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced ima...Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities.However,existing methodologies face persistent challenges,including low image contrast,noise interference,and inaccuracies in segmenting regions of interest.To address these limitations,this study introduces a novel computational framework for analyzing mammographic images,evaluated using the Mammographic Image Analysis Society(MIAS)dataset comprising 322 samples.The proposed methodology follows a structured three-stage approach.Initially,mammographic scans are classified using the Breast Imaging Reporting and Data System(BI-RADS),ensuring systematic and standardized image analysis.Next,the pectoral muscle,which can interfere with accurate segmentation,is effectively removed to refine the region of interest(ROI).The final stage involves an advanced image pre-processing module utilizing Independent Component Analysis(ICA)to enhance contrast,suppress noise,and improve image clarity.Following these enhancements,a robust segmentation technique is employed to delineated abnormal regions.Experimental results validate the efficiency of the proposed framework,demonstrating a significant improvement in the Effective Measure of Enhancement(EME)and a 3 dB increase in Peak Signal-to-Noise Ratio(PSNR),indicating superior image quality.The model also achieves an accuracy of approximately 97%,surpassing contemporary techniques evaluated on the MIAS dataset.Furthermore,its ability to process mammograms across all BI-RADS categories highlights its adaptability and reliability for clinical applications.This study presents an advanced and dependable computational framework for mammographic image analysis,effectively addressing critical challenges in noise reduction,contrast enhancement,and segmentation precision.The proposed approach lays the groundwork for seamless integration into computer-aided diagnostic(CAD)systems,with the potential to significantly enhance early breast cancer detection and contribute to improved patient outcomes.展开更多
Carotid artery plaques represent a major contributor to the morbidity and mortality associated with cerebrovascular disease,and their clinical significance is largely determined by the risk linked to plaque vulnerabil...Carotid artery plaques represent a major contributor to the morbidity and mortality associated with cerebrovascular disease,and their clinical significance is largely determined by the risk linked to plaque vulnerability.Therefore,classifying plaque risk constitutes one of themost critical tasks in the clinicalmanagement of this condition.While classification models derived from individual medical centers have been extensively investigated,these singlecenter models often fail to generalize well to multi-center data due to variations in ultrasound images caused by differences in physician expertise and equipment.To address this limitation,a Dual-Classifier Label Correction Networkmodel(DCLCN)is proposed for the classification of carotid plaque ultrasound images acrossmultiplemedical centers.TheDCLCNdesigns amulti-center domain adaptationmodule that leverages a dual-classifier strategy to extract knowledge from both source and target centers,thereby reducing feature discrepancies through a domain adaptation layer.Additionally,to mitigate the impact of image noise,a label modeling and correction module is introduced to generate pseudo-labels for the target centers and iteratively refine them using an end-to-end correction mechanism.Experiments on the carotid plaque dataset collected fromthreemedical centers demonstrate that the DCLCN achieves commendable performance and robustness.展开更多
A detector's nondestructive readout mode allows its pixels to be read multiple times during integration,enabling generation of a series of"up-the-ramp"images that continuously accumulate photons between ...A detector's nondestructive readout mode allows its pixels to be read multiple times during integration,enabling generation of a series of"up-the-ramp"images that continuously accumulate photons between successive frames.Because noise is correlated across these images,optimal stacking generally requires the images to be weighted unequally to achieve the best possible target signal-to-noise ratio(SNR).Objects in the sky present wildly varied brightness characteristics,and the counts in individual pixels of the same object can also span wide ranges.Therefore,a single set of weights cannot be optimal in all cases.To ensure that the stacked image is easily calibratable,we apply the same weight to all pixels within the same frame.In practice,results for high-SNR cases degraded only slightly when we used weights derived for low-SNR cases,whereas the low-SNR cases remained more sensitive to the weights.Therefore,we propose a quasi-optimal stacking method that maximizes the stacked SNR for the case where the RSN=1 per pixel in the last frame and use simulated data to demonstrate that this approach enhances the SNR more strongly than the equal-weight stacking and ramp fitting methods.Furthermore,we estimate the improvements in the limiting magnitudes for the China Space Station Telescope using the proposed method.When compared with the conventional readout mode,which is equivalent to selecting the last frame from the nondestructive readout,stacking 30 up-the-ramp images can improve the limiting magnitude by approximately 0.5 mag for the telescope's near-infrared observations,effectively reducing readout noise by approximately 62%.展开更多
