Brain oscillations are vital to cognitive functions,while disrupted oscillatory activity is linked to various brain disorders.Although high-frequency neural oscillations(>1 Hz)have been extensively studied in cogni...Brain oscillations are vital to cognitive functions,while disrupted oscillatory activity is linked to various brain disorders.Although high-frequency neural oscillations(>1 Hz)have been extensively studied in cognition,the neural mechanisms underlying low-frequency hemodynamic oscillations(LFHO)<1 Hz have not yet been fully explored.One way to examine oscillatory neural dynamics is to use a facial expression(FE)paradigm to induce steady-state visual evoked potentials(SSVEPs),which has been used in electroencephalography studies of high-frequency brain oscillation activity.In this study,LFHO during SSVEP-inducing periodic flickering stimuli presentation were inspected using functional near-infrared spectroscopy(fNIRS),in which hemodynamic responses in the prefrontal cortex were recorded while participants were passively viewing dynamic FEs flickering at 0.2 Hz.The fast Fourier analysis results demonstrated that the power exhibited monochronic peaks at 0.2 Hz across all channels,indicating that the periodic events successfully elicited LFHO in the prefrontal cortex.More importantly,measurement of LFHO can effectively distinguish the brain activation difference between different cognitive conditions,with happy FE presentation showing greater LFHO power than neutral FE presentation.These results demonstrate that stimuli flashing at a given frequency can induce LFHO in the prefrontal cortex,which provides new insights into the cognitive mechanisms involved in slow oscillation.展开更多
A numerical simulation of a patient’s nasal airflow was developed via computational fluid dynamics.Accordingly,computerized tomography scans of a patient with septal deviation and allergic rhinitis were obtained.The ...A numerical simulation of a patient’s nasal airflow was developed via computational fluid dynamics.Accordingly,computerized tomography scans of a patient with septal deviation and allergic rhinitis were obtained.The three-dimensional(3D)nasal model was designed using InVesalius 3.0,which was then imported to(computer aided 3D interactive application)CATIA V5 for modification,and finally to analysis system(ANSYS)flow oriented logistics upgrade for enterprise networks(FLUENT)to obtain the numerical solution.The velocity contours of the cross-sectional area were analyzed on four main surfaces:the vestibule,nasal valve,middle turbinate,and nasopharynx.The pressure and velocity characteristics were assessed at both laminar and turbulent mass flow rates for both the standardized and the patient’s model nasal cavity.The developed model of the patient is approximately half the size of the standardized model;hence,its velocity was approximately two times more than that of the standardized model.展开更多
Acoustic/ultrasonic sensors are devices that can convert mechanical energy into electrical signals.The Fabry–Perot cavity is processed on the end face of the double-clad fiber by a two-photon three-dimensional lithog...Acoustic/ultrasonic sensors are devices that can convert mechanical energy into electrical signals.The Fabry–Perot cavity is processed on the end face of the double-clad fiber by a two-photon three-dimensional lithography machine.In this study,the outer diameter of the core cladding was 250μm,the diameter of the core was 9μm,and the microcavity sensing unit was only 30μm.It could measure ultrasonic signals with high precision.The characteristics of the proposed ultrasonic sensor were investigated,and its feasibility was proven through experiments.Its design has a small size and can replace a larger ultrasonic detector device for photoacoustic signal detection.The sensor is applicable to the field of biomedical information technology,including medical diagnosis,photoacoustic endoscopy,and photoacoustic imaging.展开更多
One example of an artificial intelligence ethical dilemma is the autonomous vehicle situation presented by Massachusetts Institute of Technology researchers in the Moral Machine Experiment.To solve such dilemmas,the M...One example of an artificial intelligence ethical dilemma is the autonomous vehicle situation presented by Massachusetts Institute of Technology researchers in the Moral Machine Experiment.To solve such dilemmas,the MIT researchers used a classic statistical method known as the hierarchical Bayesian(HB)model.This paper builds upon previous work for modeling moral decision making,applies a deep learning method to learn human ethics in this context,and compares it to the HB approach.These methods were tested to predict moral decisions of simulated populations of Moral Machine participants.Overall,test results indicate that deep neural networks can be effective in learning the group morality of a population through observation,and outperform the Bayesian model in the cases of model mismatches.展开更多
Molecular imaging(MI)is a novel imaging discipline that has been continuously developed in recent years.It combines biochemistry,multimodal imaging,biomathematics,bioinformatics,cell&molecular physiology,biophysic...Molecular imaging(MI)is a novel imaging discipline that has been continuously developed in recent years.It combines biochemistry,multimodal imaging,biomathematics,bioinformatics,cell&molecular physiology,biophysics,and pharmacology,and it provides a new technology platform for the early diagnosis and quantitative analysis of diseases,treatment monitoring and evaluation,and the development of comprehensive physiology.Fluorescence Molecular Tomography(FMT)is a type of optical imaging modality in MI that captures the three-dimensional distribution of fluorescence within a biological tissue generated by a specific molecule of fluorescent material within a biological tissue.Compared with other optical molecular imaging methods,FMT has the characteristics of high sensitivity,low cost,and safety and reliability.It has become the research frontier and research hotspot of optical molecular imaging technology.This paper took an overview of the recent methodology advances in FMT,mainly focused on the photon propagation model of FMT based on the radiative transfer equation(RTE),and the reconstruction problem solution consist of forward problem and inverse problem.We introduce the detailed technologies utilized in reconstruction of FMT.Finally,the challenges in FMT were discussed.This survey aims at summarizing current research hotspots in methodology of FMT,fromwhich future research may benefit.展开更多
