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Simulation of Structural Characteristics and Depth Filtration Elements in Interconnected Nanofibrous Membrane Based on Adaptive image analysis

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World Journal of Nano Science and Engineering 2013年第1期3卷 6-16页

作者： Mohammad Kazemi Pilehrood Pirjo Heikkila Ali Harlin VTT Technical Research Centre of Finland EspooFinland

Due to their unique structural features, electrospun membranes have gained considerable attention for use in applications where quality of depth filtration is a dominant performance factor. To elucidate the depth filtration phenomena it is important to quantify the intrinsic structural properties independent from the dynamics of transport media. Several methods have been proposed for structural characterization of such membranes. However, these methods do not meet the requirement for the quantification of intrinsic structural properties in depth filtration. This may be due to the complex influence of transport media dynamics and structural elements in the depth filtration process. In addition, the different morphological architectures of electrospun membranes present obstacles to precise quantification. This paper seeks to quantify the structural characteristics of electrospun membranes by introducing a robust image analysis technique and exploiting it to evaluate the permeation-filtration mechanism. To this end, a nanostructured fibrous network was simulated as an ideal membrane using adaptive local criteria in the image analysis. The reliability of the proposed approach was validated with measurements and comparison of structural characteristics in different morphological conditions. The results were found to be well compatible with empirical observations of perfect membrane structures. This approach, based on optimization of electrospinning parameters, may pave the way for producing optimal membrane structures for boosting the performance of electrospun membranes in end-use applications.

关键词： Nanofibrous Membrane image analysis Local Criteria Structural Characteristics Pore Interconnectivity Depth Filtration

image Feature Extraction Method based on Shape Characteristics and its Application in Medical image analysis

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Image Feature Extraction Method based on Shape Characteristi...

image analysis for In-situ Detection of Agglomeration for Needle-like Crystals

2010 The 3rd International Conference on Computational Intelligence and Industrial Application (PACIIA2010)

作者： Jianhua Liu Department of Sofeware Institute North China University of Water Resources and Electric Power Zhengzhou Henan Province China Yanling Shi Department of Information Engineering North China University of Water Resources and Electric Power Zhengzhou Henan Province China

With the continuous development and perfection of computer-aided diagnosis technology, image feature extraction methods are more and more used in medical image analysis, which increase diagnostic accuracy and work efficiency of doctors. In this article, common image shape features and their characteristics are introduced. Methods of shape characteristics are summarized, and their applications in medical image analysis are introduced.

关键词： image analysis Feature Exaction Shape Characteristics Recognition.

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Image Analysis for In-situ Detection of Agglomeration for Ne...

Machine learning for enumeration of cell colony forming units

第36届中国控制会议

作者： Kang Zou Tao Liu Yan Huo Fangkun Zhang Xiongwei Ni Institute of Advanced Control Technology Dalian University of Technology School of Engineering and Physical Science Heriot-Watt University

ISBN: (纸本)9781538629185

A synthetic image analysis method is proposed for in-situ detection of particle agglomeration for monitoring crystallization processes,based on using a non-invasive imaging system.The proposed method consists of image pre-processing,feature analysis,shape identification,and re-segmentation.Firstly,in-situ captured images are pre-processed to eliminate the influence from uneven illumination background and particle motion.Then,based on choosing the fundamental image features of needle-like crystals,a texture computation algorithm is established with a gray level co-occurrence matrix（GLCM） defined for different particle types.Subsequently,a shape identification algorithm is given to distinguish the primary particles from overlapped particles in a captured image.Finally,a re-segmentation algorithm is constructed to separate individual crystals from the overlapped crystals,by using a geometric approach and the chord-to-point distance accumulation（CPDA） technique,and then pseudo agglomerates are recognized from the overlapped crystals based on the texture analysis.Experimental results on the cooling crystallization of β form L-glutamic acid well demonstrate the effectiveness of the proposed image analysis method.