In the task of classifying massive celestial data,the accurate classification of galaxies,stars,and quasars usually relies on spectral labels.However,spectral data account for only a small fraction of all astronomical...In the task of classifying massive celestial data,the accurate classification of galaxies,stars,and quasars usually relies on spectral labels.However,spectral data account for only a small fraction of all astronomical observation data,and the target source classification information in vast photometric data has not been accurately measured.To address this,we propose a novel deep learning-based algorithm,YL8C4Net,for the automatic detection and classification of target sources in photometric images.This algorithm combines the YOLOv8 detection network with the Conv4Net classification network.Additionally,we propose a novel magnitude-based labeling method for target source annotation.In the performance evaluation,the YOLOv8 achieves impressive performance with average precision scores of 0.824 for AP@0.5 and 0.795 for AP@0.5:0.95.Meanwhile,the constructed Conv4Net attains an accuracy of 0.8895.Overall,YL8C4Net offers the advantages of fewer parameters,faster processing speed,and higher classification accuracy,making it particularly suitable for large-scale data processing tasks.Furthermore,we employed the YL8C4Net model to conduct target source detection and classification on photometric images from 20 sky regions in SDSS-DR17.As a result,a catalog containing about 9.39 million target source classification results has been preliminarily constructed,thereby providing valuable reference data for astronomical research.展开更多
This paper provides a comprehensive introduction to the mini-Si Tian Real-time Image Processing pipeline(STRIP)and evaluates its operational performance.The STRIP pipeline is specifically designed for real-time alert ...This paper provides a comprehensive introduction to the mini-Si Tian Real-time Image Processing pipeline(STRIP)and evaluates its operational performance.The STRIP pipeline is specifically designed for real-time alert triggering and light curve generation for transient sources.By applying the STRIP pipeline to both simulated and real observational data of the Mini-Si Tian survey,it successfully identified various types of variable sources,including stellar flares,supernovae,variable stars,and asteroids,while meeting requirements of reduction speed within 5 minutes.For the real observational data set,the pipeline detected one flare event,127 variable stars,and14 asteroids from three monitored sky regions.Additionally,two data sets were generated:one,a real-bogus training data set comprising 218,818 training samples,and the other,a variable star light curve data set with 421instances.These data sets will be used to train machine learning algorithms,which are planned for future integration into STRIP.展开更多
As a pathfinder of the SiTian project,the Mini-SiTian(MST)Array,employed three commercial CMOS cameras,represents a next-generation,cost-effective optical time-domain survey project.This paper focuses primarily on the...As a pathfinder of the SiTian project,the Mini-SiTian(MST)Array,employed three commercial CMOS cameras,represents a next-generation,cost-effective optical time-domain survey project.This paper focuses primarily on the precise data processing pipeline designed for wide-field,CMOS-based devices,including the removal of instrumental effects,astrometry,photometry,and flux calibration.When applying this pipeline to approximately3000 observations taken in the Field 02(f02)region by MST,the results demonstrate a remarkable astrometric precision of approximately 70–80 mas(about 0.1 pixel),an impressive calibration accuracy of approximately1 mmag in the MST zero points,and a photometric accuracy of about 4 mmag for bright stars.Our studies demonstrate that MST CMOS can achieve photometric accuracy comparable to that of CCDs,highlighting the feasibility of large-scale CMOS-based optical time-domain surveys and their potential applications for cost optimization in future large-scale time-domain surveys,like the SiTian project.展开更多
Using the Radon transform and morphological image processing, an algorithm for ship's wake detection in the SAR (synthetic aperture radar) image is developed. Being manipulated in the Radon space to invert the gra...Using the Radon transform and morphological image processing, an algorithm for ship's wake detection in the SAR (synthetic aperture radar) image is developed. Being manipulated in the Radon space to invert the gray-level and binary images, the linear texture of ship wake in oceanic clutter can be well detected. It has been applied to the automatic detection of a moving ship from the SEASAT SAR image. The results show that this algorithm is well robust in a strong noisy background and is not very sensitive to the threshold parameter and the working window size.展开更多
Backscatter electron analysis from scanning electron microscopes(BSE-SEM)produces high-resolution image data of both rock samples and thin-sections,showing detailed structural and geochemical(mineralogical)information...Backscatter electron analysis from scanning electron microscopes(BSE-SEM)produces high-resolution image data of both rock samples and thin-sections,showing detailed structural and geochemical(mineralogical)information.This allows an in-depth exploration of the rock microstructures and the coupled chemical characteristics in the BSE-SEM image to be made using image processing techniques.Although image processing is a powerful tool for revealing the more subtle data“hidden”in a picture,it is not a commonly employed method in geoscientific microstructural analysis.Here,we briefly introduce the general principles of image processing,and further discuss its application in studying rock microstructures using BSE-SEM image data.展开更多
Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when deal...Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different pathologies.Furthermore,the speed of the retinal vessel segmentation system is of utmost importance.With the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the algorithm.To address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical morphology.Our algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)dataset.The experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE datasets.Notably,our algorithmexhibits competitive performance with state-of-the-art methods.Importantly,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE datasets.It is worth noting that these results were achieved using Matlab scripts containing multiple loops.This suggests that the processing time can be further reduced by replacing loops with vectorization.Thus the proposed algorithm can be deployed in real time applications.In summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.展开更多