In this paper,we propose an efficient computational method for converting local coordinates to world coordinates using specially structured coordinate data.The problem in question is the computation of world coordinat...In this paper,we propose an efficient computational method for converting local coordinates to world coordinates using specially structured coordinate data.The problem in question is the computation of world coordinates of an object throughout a motion,assuming that we only know the changing coordinates of some fixed surrounding reference points in the local coordinate system of the object.The proposed method is based on barycentric coordinates;by taking the aforementioned static positions as the vertices of a polyhedron,we can specify the coordinates of the object in each step with the help of barycentric coordinates.This approach can significantly help us to achieve more accurate results than by using other possible methods.In the paper,we describe the problem and barycentric coordinate-based solution in detail.We then compare the barycentric method with a technique based on transformation matrices,which we also tested for solving our problem.We also present various diagrams that demonstrate the efficiency of our proposed approach in terms of precision and performance.展开更多
To overcome the topological constraints of non-uniform rational B-splines,T-splines have been proposed to define the freeform surfaces.The introduction of T-junctions and extraordinary points makes it possible to repr...To overcome the topological constraints of non-uniform rational B-splines,T-splines have been proposed to define the freeform surfaces.The introduction of T-junctions and extraordinary points makes it possible to represent arbitrarily shaped models by a single T-spline surface.Whereas,the complexity and flexibility of topology structure bring difficulty in programming,which have caused a great obstacle for the development and application of T-spline technologies.So far,research literatures concerning T-spline data structures compatible with extraordinary points are very scarce.In this paper,an efficient data structure for calculation of unstructured T-spline surfaces is developed,by which any complex T-spline surface models can be easily and efficiently computed.Several unstructured T-spline surface models are calculated and visualized in our prototype system to verify the validity of the proposed method.展开更多
Following publication of the original article[1],the authors identified an error in the article title.The first word ‘Review’ is added mistakenly by the typesetter.
This paper addresses the efficiency of two feature extraction methods for classifying small metal objects including screws,nuts,keys,and coins:the histogram of oriented gradients(HOG)and local binary pattern(LBP).The ...This paper addresses the efficiency of two feature extraction methods for classifying small metal objects including screws,nuts,keys,and coins:the histogram of oriented gradients(HOG)and local binary pattern(LBP).The desired features for the labeled images are first extracted and saved in the form of a feature matrix.Using three different classification methods(non-parametric K-nearest neighbors algorithm,support vector machine,and naïve Bayesian method),the images are classified into four different classes.Then,by examining the resulting confusion matrix,the performances of the HOG and LBP approaches are compared for these four classes.The effectiveness of these two methods is also compared with the“You Only Look Once”and faster region-based convolutional neural network approaches,which are based on deep learning.The collected image set in this paper includes 800 labeled training images and 180 test images.The results show that the use of the HOG is more efficient than the use of the LBP.Moreover,a combination of the HOG and LBP provides better results than either alone.展开更多
Image reconstruction for list-mode time-of-flight(TOF)positron emission tomography(PET)can be achieved by analytic algorithms.The backprojection filtering(BPF)algorithm is an efficient algorithm for this task.The conv...Image reconstruction for list-mode time-of-flight(TOF)positron emission tomography(PET)can be achieved by analytic algorithms.The backprojection filtering(BPF)algorithm is an efficient algorithm for this task.The conventional noise control method for analytic image reconstruction is the use of a stationary lowpass filter,which does not model the Poisson noise properly.This study proposes a nonstationary filter for Poisson noise control.The filter is implemented in the spatial domain in a form similar to convolution.展开更多
Radiomics has increasingly been investigated as a potential biomarker in quantitative imaging to facilitate personalized diagnosis and treatment of head and neck cancer(HNC),a group of malignancies associated with hig...Radiomics has increasingly been investigated as a potential biomarker in quantitative imaging to facilitate personalized diagnosis and treatment of head and neck cancer(HNC),a group of malignancies associated with high heterogeneity.However,the feature reliability of radiomics is a major obstacle to its broad validity and generality in application to the highly heterogeneous head and neck(HN)tissues.In particular,feature repeatability of radiomics in magnetic resonance imaging(MRI)acquisition,which is considered a crucial confounding factor of radiomics feature reliability,is still sparsely investigated.This study prospectively investigated the acquisition repeatability of 93 MRI radiomics features in ten HN tissues of 15 healthy volunteers,aiming for potential magnetic resonance-guided