关键词： image analysis agglomeration texture feature re-segmentation

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Visual Computing for Industry,Biomedicine,and Art 2022年第1期5卷 315-322页

作者： Louis Zhang Department of Molecular Biosciences College of Natural SciencesUniversity of Texas at AustinAustin TX 78713-8058USA

As one of the most widely used assays in biological research,an enumeration of the bacterial cell colonies is an important but time-consuming and labor-intensive process.To speed up the colony counting,a machine learning method is presented for counting the colony forming units(CFUs),which is referred to as CFUCounter.This cellcounting program processes digital images and segments bacterial colonies.The algorithm combines unsupervised machine learning,iterative adaptive thresholding,and local-minima-based watershed segmentation to enable an accurate and robust cell counting.Compared to a manual counting method,CFUCounter supports color-based CFU classification,allows plates containing heterologous colonies to be counted individually,and demonstrates overall performance(slope 0.996,SD 0.013,95%CI:0.97–1.02,p value<1e-11,r=0.999)indistinguishable from the gold standard of point-and-click counting.This CFUCounter application is open-source and easy to use as a unique addition to the arsenal of colony-counting tools.

关键词： Colony forming unit Enumeration image analysis Blue/white screen Colony count

Estimation of body weight in captive Amur tigers(Panthera tigris altaica)

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Integrative Zoology 2022年第6期17卷 1106-1120页

作者： Qi WANG Chunmei SHI Dan LIU Guangshun JIANG School of Mental Health Wenzhou Medical UniversityWenzhouChina Feline Research Center of National Forestry and Grassland Administration College of Wildlife and Protected AreaNortheast Forestry UniversityHarbinChina College of Mathematics and Computer Science Zhejiang Agriculture and Forestry UniversityHangzhouChina Siberian Tiger Park HarbinChina

So far,there has been no safe and convenient method to weigh the largeﬁerce animals,like Amur tigers.To address this problem,we built models to predict the body weight of Amur tigers based on the fact that body weight is proportional to body measurements or age.Using the method of body measurements,we extracted the body measurements from 4 different kinds of the lateral body image of tigers,that is,total lateral image,central lateral image,ellipseﬁtting image,and rectangleﬁtting image,and then we respectively used artiﬁcial neural network(ANN)and power regression model to analyze the predictive relationships between body weight and body measurements.Our results demonstrated that,among all ANN models,the model built with rectangleﬁtting image had the smallest mean square error.Comparatively,we screened power regression models which had the smallest Akakai information criteria(AIC).In addition,using the method of age,weﬁtted nonlinear regression models for the relationship between body weight and age and found that,for male tigers,logistic model had the smallest AIC.For female tigers,Gompertz model had the smallest AIC.Consequently,this study could be applied to estimate body weight of captive,or even wild,Amur tigers safely and conveniently,helping to monitor individual health and growth of the Amur tiger populations.

关键词： Amur tiger body weight growth curve image analysis Panthera tigris altaica

Use of a tissue clearing technique combined with retrograde trans-synaptic viral tracing to evaluate changes in mouse retinorecipient brain regions following optic nerve crush

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Neural Regeneration Research 2023年第4期18卷 913-921页

作者： Zong-Yi Zhan Yi-Ru Huang Lu-Wei Zhao Ya-Dan Quan Zi-Jing Li Di-Fang Sun Ya-Li Wu Hao-Yuan Wu Zi-Tian Liu Kai-Li Wu Yu-Qing Lan Min-Bin Yu Department of Ophthalmology Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene RegulationSun Yat-sen Memorial HospitalSun Yat-sen UniversityGuangzhouGuangdong ProvinceChina State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangzhouGuangdong ProvinceChina Light Innovation Technology Ltd. Hong Kong Special Administrative RegionChina Department of Ophthalmology&Visual Science Shanghai Medical CollegeFudan UniversityShanghaiChina Department of Ophthalmology Eye&ENT HospitalFudan UniversityShanghaiChina Department of Ophthalmology The First Affiliated Hospital of USTCDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhui ProvinceChina

Successful establishment of reconnection between retinal ganglion cells and retinorecipient regions in the brain is critical to optic nerve regeneration.However,morphological assessments of retinorecipient regions are limited by the opacity of brain tissue.In this study,we used an innovative tissue cleaning technique combined with retrograde trans-synaptic viral tracing to observe changes in retinorecipient regions connected to retinal ganglion cells in mice after optic nerve injury.Specifically,we performed light-sheet imaging of whole brain tissue after a clearing process.We found that pseudorabies virus 724(PRV724)mostly infected retinal ganglion cells,and that we could use it to retrogradely trace the retinorecipient regions in whole tissue-cleared brains.Unexpectedly,PRV724-traced neurons were more widely distributed compared with data from previous studies.We found that optic nerve injury could selectively modify projections from retinal ganglion cells in the hypothalamic paraventricular nucleus,intergeniculate leaflet,ventral lateral geniculate nucleus,central amygdala,basolateral amygdala,Edinger-Westphal nucleus,and oculomotor nucleus,but not the superior vestibular nucleus,red nucleus,locus coeruleus,gigantocellular reticular nucleus,or facial nerve nucleus.Our findings demonstrate that the tissue clearing technique,combined with retrograde trans-synaptic viral tracing,can be used to objectively and comprehensively evaluate changes in mouse retinorecipient regions that receive projections from retinal ganglion cells after optic nerve injury.Thus,our approach may be useful for future estimations of optic nerve injury and regeneration.