We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms.Our approach not only allows for the anti-aliasing ...We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms.Our approach not only allows for the anti-aliasing of the images but also enables Point-Spread Function(PSF)deconvolution,resulting in enhanced restoration of extended sources,the highest peak signal-to-noise ratio,and reduced ringing artefacts.To test our method,we conducted numerical simulations that replicated observation runs of the China Space Station Telescope/the VLT Survey Telescope(VST)and compared our results to those obtained using previous algorithms.The simulation showed that our method outperforms previous approaches in several ways,such as restoring the profile of extended sources and minimizing ringing artefacts.Additionally,because our method relies on the inherent advantages of least squares fitting,it is more versatile and does not depend on the local uniformity hypothesis for the PSF.However,the new method consumes much more computation than the other approaches.展开更多
A topic studied in cartography is to make the extraction of cartographic features that provide the update of cartographic maps more easily. For this reason many automatic routines were created with the intent to perfo...A topic studied in cartography is to make the extraction of cartographic features that provide the update of cartographic maps more easily. For this reason many automatic routines were created with the intent to perform the features extraction. Despite of all studies about this, some features cannot be found by the algorithm or it can extract some pixels unduly. So the current article aims to show the results with the software development that uses the original and reference image to calculate some statistics about the extraction process. Furthermore, the calculated statistics can be used to evaluate the extraction process.展开更多
This paper presents a method for determining the percentage of coarse aggregate in concrete specimens by image processing. The test pieces were produced with the aim of obtaining images of their cross sections through...This paper presents a method for determining the percentage of coarse aggregate in concrete specimens by image processing. The test pieces were produced with the aim of obtaining images of their cross sections through a scanner table. In order to increase the contrast between mortar and coarse aggregate the sliced surfaces were treated with the phenolphthale in solution. The images obtained in the scanner were processed in a program developed with MATLAB （matrix laboratory）. The average coarse aggregate in each section and the mean of coarse aggregate per test body were calculated. With the results, it was revealed that the method returned satisfying results when compared to the original trace of the concrete.展开更多
In recent years, the widespread adoption of parallel computing, especially in multi-core processors and high-performance computing environments, ushered in a new era of efficiency and speed. This trend was particularl...In recent years, the widespread adoption of parallel computing, especially in multi-core processors and high-performance computing environments, ushered in a new era of efficiency and speed. This trend was particularly noteworthy in the field of image processing, which witnessed significant advancements. This parallel computing project explored the field of parallel image processing, with a focus on the grayscale conversion of colorful images. Our approach involved integrating OpenMP into our framework for parallelization to execute a critical image processing task: grayscale conversion. By using OpenMP, we strategically enhanced the overall performance of the conversion process by distributing the workload across multiple threads. The primary objectives of our project revolved around optimizing computation time and improving overall efficiency, particularly in the task of grayscale conversion of colorful images. Utilizing OpenMP for concurrent processing across multiple cores significantly reduced execution times through the effective distribution of tasks among these cores. The speedup values for various image sizes highlighted the efficacy of parallel processing, especially for large images. However, a detailed examination revealed a potential decline in parallelization efficiency with an increasing number of cores. This underscored the importance of a carefully optimized parallelization strategy, considering factors like load balancing and minimizing communication overhead. Despite challenges, the overall scalability and efficiency achieved with parallel image processing underscored OpenMP’s effectiveness in accelerating image manipulation tasks.展开更多
This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specif...This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specifically utilizing Cycle GAN. Synthetic pairs of images, simulating artifacts in fetal MRI, are generated to train the model. Our primary contribution is the use of Cycle GAN for fetal MRI restoration, augmented by artificially corrupted data. We compare three approaches (supervised Cycle GAN, Pix2Pix, and Mobile Unet) for artifact removal. Experimental results demonstrate that the proposed supervised Cycle GAN effectively removes artifacts while preserving image details, as validated through Structural Similarity Index Measure (SSIM) and normalized Mean Absolute Error (MAE). The method proves comparable to alternatives but avoids the generation of spurious regions, which is crucial for medical accuracy.展开更多