radiotherapy(MRgRT)treatment of HNC.Each subject underwent four MRI acquisitions with MRgRT treatment position and immobilization using two pulse sequences of 3D T1-weighed turbo spin-echo and 3D T2-weighed turbo spin-echo on a 1.5T MRI simulator.The repeatability of radiomics feature acquisition was evaluated in terms of the intraclass correlation coefficient(ICC),whereas within-subject acquisition variability was evaluated in terms of the coefficient of variation(CV).The results showed that MRI radiomics features exhibited heterogeneous acquisition variability and uncertainty dependent on feature types,tissues,and pulse sequences.Only a small fraction of features showed excellent acquisition repeatability(ICC>0.9)and low within-subject variability.Multiple MRI scans improved the accuracy and confidence of the identification of reliable features concerning MRI acquisition compared to simple test-retest repeated scans.This study contributes to the literature on the reliability of radiomics features with respect to MRI acquisition and the selection of reliable radiomics features for use in modeling in future HNC MRgRT applications.展开更多
Virtual reality,augmented reality,robotics,and autonomous driving,have recently attracted much attention from both academic and industrial communities,in which image-based camera localization is a key task.However,the...Virtual reality,augmented reality,robotics,and autonomous driving,have recently attracted much attention from both academic and industrial communities,in which image-based camera localization is a key task.However,there has not been a complete review on image-based camera localization.It is urgent to map this topic to enable individuals enter the field quickly.In this paper,an overview of image-based camera localization is presented.A new and complete classification of image-based camera localization approaches is provided and the related techniques are introduced.Trends for future development are also discussed.This will be useful not only to researchers,but also to engineers and other individuals interested in this field.展开更多
An unmanned aerial vehicle(UAV)is a small,fast aircraft with many useful features.It is widely used in military reconnaissance,aerial photography,searches,and other fields;it also has very good practical-application a...An unmanned aerial vehicle(UAV)is a small,fast aircraft with many useful features.It is widely used in military reconnaissance,aerial photography,searches,and other fields;it also has very good practical-application and development prospects.Since the UAV’s flight orientation is easily changeable,its orientation and flight path are difficult to control,leading to its high damage rate.Therefore,UAV flight-control technology has become the focus of attention.This study focuses on simulating a UAV’s flight and orientation control,and detecting collisions between a UAV and objects in a complex virtual environment.The proportional-integral-derivative control algorithm is used to control the orientation and position of the UAV in a virtual environment.A version of the bounding-box method that combines a grid with a k-dimensional tree is adopted in this paper,to improve the system performance and accelerate the collision-detection process.This provides a practical method for future studies on UAV flight position and orientation control,collision detection,etc.展开更多
Specimen observation and dissection have been regarded as the best approach to teach anatomy,but due to the severe lack of anatomical specimens in recent years,the quality of anatomy teaching has been seriously affect...Specimen observation and dissection have been regarded as the best approach to teach anatomy,but due to the severe lack of anatomical specimens in recent years,the quality of anatomy teaching has been seriously affected.In order to disseminate anatomical knowledge effectively under such circumstances,this study discusses three key factors(modeling,perception,and interaction)involved in constructing virtual anatomy teaching systems in detail.To ensure the authenticity,integrity,and accuracy of modeling,detailed three-dimensional(3D)digital anatomical models are constructed using multi-scale data,such as the Chinese Visible Human dataset,clinical imaging data,tissue sections,and other sources.The anatomical knowledge ontology is built according to the needs of the particular teaching purposes.Various kinds of anatomical knowledge and 3D digital anatomical models are organically combined to construct virtual anatomy teaching system by means of virtual reality equipment and technology.The perception of knowledge is realized by the Yi Chuang Digital Human Anatomy Teaching System that we have created.The virtual interaction mode,which is similar to actual anatomical specimen observation and dissection,can enhance the transmissibility of anatomical knowledge.This virtual anatomy teaching system captures the three key factors.It can provide realistic and reusable teaching resources,expand the new medical education model,and effectively improve the quality of anatomy teaching.展开更多
Photoacoustic(PA)imaging is a promising non-invasive and non-ionizing biomedical imaging modality that emerged in recent years.The articles presented in this special issue describe some of newest progress in this fiel...Photoacoustic(PA)imaging is a promising non-invasive and non-ionizing biomedical imaging modality that emerged in recent years.The articles presented in this special issue describe some of newest progress in this field.We are extremely grateful to all contributing authors.The first part of the issue covers new laser source devel-opment,including fiber lasers and laser diodes.The sec-ond part is dedicated to improving the image resolution through chronic cranial window techniques,virtual-point concept,fast polygon scanning,and Fabry Perot sensing.The third part shows the basic principles of photoacous-tic/ultrasound imaging and its applications.展开更多