Algal blooms,the spread of algae on the surface of water bodies,have adverse effects not only on aquatic ecosystems but also on human life.The adverse effects of harmful algal blooms(HABs)necessitate a convenient solu...Algal blooms,the spread of algae on the surface of water bodies,have adverse effects not only on aquatic ecosystems but also on human life.The adverse effects of harmful algal blooms(HABs)necessitate a convenient solution for detection and monitoring.Unmanned aerial vehicles(UAVs)have recently emerged as a tool for algal bloom detection,efficiently providing on-demand images at high spatiotemporal resolutions.This study developed an image processing method for algal bloom area estimation from the aerial images(obtained from the internet)captured using UAVs.As a remote sensing method of HAB detection,analysis,and monitoring,a combination of histogram and texture analyses was used to efficiently estimate the area of HABs.Statistical features like entropy(using the Kullback-Leibler method)were emphasized with the aid of a gray-level co-occurrence matrix.The results showed that the orthogonal images demonstrated fewer errors,and the morphological filter best detected algal blooms in real time,with a precision of 80%.This study provided efficient image processing approaches using on-board UAVs for HAB monitoring.展开更多
基金financial support from the Brazilian Federal Agency for Support and Evaluation of Graduate Education(Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior—CAPES,scholarship process no BEX 0506/15-0)the Brazilian National Agency of Petroleum,Natural Gas and Biofuels(Agencia Nacional do Petroleo,Gas Natural e Biocombustiveis—ANP),in cooperation with the Brazilian Financier of Studies and Projects(Financiadora de Estudos e Projetos—FINEP)the Brazilian Ministry of Science,Technology and Innovation(Ministério da Ciencia,Tecnologia e Inovacao—MCTI)through the ANP’s Human Resources Program of the State University of Sao Paulo(Universidade Estadual Paulista—UNESP)for the Oil and Gas Sector PRH-ANP/MCTI no 48(PRH48).
文摘Important in many different sectors of the industry, the determination of stream velocity has become more and more important due to measurements precision necessity, in order to determine the right production rates, determine the volumetric production of undesired fluid, establish automated controls based on these measurements avoiding over-flooding or over-production, guaranteeing accurate predictive maintenance, etc. Difficulties being faced have been the determination of the velocity of specific fluids embedded in some others, for example, determining the gas bubbles stream velocity flowing throughout liquid fluid phase. Although different and already applicable methods have been researched and already implemented within the industry, a non-intrusive automated way of providing those stream velocities has its importance, and may have a huge impact in projects budget. Knowing the importance of its determination, this developed script uses a methodology of breaking-down real-time videos media into frame images, analyzing by pixel correlations possible superposition matches for further gas bubbles stream velocity estimation. In raw sense, the script bases itself in functions and procedures already available in MatLab, which can be used for image processing and treatments, allowing the methodology to be implemented. Its accuracy after the running test was of around 97% (ninety-seven percent);the raw source code with comments had almost 3000 (three thousand) characters;and the hardware placed for running the code was an Intel Core Duo 2.13 [Ghz] and 2 [Gb] RAM memory capable workstation. Even showing good results, it could be stated that just the end point correlations were actually getting to the final solution. So that, making use of self-learning functions or neural network, one could surely enhance the capability of the application to be run in real-time without getting exhaust by iterative loops.
基金funded by the National Natural Science Foundation of China(NSFC,Nos.12373086 and 12303082)CAS“Light of West China”Program+2 种基金Yunnan Revitalization Talent Support Program in Yunnan ProvinceNational Key R&D Program of ChinaGravitational Wave Detection Project No.2022YFC2203800。
文摘Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometric observations,outliers may exist in the obtained light curves due to various reasons.Therefore,preprocessing is required to remove these outliers to obtain high quality light curves.Through statistical analysis,the reasons leading to outliers can be categorized into two main types:first,the brightness of the object significantly increases due to the passage of a star nearby,referred to as“stellar contamination,”and second,the brightness markedly decreases due to cloudy cover,referred to as“cloudy contamination.”The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive.However,we propose the utilization of machine learning methods as a substitute.Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination,achieving F1 scores of 1.00 and 0.98 on a test set,respectively.We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine,then conduct comparative analyses of the results.
文摘In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But they still face challenges in terms of computational requirements,overfitting and generalization issues,etc.To resolve such issues,optimization algorithms provide greater control and transparency in designing digital filters for image enhancement and denoising.Therefore,this paper presented a novel denoising approach for medical applications using an Optimized Learning⁃based Multi⁃level discrete Wavelet Cascaded Convolutional Neural Network(OLMWCNN).In this approach,the optimal filter parameters are identified to preserve the image quality after denoising.The performance and efficiency of the OLMWCNN filter are evaluated,demonstrating significant progress in denoising medical images while overcoming the limitations of conventional methods.
文摘Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone;however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with tremendous accuracy. Therefore, in this study, we proposed a novel feature extractor framework associated with a supervised three-class XGBoost algorithm for the detection of osteosarcoma in whole slide histopathology images. This method allows for quicker and more effective data analysis. The first step involves preprocessing the imbalanced histopathology dataset, followed by augmentation and balancing utilizing two techniques: SMOTE and ADASYN. Next, a unique feature extraction framework is used to extract features, which are then inputted into the supervised three-class XGBoost algorithm for classification into three categories: non-tumor, viable tumor, and non-viable tumor. The experimental findings indicate that the proposed model exhibits superior efficiency, accuracy, and a more lightweight design in comparison to other current models for osteosarcoma detection.
基金supported by grants fromthe North China University of Technology Research Start-Up Fund(11005136024XN147-14)and(110051360024XN151-97)Guangzhou Development Zone Science and Technology Project(2023GH02)+4 种基金the National Key R&D Program of China(2021YFE0201100 and 2022YFA1103401 to Juntao Gao)National Natural Science Foundation of China(981890991 to Juntao Gao)Beijing Municipal Natural Science Foundation(Z200021 to Juntao Gao)CAS Interdisciplinary Innovation Team(JCTD-2020-04 to Juntao Gao)0032/2022/A,by Macao FDCT,and MYRG2022-00271-FST.
文摘Hematoxylin and Eosin(H&E)images,popularly used in the field of digital pathology,often pose challenges due to their limited color richness,hindering the differentiation of subtle cell features crucial for accurate classification.Enhancing the visibility of these elusive cell features helps train robust deep-learning models.However,the selection and application of image processing techniques for such enhancement have not been systematically explored in the research community.To address this challenge,we introduce Salient Features Guided Augmentation(SFGA),an approach that strategically integrates machine learning and image processing.SFGA utilizes machine learning algorithms to identify crucial features within cell images,subsequently mapping these features to appropriate image processing techniques to enhance training images.By emphasizing salient features and aligning them with corresponding image processing methods,SFGA is designed to enhance the discriminating power of deep learning models in cell classification tasks.Our research undertakes a series of experiments,each exploring the performance of different datasets and data enhancement techniques in classifying cell types,highlighting the significance of data quality and enhancement in mitigating overfitting and distinguishing cell characteristics.Specifically,SFGA focuses on identifying tumor cells from tissue for extranodal extension detection,with the SFGA-enhanced dataset showing notable advantages in accuracy.We conducted a preliminary study of five experiments,among which the accuracy of the pleomorphism experiment improved significantly from 50.81%to 95.15%.The accuracy of the other four experiments also increased,with improvements ranging from 3 to 43 percentage points.Our preliminary study shows the possibilities to enhance the diagnostic accuracy of deep learning models and proposes a systematic approach that could enhance cancer diagnosis,contributing as a first step in using SFGA in medical image enhancement.
基金funded by Deanship of Graduate Studies and Scientific Research at Najran University for supporting the research project through the Nama’a program,with the project code NU/GP/MRC/13/771-4.
文摘Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities.However,existing methodologies face persistent challenges,including low image contrast,noise interference,and inaccuracies in segmenting regions of interest.To address these limitations,this study introduces a novel computational framework for analyzing mammographic images,evaluated using the Mammographic Image Analysis Society(MIAS)dataset comprising 322 samples.The proposed methodology follows a structured three-stage approach.Initially,mammographic scans are classified using the Breast Imaging Reporting and Data System(BI-RADS),ensuring systematic and standardized image analysis.Next,the pectoral muscle,which can interfere with accurate segmentation,is effectively removed to refine the region of interest(ROI).The final stage involves an advanced image pre-processing module utilizing Independent Component Analysis(ICA)to enhance contrast,suppress noise,and improve image clarity.Following these enhancements,a robust segmentation technique is employed to delineated abnormal regions.Experimental results validate the efficiency of the proposed framework,demonstrating a significant improvement in the Effective Measure of Enhancement(EME)and a 3 dB increase in Peak Signal-to-Noise Ratio(PSNR),indicating superior image quality.The model also achieves an accuracy of approximately 97%,surpassing contemporary techniques evaluated on the MIAS dataset.Furthermore,its ability to process mammograms across all BI-RADS categories highlights its adaptability and reliability for clinical applications.This study presents an advanced and dependable computational framework for mammographic image analysis,effectively addressing critical challenges in noise reduction,contrast enhancement,and segmentation precision.The proposed approach lays the groundwork for seamless integration into computer-aided diagnostic(CAD)systems,with the potential to significantly enhance early breast cancer detection and contribute to improved patient outcomes.
基金supported by Shanghai Technical Service Computing Center of Science and Engineering,Shanghai University.