Radiomic analysis has exponentially increased the amount of quantitative data that can be extracted from a single image.These imaging biomarkers can aid in the generation of prediction models aimed to further personal...Radiomic analysis has exponentially increased the amount of quantitative data that can be extracted from a single image.These imaging biomarkers can aid in the generation of prediction models aimed to further personalized medicine.However,the generalizability of the model is dependent on the robustness of these features.The purpose of this study is to review the current literature regarding robustness of radiomic features on magnetic resonance imaging.Additionally,a phantom study is performed to systematically evaluate the behavior of radiomic features under various conditions(signal to noise ratio,region of interest delineation,voxel size change and normalization methods)using intraclass correlation coefficients.The features extracted in this phantom study include first order,shape,gray level cooccurrence matrix and gray level run length matrix.Many features are found to be non-robust to changing parameters.Feature robustness assessment prior to feature selection,especially in the case of combining multi-institutional data,may be warranted.Further investigation is needed in this area of research.展开更多
With the rapid development of deep learning technology,behavior recognition based on video streams has made great progress in recent years.However,there are also some problems that must be solved:(1)In order to improv...With the rapid development of deep learning technology,behavior recognition based on video streams has made great progress in recent years.However,there are also some problems that must be solved:(1)In order to improve behavior recognition performance,the models have tended to become deeper,wider,and more complex.However,some new problems have been introduced also,such as that their real-time performance decreases;(2)Some actions in existing datasets are so similar that they are difficult to distinguish.To solve these problems,the ResNet34-3DRes18 model,which is a lightweight and efficient two-dimensional(2D)and three-dimensional(3D)fused model,is constructed in this study.The model used 2D convolutional neural network(2DCNN)to obtain the feature maps of input images and 3D convolutional neural network(3DCNN)to process the temporal relationships between frames,which made the model not only make use of 3DCNN’s advantages on video temporal modeling but reduced model complexity.Compared with state-of-the-art models,this method has shown excellent performance at a faster speed.Furthermore,to distinguish between similar motions in the datasets,an attention gate mechanism is added,and a Res34-SE-IM-Net attention recognition model is constructed.The Res34-SE-IM-Net achieved 71.85%,92.196%,and 36.5%top-1 accuracy(The predicting label obtained from model is the largest one in the output probability vector.If the label is the same as the target label of the motion,the classification is correct.)respectively on the test sets of the HMDB51,UCF101,and Something-Something v1 datasets.展开更多
In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest...In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.展开更多
One of the most critical steps in medical health is the proper diagnosis of the disease.Dermatology is one of the most volatile and challenging fields in terms of diagnosis.Dermatologists often require further testing...One of the most critical steps in medical health is the proper diagnosis of the disease.Dermatology is one of the most volatile and challenging fields in terms of diagnosis.Dermatologists often require further testing,review of the patient’s history,and other data to ensure a proper diagnosis.Therefore,finding a method that can guarantee a proper trusted diagnosis quickly is essential.Several approaches have been developed over the years to facilitate the diagnosis based on machine learning.However,the developed systems lack certain properties,such as high accuracy.This study proposes a system developed in MATLAB that can identify skin lesions and classify them as normal or benign.The classification process is effectuated by implementing the K-nearest neighbor(KNN)approach to differentiate between normal skin and malignant skin lesions that imply pathology.KNN is used because it is time efficient and promises highly accurate results.The accuracy of the system reached 98%in classifying skin lesions.展开更多
Although the education sector is improving more quickly than ever with the help of advancing technologies,there are still many areas yet to be discovered,and there will always be room for further enhancements.Two of t...Although the education sector is improving more quickly than ever with the help of advancing technologies,there are still many areas yet to be discovered,and there will always be room for further enhancements.Two of the most disruptive technologies,machine learning(ML)and blockchain,have helped replace conventional approaches used in the education sector with highly technical and effective methods.In this study,a system is proposed that combines these two radiant technologies and helps resolve problems such as forgeries of educational records and fake degrees.The idea here is that if these technologies can be merged and a system can be developed that uses blockchain to store student data and ML to accurately predict the future job roles for students after graduation,the problems of further counterfeiting and insecurity in the student achievements can be avoided.Further,ML models will be used to train and predict valid data.This system will provide the university with an official decentralized database of student records who have graduated from there.In addition,this system provides employers with a platform where the educational records of the employees can be verified.Students can share their educational information in their e-portfolios on platforms such as LinkedIn,which is a platform for managing professional profiles.This allows students,companies,and other industries to find approval for student data more easily.展开更多
基金University of Macao,Nos.MYRG2019-00082-FHS and MYRG2018-00081-FHSMacao Science and Technology Development Fund,No.FDCT 025/2015/A1 and FDCT 0011/2018/A1.