文摘Carotid artery plaques represent a major contributor to the morbidity and mortality associated with cerebrovascular disease,and their clinical significance is largely determined by the risk linked to plaque vulnerability.Therefore,classifying plaque risk constitutes one of themost critical tasks in the clinicalmanagement of this condition.While classification models derived from individual medical centers have been extensively investigated,these singlecenter models often fail to generalize well to multi-center data due to variations in ultrasound images caused by differences in physician expertise and equipment.To address this limitation,a Dual-Classifier Label Correction Networkmodel(DCLCN)is proposed for the classification of carotid plaque ultrasound images acrossmultiplemedical centers.TheDCLCNdesigns amulti-center domain adaptationmodule that leverages a dual-classifier strategy to extract knowledge from both source and target centers,thereby reducing feature discrepancies through a domain adaptation layer.Additionally,to mitigate the impact of image noise,a label modeling and correction module is introduced to generate pseudo-labels for the target centers and iteratively refine them using an end-to-end correction mechanism.Experiments on the carotid plaque dataset collected fromthreemedical centers demonstrate that the DCLCN achieves commendable performance and robustness.
基金supported by the National Key R&D Program of China (2022YFF0503400)the National Natural Science Foundation of China grant (U1931208)China Manned Space Program through its Space Application System.
文摘A detector's nondestructive readout mode allows its pixels to be read multiple times during integration,enabling generation of a series of"up-the-ramp"images that continuously accumulate photons between successive frames.Because noise is correlated across these images,optimal stacking generally requires the images to be weighted unequally to achieve the best possible target signal-to-noise ratio(SNR).Objects in the sky present wildly varied brightness characteristics,and the counts in individual pixels of the same object can also span wide ranges.Therefore,a single set of weights cannot be optimal in all cases.To ensure that the stacked image is easily calibratable,we apply the same weight to all pixels within the same frame.In practice,results for high-SNR cases degraded only slightly when we used weights derived for low-SNR cases,whereas the low-SNR cases remained more sensitive to the weights.Therefore,we propose a quasi-optimal stacking method that maximizes the stacked SNR for the case where the RSN=1 per pixel in the last frame and use simulated data to demonstrate that this approach enhances the SNR more strongly than the equal-weight stacking and ramp fitting methods.Furthermore,we estimate the improvements in the limiting magnitudes for the China Space Station Telescope using the proposed method.When compared with the conventional readout mode,which is equivalent to selecting the last frame from the nondestructive readout,stacking 30 up-the-ramp images can improve the limiting magnitude by approximately 0.5 mag for the telescope's near-infrared observations,effectively reducing readout noise by approximately 62%.
基金supported by the National Natural Science Foundation of China (NSFC, Grant No. U1731128)
文摘In the task of classifying massive celestial data,the accurate classification of galaxies,stars,and quasars usually relies on spectral labels.However,spectral data account for only a small fraction of all astronomical observation data,and the target source classification information in vast photometric data has not been accurately measured.To address this,we propose a novel deep learning-based algorithm,YL8C4Net,for the automatic detection and classification of target sources in photometric images.This algorithm combines the YOLOv8 detection network with the Conv4Net classification network.Additionally,we propose a novel magnitude-based labeling method for target source annotation.In the performance evaluation,the YOLOv8 achieves impressive performance with average precision scores of 0.824 for AP@0.5 and 0.795 for AP@0.5:0.95.Meanwhile,the constructed Conv4Net attains an accuracy of 0.8895.Overall,YL8C4Net offers the advantages of fewer parameters,faster processing speed,and higher classification accuracy,making it particularly suitable for large-scale data processing tasks.Furthermore,we employed the YL8C4Net model to conduct target source detection and classification on photometric images from 20 sky regions in SDSS-DR17.As a result,a catalog containing about 9.39 million target source classification results has been preliminarily constructed,thereby providing valuable reference data for astronomical research.