文摘Brain oscillations are vital to cognitive functions,while disrupted oscillatory activity is linked to various brain disorders.Although high-frequency neural oscillations(>1 Hz)have been extensively studied in cognition,the neural mechanisms underlying low-frequency hemodynamic oscillations(LFHO)<1 Hz have not yet been fully explored.One way to examine oscillatory neural dynamics is to use a facial expression(FE)paradigm to induce steady-state visual evoked potentials(SSVEPs),which has been used in electroencephalography studies of high-frequency brain oscillation activity.In this study,LFHO during SSVEP-inducing periodic flickering stimuli presentation were inspected using functional near-infrared spectroscopy(fNIRS),in which hemodynamic responses in the prefrontal cortex were recorded while participants were passively viewing dynamic FEs flickering at 0.2 Hz.The fast Fourier analysis results demonstrated that the power exhibited monochronic peaks at 0.2 Hz across all channels,indicating that the periodic events successfully elicited LFHO in the prefrontal cortex.More importantly,measurement of LFHO can effectively distinguish the brain activation difference between different cognitive conditions,with happy FE presentation showing greater LFHO power than neutral FE presentation.These results demonstrate that stimuli flashing at a given frequency can induce LFHO in the prefrontal cortex,which provides new insights into the cognitive mechanisms involved in slow oscillation.
基金This research was funded by the Universiti Sains Malaysia,No.1001/PAERO/814276.
文摘A numerical simulation of a patient’s nasal airflow was developed via computational fluid dynamics.Accordingly,computerized tomography scans of a patient with septal deviation and allergic rhinitis were obtained.The three-dimensional(3D)nasal model was designed using InVesalius 3.0,which was then imported to(computer aided 3D interactive application)CATIA V5 for modification,and finally to analysis system(ANSYS)flow oriented logistics upgrade for enterprise networks(FLUENT)to obtain the numerical solution.The velocity contours of the cross-sectional area were analyzed on four main surfaces:the vestibule,nasal valve,middle turbinate,and nasopharynx.The pressure and velocity characteristics were assessed at both laminar and turbulent mass flow rates for both the standardized and the patient’s model nasal cavity.The developed model of the patient is approximately half the size of the standardized model;hence,its velocity was approximately two times more than that of the standardized model.
基金This work was supported in part by the Natural Science Foundation of Guangdong Province,No.2020A1515010958Key Project of Shenzhen Science and Technology Plan,No.JCYJ20200109113808048.
文摘Acoustic/ultrasonic sensors are devices that can convert mechanical energy into electrical signals.The Fabry–Perot cavity is processed on the end face of the double-clad fiber by a two-photon three-dimensional lithography machine.In this study,the outer diameter of the core cladding was 250μm,the diameter of the core was 9μm,and the microcavity sensing unit was only 30μm.It could measure ultrasonic signals with high precision.The characteristics of the proposed ultrasonic sensor were investigated,and its feasibility was proven through experiments.Its design has a small size and can replace a larger ultrasonic detector device for photoacoustic signal detection.The sensor is applicable to the field of biomedical information technology,including medical diagnosis,photoacoustic endoscopy,and photoacoustic imaging.
文摘One example of an artificial intelligence ethical dilemma is the autonomous vehicle situation presented by Massachusetts Institute of Technology researchers in the Moral Machine Experiment.To solve such dilemmas,the MIT researchers used a classic statistical method known as the hierarchical Bayesian(HB)model.This paper builds upon previous work for modeling moral decision making,applies a deep learning method to learn human ethics in this context,and compares it to the HB approach.These methods were tested to predict moral decisions of simulated populations of Moral Machine participants.Overall,test results indicate that deep neural networks can be effective in learning the group morality of a population through observation,and outperform the Bayesian model in the cases of model mismatches.
基金supported by Ministry of Science and Technology of China under Grant No.2017YFA0205200,2016YFC0103702National Natural Science Foundation of China under Grant No.81227901,81527805‘Chinese Academy of Sciences under Grant No.GJJSTD20170004’Beijing Municipal Science&Technology Commission No.Z161100002616022by the General Financial Grant from the China Postdoctoral Science Foundation under Grant 2017 M620952.
文摘Molecular imaging(MI)is a novel imaging discipline that has been continuously developed in recent years.It combines biochemistry,multimodal imaging,biomathematics,bioinformatics,cell&molecular physiology,biophysics,and pharmacology,and it provides a new technology platform for the early diagnosis and quantitative analysis of diseases,treatment monitoring and evaluation,and the development of comprehensive physiology.Fluorescence Molecular Tomography(FMT)is a type of optical imaging modality in MI that captures the three-dimensional distribution of fluorescence within a biological tissue generated by a specific molecule of fluorescent material within a biological tissue.Compared with other optical molecular imaging methods,FMT has the characteristics of high sensitivity,low cost,and safety and reliability.It has become the research frontier and research hotspot of optical molecular imaging technology.This paper took an overview of the recent methodology advances in FMT,mainly focused on the photon propagation model of FMT based on the radiative transfer equation(RTE),and the reconstruction problem solution consist of forward problem and inverse problem.We introduce the detailed technologies utilized in reconstruction of FMT.Finally,the challenges in FMT were discussed.This survey aims at summarizing current research hotspots in methodology of FMT,fromwhich future research may benefit.
基金supported by the construction EFOP-3.6.3-VEKOP-16-2017-00002supported by the European Union,co-financed by the European Social Fund.
文摘In this paper,we propose an efficient computational method for converting local coordinates to world coordinates using specially structured coordinate data.The problem in question is the computation of world coordinates of an object throughout a motion,assuming that we only know the changing coordinates of some fixed surrounding reference points in the local coordinate system of the object.The proposed method is based on barycentric coordinates;by taking the aforementioned static positions as the vertices of a polyhedron,we can specify the coordinates of the object in each step with the help of barycentric coordinates.This approach can significantly help us to achieve more accurate results than by using other possible methods.In the paper,we describe the problem and barycentric coordinate-based solution in detail.We then compare the barycentric method with a technique based on transformation matrices,which we also tested for solving our problem.We also present various diagrams that demonstrate the efficiency of our proposed approach in terms of precision and performance.
基金The authors would like to acknowledge the support by the National Natural Science Foundation of China(Nos.61572056 and 51305016).
文摘To overcome the topological constraints of non-uniform rational B-splines,T-splines have been proposed to define the freeform surfaces.The introduction of T-junctions and extraordinary points makes it possible to represent arbitrarily shaped models by a single T-spline surface.Whereas,the complexity and flexibility of topology structure bring difficulty in programming,which have caused a great obstacle for the development and application of T-spline technologies.So far,research literatures concerning T-spline data structures compatible with extraordinary points are very scarce.In this paper,an efficient data structure for calculation of unstructured T-spline surfaces is developed,by which any complex T-spline surface models can be easily and efficiently computed.Several unstructured T-spline surface models are calculated and visualized in our prototype system to verify the validity of the proposed method.
文摘Following publication of the original article[1],the authors identified an error in the article title.The first word ‘Review’ is added mistakenly by the typesetter.
文摘This paper addresses the efficiency of two feature extraction methods for classifying small metal objects including screws,nuts,keys,and coins:the histogram of oriented gradients(HOG)and local binary pattern(LBP).The desired features for the labeled images are first extracted and saved in the form of a feature matrix.Using three different classification methods(non-parametric K-nearest neighbors algorithm,support vector machine,and naïve Bayesian method),the images are classified into four different classes.Then,by examining the resulting confusion matrix,the performances of the HOG and LBP approaches are compared for these four classes.The effectiveness of these two methods is also compared with the“You Only Look Once”and faster region-based convolutional neural network approaches,which are based on deep learning.The collected image set in this paper includes 800 labeled training images and 180 test images.The results show that the use of the HOG is more efficient than the use of the LBP.Moreover,a combination of the HOG and LBP provides better results than either alone.
文摘Image reconstruction for list-mode time-of-flight(TOF)positron emission tomography(PET)can be achieved by analytic algorithms.The backprojection filtering(BPF)algorithm is an efficient algorithm for this task.The conventional noise control method for analytic image reconstruction is the use of a stationary lowpass filter,which does not model the Poisson noise properly.This study proposes a nonstationary filter for Poisson noise control.The filter is implemented in the spatial domain in a form similar to convolution.
基金This study was supported by hospital research project,No.REC-2019-09.