基金supported from the Strategic Pioneer Program of the Astronomy Large-Scale Scientific FacilityChinese Academy of Sciences and the Science and Education Integration Funding of University of Chinese Academy of Sciences+9 种基金the supports from the National Key Basic R&D Program of China via 2023YFA1608303the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0550103)the supports from the Strategic Priority Research Program of the Chinese Academy of Sciences under grant No.XDB0550000the National Natural Science Foundation of China(NSFC,grant Nos.12422303 and12261141690)the supports from the NSFC(grant No.12403024)supports from the NSFC through grant Nos.11988101 and 11933004the Postdoctoral Fellowship Program of CPSF under grant No.GZB20240731the Young Data Scientist Project of the National Astronomical Data Centerthe China Post-doctoral Science Foundation(No.2023M743447)supports from the New Cornerstone Science Foundation through the New Cornerstone Investigator Program and the XPLORER PRIZE。
文摘This paper provides a comprehensive introduction to the mini-Si Tian Real-time Image Processing pipeline(STRIP)and evaluates its operational performance.The STRIP pipeline is specifically designed for real-time alert triggering and light curve generation for transient sources.By applying the STRIP pipeline to both simulated and real observational data of the Mini-Si Tian survey,it successfully identified various types of variable sources,including stellar flares,supernovae,variable stars,and asteroids,while meeting requirements of reduction speed within 5 minutes.For the real observational data set,the pipeline detected one flare event,127 variable stars,and14 asteroids from three monitored sky regions.Additionally,two data sets were generated:one,a real-bogus training data set comprising 218,818 training samples,and the other,a variable star light curve data set with 421instances.These data sets will be used to train machine learning algorithms,which are planned for future integration into STRIP.
基金supported by the National Key Basic R&D Program of China via 2023YFA1608303the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0550103)+3 种基金the National Science Foundation of China 12422303,12403024,12222301,12173007,and 12261141690the Postdoctoral Fellowship Program of CPSF under grant Number GZB20240731the Young Data Scientist Project of the National Astronomical Data Center,and the China Post-doctoral Science Foundation No.2023M743447support from the NSFC through grant No.12303039 and No.12261141690.
文摘As a pathfinder of the SiTian project,the Mini-SiTian(MST)Array,employed three commercial CMOS cameras,represents a next-generation,cost-effective optical time-domain survey project.This paper focuses primarily on the precise data processing pipeline designed for wide-field,CMOS-based devices,including the removal of instrumental effects,astrometry,photometry,and flux calibration.When applying this pipeline to approximately3000 observations taken in the Field 02(f02)region by MST,the results demonstrate a remarkable astrometric precision of approximately 70–80 mas(about 0.1 pixel),an impressive calibration accuracy of approximately1 mmag in the MST zero points,and a photometric accuracy of about 4 mmag for bright stars.Our studies demonstrate that MST CMOS can achieve photometric accuracy comparable to that of CCDs,highlighting the feasibility of large-scale CMOS-based optical time-domain surveys and their potential applications for cost optimization in future large-scale time-domain surveys,like the SiTian project.
基金This project was supported by the National Natural Science Foundation of China (No. 49831060).
文摘Using the Radon transform and morphological image processing, an algorithm for ship's wake detection in the SAR (synthetic aperture radar) image is developed. Being manipulated in the Radon space to invert the gray-level and binary images, the linear texture of ship wake in oceanic clutter can be well detected. It has been applied to the automatic detection of a moving ship from the SEASAT SAR image. The results show that this algorithm is well robust in a strong noisy background and is not very sensitive to the threshold parameter and the working window size.
基金funded by the National Natural Science Foundation(No.42261134535)the National Key Research and Development Program(No.2023YFE0125000)+2 种基金the Frontiers Science Center for Deep-time Digital Earth(No.2652023001)the 111 Project of the Ministry of Science and Technology(No.BP0719021)supported by the department of Geology,University of Vienna(No.FA536901)。
文摘Backscatter electron analysis from scanning electron microscopes(BSE-SEM)produces high-resolution image data of both rock samples and thin-sections,showing detailed structural and geochemical(mineralogical)information.This allows an in-depth exploration of the rock microstructures and the coupled chemical characteristics in the BSE-SEM image to be made using image processing techniques.Although image processing is a powerful tool for revealing the more subtle data“hidden”in a picture,it is not a commonly employed method in geoscientific microstructural analysis.Here,we briefly introduce the general principles of image processing,and further discuss its application in studying rock microstructures using BSE-SEM image data.
文摘Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different pathologies.Furthermore,the speed of the retinal vessel segmentation system is of utmost importance.With the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the algorithm.To address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical morphology.Our algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)dataset.The experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE datasets.Notably,our algorithmexhibits competitive performance with state-of-the-art methods.Importantly,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE datasets.It is worth noting that these results were achieved using Matlab scripts containing multiple loops.This suggests that the processing time can be further reduced by replacing loops with vectorization.Thus the proposed algorithm can be deployed in real time applications.In summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.
基金supported by the GHfund A(202302017475)supported by the Foundation for Distinguished Young Scholars of Jiangsu Province(No.BK20140050)+5 种基金the National Natural Science Foundation of China(Nos.11973070,11333008,11273061,11825303,and 11673065)the China Manned Space Project with No.CMS-CSST-2021-A01,CMSCSST-2021-A03,CMS-CSST-2021-B01the Joint Funds of the National Natural Science Foundation of China(No.U1931210)the support from Key Research Program of Frontier Sciences,CAS,grant No.ZDBS-LY-7013Program of Shanghai Academic/Technology Research Leaderthe support from the science research grants from the China Manned Space Project with CMS-CSST-2021-A04,CMS-CSST-2021-A07。
文摘We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms.Our approach not only allows for the anti-aliasing of the images but also enables Point-Spread Function(PSF)deconvolution,resulting in enhanced restoration of extended sources,the highest peak signal-to-noise ratio,and reduced ringing artefacts.To test our method,we conducted numerical simulations that replicated observation runs of the China Space Station Telescope/the VLT Survey Telescope(VST)and compared our results to those obtained using previous algorithms.The simulation showed that our method outperforms previous approaches in several ways,such as restoring the profile of extended sources and minimizing ringing artefacts.Additionally,because our method relies on the inherent advantages of least squares fitting,it is more versatile and does not depend on the local uniformity hypothesis for the PSF.However,the new method consumes much more computation than the other approaches.
文摘A topic studied in cartography is to make the extraction of cartographic features that provide the update of cartographic maps more easily. For this reason many automatic routines were created with the intent to perform the features extraction. Despite of all studies about this, some features cannot be found by the algorithm or it can extract some pixels unduly. So the current article aims to show the results with the software development that uses the original and reference image to calculate some statistics about the extraction process. Furthermore, the calculated statistics can be used to evaluate the extraction process.
文摘This paper presents a method for determining the percentage of coarse aggregate in concrete specimens by image processing. The test pieces were produced with the aim of obtaining images of their cross sections through a scanner table. In order to increase the contrast between mortar and coarse aggregate the sliced surfaces were treated with the phenolphthale in solution. The images obtained in the scanner were processed in a program developed with MATLAB （matrix laboratory）. The average coarse aggregate in each section and the mean of coarse aggregate per test body were calculated. With the results, it was revealed that the method returned satisfying results when compared to the original trace of the concrete.
文摘In recent years, the widespread adoption of parallel computing, especially in multi-core processors and high-performance computing environments, ushered in a new era of efficiency and speed. This trend was particularly noteworthy in the field of image processing, which witnessed significant advancements. This parallel computing project explored the field of parallel image processing, with a focus on the grayscale conversion of colorful images. Our approach involved integrating OpenMP into our framework for parallelization to execute a critical image processing task: grayscale conversion. By using OpenMP, we strategically enhanced the overall performance of the conversion process by distributing the workload across multiple threads. The primary objectives of our project revolved around optimizing computation time and improving overall efficiency, particularly in the task of grayscale conversion of colorful images. Utilizing OpenMP for concurrent processing across multiple cores significantly reduced execution times through the effective distribution of tasks among these cores. The speedup values for various image sizes highlighted the efficacy of parallel processing, especially for large images. However, a detailed examination revealed a potential decline in parallelization efficiency with an increasing number of cores. This underscored the importance of a carefully optimized parallelization strategy, considering factors like load balancing and minimizing communication overhead. Despite challenges, the overall scalability and efficiency achieved with parallel image processing underscored OpenMP’s effectiveness in accelerating image manipulation tasks.
文摘This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specifically utilizing Cycle GAN. Synthetic pairs of images, simulating artifacts in fetal MRI, are generated to train the model. Our primary contribution is the use of Cycle GAN for fetal MRI restoration, augmented by artificially corrupted data. We compare three approaches (supervised Cycle GAN, Pix2Pix, and Mobile Unet) for artifact removal. Experimental results demonstrate that the proposed supervised Cycle GAN effectively removes artifacts while preserving image details, as validated through Structural Similarity Index Measure (SSIM) and normalized Mean Absolute Error (MAE). The method proves comparable to alternatives but avoids the generation of spurious regions, which is crucial for medical accuracy.
文摘Algal blooms,the spread of algae on the surface of water bodies,have adverse effects not only on aquatic ecosystems but also on human life.The adverse effects of harmful algal blooms(HABs)necessitate a convenient solution for detection and monitoring.Unmanned aerial vehicles(UAVs)have recently emerged as a tool for algal bloom detection,efficiently providing on-demand images at high spatiotemporal resolutions.This study developed an image processing method for algal bloom area estimation from the aerial images(obtained from the internet)captured using UAVs.As a remote sensing method of HAB detection,analysis,and monitoring,a combination of histogram and texture analyses was used to efficiently estimate the area of HABs.Statistical features like entropy(using the Kullback-Leibler method)were emphasized with the aid of a gray-level co-occurrence matrix.The results showed that the orthogonal images demonstrated fewer errors,and the morphological filter best detected algal blooms in real time,with a precision of 80%.This study provided efficient image processing approaches using on-board UAVs for HAB monitoring.