文摘Radiomics has increasingly been investigated as a potential biomarker in quantitative imaging to facilitate personalized diagnosis and treatment of head and neck cancer(HNC),a group of malignancies associated with high heterogeneity.However,the feature reliability of radiomics is a major obstacle to its broad validity and generality in application to the highly heterogeneous head and neck(HN)tissues.In particular,feature repeatability of radiomics in magnetic resonance imaging(MRI)acquisition,which is considered a crucial confounding factor of radiomics feature reliability,is still sparsely investigated.This study prospectively investigated the acquisition repeatability of 93 MRI radiomics features in ten HN tissues of 15 healthy volunteers,aiming for potential magnetic resonance-guided radiotherapy(MRgRT)treatment of HNC.Each subject underwent four MRI acquisitions with MRgRT treatment position and immobilization using two pulse sequences of 3D T1-weighed turbo spin-echo and 3D T2-weighed turbo spin-echo on a 1.5T MRI simulator.The repeatability of radiomics feature acquisition was evaluated in terms of the intraclass correlation coefficient(ICC),whereas within-subject acquisition variability was evaluated in terms of the coefficient of variation(CV).The results showed that MRI radiomics features exhibited heterogeneous acquisition variability and uncertainty dependent on feature types,tissues,and pulse sequences.Only a small fraction of features showed excellent acquisition repeatability(ICC>0.9)and low within-subject variability.Multiple MRI scans improved the accuracy and confidence of the identification of reliable features concerning MRI acquisition compared to simple test-retest repeated scans.This study contributes to the literature on the reliability of radiomics features with respect to MRI acquisition and the selection of reliable radiomics features for use in modeling in future HNC MRgRT applications.
基金supported by the National Natural Science Foundation of China under Grant No.61421004,61572499,61632003.
文摘Virtual reality,augmented reality,robotics,and autonomous driving,have recently attracted much attention from both academic and industrial communities,in which image-based camera localization is a key task.However,there has not been a complete review on image-based camera localization.It is urgent to map this topic to enable individuals enter the field quickly.In this paper,an overview of image-based camera localization is presented.A new and complete classification of image-based camera localization approaches is provided and the related techniques are introduced.Trends for future development are also discussed.This will be useful not only to researchers,but also to engineers and other individuals interested in this field.
基金This work was supported by the National Key Technology Research and Development Program of China(Nos.2015BAK01B06,2017YFB1002705,2017YFB1002601,and 2017YFB0203002)the National Marine Public Service Project(No.201505014-3)+1 种基金the National Natural Science Foundation of China(NSFC)(Nos.61472010 and 61661146002)the Equipment Development Project(No.315050501).
文摘An unmanned aerial vehicle(UAV)is a small,fast aircraft with many useful features.It is widely used in military reconnaissance,aerial photography,searches,and other fields;it also has very good practical-application and development prospects.Since the UAV’s flight orientation is easily changeable,its orientation and flight path are difficult to control,leading to its high damage rate.Therefore,UAV flight-control technology has become the focus of attention.This study focuses on simulating a UAV’s flight and orientation control,and detecting collisions between a UAV and objects in a complex virtual environment.The proportional-integral-derivative control algorithm is used to control the orientation and position of the UAV in a virtual environment.A version of the bounding-box method that combines a grid with a k-dimensional tree is adopted in this paper,to improve the system performance and accelerate the collision-detection process.This provides a practical method for future studies on UAV flight position and orientation control,collision detection,etc.
基金This work was funded by the National Natural Science Foundation of China(No.61190122).
文摘Specimen observation and dissection have been regarded as the best approach to teach anatomy,but due to the severe lack of anatomical specimens in recent years,the quality of anatomy teaching has been seriously affected.In order to disseminate anatomical knowledge effectively under such circumstances,this study discusses three key factors(modeling,perception,and interaction)involved in constructing virtual anatomy teaching systems in detail.To ensure the authenticity,integrity,and accuracy of modeling,detailed three-dimensional(3D)digital anatomical models are constructed using multi-scale data,such as the Chinese Visible Human dataset,clinical imaging data,tissue sections,and other sources.The anatomical knowledge ontology is built according to the needs of the particular teaching purposes.Various kinds of anatomical knowledge and 3D digital anatomical models are organically combined to construct virtual anatomy teaching system by means of virtual reality equipment and technology.The perception of knowledge is realized by the Yi Chuang Digital Human Anatomy Teaching System that we have created.The virtual interaction mode,which is similar to actual anatomical specimen observation and dissection,can enhance the transmissibility of anatomical knowledge.This virtual anatomy teaching system captures the three key factors.It can provide realistic and reusable teaching resources,expand the new medical education model,and effectively improve the quality of anatomy teaching.
文摘Photoacoustic(PA)imaging is a promising non-invasive and non-ionizing biomedical imaging modality that emerged in recent years.The articles presented in this special issue describe some of newest progress in this field.We are extremely grateful to all contributing authors.The first part of the issue covers new laser source devel-opment,including fiber lasers and laser diodes.The sec-ond part is dedicated to improving the image resolution through chronic cranial window techniques,virtual-point concept,fast polygon scanning,and Fabry Perot sensing.The third part shows the basic principles of photoacous-tic/ultrasound imaging and its applications.
基金This work is in part funded by Walk-for-Beauty Foundation and Carol M.Baldwin Breast Cancer Research Foundation.
文摘Radiomic analysis has exponentially increased the amount of quantitative data that can be extracted from a single image.These imaging biomarkers can aid in the generation of prediction models aimed to further personalized medicine.However,the generalizability of the model is dependent on the robustness of these features.The purpose of this study is to review the current literature regarding robustness of radiomic features on magnetic resonance imaging.Additionally,a phantom study is performed to systematically evaluate the behavior of radiomic features under various conditions(signal to noise ratio,region of interest delineation,voxel size change and normalization methods)using intraclass correlation coefficients.The features extracted in this phantom study include first order,shape,gray level cooccurrence matrix and gray level run length matrix.Many features are found to be non-robust to changing parameters.Feature robustness assessment prior to feature selection,especially in the case of combining multi-institutional data,may be warranted.Further investigation is needed in this area of research.
基金the National Science Fund for Distinguished Young Scholars,No.61425002the National Natural Science Foundation of China,Nos.91748104,61632006,61877008+3 种基金Program for ChangJiang Scholars and Innovative Research Team in University,No.IRT_15R07Program for the Liaoning Distinguished Professor,Program for Dalian High-level Talent Innovation Support,No.2017RD11the Scientific Research fund of Liaoning Provincial Education Department,No.L2019606the Science and Technology Innovation Fund of Dalian,No.2018J12GX036.
文摘With the rapid development of deep learning technology,behavior recognition based on video streams has made great progress in recent years.However,there are also some problems that must be solved:(1)In order to improve behavior recognition performance,the models have tended to become deeper,wider,and more complex.However,some new problems have been introduced also,such as that their real-time performance decreases;(2)Some actions in existing datasets are so similar that they are difficult to distinguish.To solve these problems,the ResNet34-3DRes18 model,which is a lightweight and efficient two-dimensional(2D)and three-dimensional(3D)fused model,is constructed in this study.The model used 2D convolutional neural network(2DCNN)to obtain the feature maps of input images and 3D convolutional neural network(3DCNN)to process the temporal relationships between frames,which made the model not only make use of 3DCNN’s advantages on video temporal modeling but reduced model complexity.Compared with state-of-the-art models,this method has shown excellent performance at a faster speed.Furthermore,to distinguish between similar motions in the datasets,an attention gate mechanism is added,and a Res34-SE-IM-Net attention recognition model is constructed.The Res34-SE-IM-Net achieved 71.85%,92.196%,and 36.5%top-1 accuracy(The predicting label obtained from model is the largest one in the output probability vector.If the label is the same as the target label of the motion,the classification is correct.)respectively on the test sets of the HMDB51,UCF101,and Something-Something v1 datasets.
基金The studies mentioned in this paper were supported in part by Grants R01 CA160205 and R01 CA197150 from the National Cancer Institute,National Institutes of Health,USAGrant HR15-016 from Oklahoma Center for the Advancement of Science and Technology,USA.
文摘In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.
文摘One of the most critical steps in medical health is the proper diagnosis of the disease.Dermatology is one of the most volatile and challenging fields in terms of diagnosis.Dermatologists often require further testing,review of the patient’s history,and other data to ensure a proper diagnosis.Therefore,finding a method that can guarantee a proper trusted diagnosis quickly is essential.Several approaches have been developed over the years to facilitate the diagnosis based on machine learning.However,the developed systems lack certain properties,such as high accuracy.This study proposes a system developed in MATLAB that can identify skin lesions and classify them as normal or benign.The classification process is effectuated by implementing the K-nearest neighbor(KNN)approach to differentiate between normal skin and malignant skin lesions that imply pathology.KNN is used because it is time efficient and promises highly accurate results.The accuracy of the system reached 98%in classifying skin lesions.
文摘Although the education sector is improving more quickly than ever with the help of advancing technologies,there are still many areas yet to be discovered,and there will always be room for further enhancements.Two of the most disruptive technologies,machine learning(ML)and blockchain,have helped replace conventional approaches used in the education sector with highly technical and effective methods.In this study,a system is proposed that combines these two radiant technologies and helps resolve problems such as forgeries of educational records and fake degrees.The idea here is that if these technologies can be merged and a system can be developed that uses blockchain to store student data and ML to accurately predict the future job roles for students after graduation,the problems of further counterfeiting and insecurity in the student achievements can be avoided.Further,ML models will be used to train and predict valid data.This system will provide the university with an official decentralized database of student records who have graduated from there.In addition,this system provides employers with a platform where the educational records of the employees can be verified.Students can share their educational information in their e-portfolios on platforms such as LinkedIn,which is a platform for managing professional profiles.This allows students,companies,and other industries to find approval for student data more easily.