关键词： histology image analysis light-sheet imaging optic nerve crush pseudorabies virus retinal ganglion cells three-dimensional imaging tissue clearing viral tracing whole brain study

Non-invasive sensing techniques to phenotype multiple apple tree architectures

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Information Processing in Agriculture 2023年第1期10卷 136-147页

作者： Chongyuan Zhang Sara Serra Juan Quiro´s-Vargas Worasit Sangjan Stefano Musacchi Sindhuja Sankaran Department of Biological Systems Engineering Washington State UniversityPullmanWA 99164USA Tree Fruit Research&Extension Center Washington State UniversityWenatcheeWA 98801USA Department of Horticulture Washington State UniversityPullmanWA 99164USA

Tree fruit architecture results from combination of the training system and pruning and thinning processes across multiple growth and development years.Further,the tree fruit architecture contributes to the light interception and improves tree growth,fruit quality,and fruit yield,in addition to easing the process of orchard management and harvest.Currently tree architectural traits are measured manually by researchers or growers,which is labor-intensive and time-consuming.In this study,the remote sensing techniques were evaluated to phenotype critical architectural traits with the final goal to assist tree fruit breeders,physiologists and growers in collecting architectural traits efficiently and in a standardized manner.For this,a consumer-grade red–green–blue(RGB)camera was used to collect apple tree side-images,while an unmanned aerial vehicle(UAV)integrated RGB camera was programmed to image tree canopy at 15 m above ground level to evaluate multiple tree fruit architectures.The sensing data were compared to ground reference data associated with tree orchard blocks within three training systems(Spindle,V-trellis,Biaxis),two rootstocks(‘WA 38 trees grafted on G41 and M9-Nic29)and two pruning methods(referred as bending and click pruning).The data were processed to extract architectural features from ground-based 2D images and UAV-based 3D digital surface model.The traits extracted from sensing data included box-counting fractal dimension(DBs),middle branch angle,number of branches,trunk basal diameter,and tree row volume(TRV).The results from ground-based sensing data indicated that there was a significant(P<0.0001)difference in DBs between Spindle and V-trellis training systems,and correlations between DBs with tree height(r=0.79)and total fruit yield per unit area(r=0.74)were significant(P<0.05).Moreover,correlations between average or total TRV and ground reference data,such as tree height and total fruit yield per unit area,were significant(P<0.05).With the reported findings,this study demonstrated the pot

关键词： Tree training systems Tree row volume Unmanned aerial vehicle image analysis

Review in situ transmission electron microscope with machine learning

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Journal of Semiconductors 2022年第8期43卷 10-23页

作者： Zhiheng Cheng Chaolun Wang Xing Wu Junhao Chu In Situ Devices Center Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghai 200241China

Advanced electronic materials are the fundamental building blocks of integrated circuits(ICs).The microscale properties of electronic materials(e.g.,crystal structures,defects,and chemical properties)can have a considerable impact on the performance of ICs.Comprehensive characterization and analysis of the material in real time with high-spatial resolution are indispensable.In situ transmission electron microscope(TEM)with atomic resolution and external field can be applied as a physical simulation platform to study the evolution of electronic material in working conditions.The high-speed camera of the in situ TEM generates a high frame rate video,resulting in a large dataset that is beyond the data processing ability of researchers using the traditional method.To overcome this challenge,many works on automated TEM analysis by using machine-learning algorithm have been proposed.In this review,we introduce the technical evolution of TEM data acquisition,including analysis,and we summarize the application of machine learning to TEM data analysis in the aspects of morphology,defect,structure,and spectra.Some of the challenges of automated TEM analysis are given in the conclusion.

关键词： electron microscopy machine learning in situ image analysis semiconductor

Experimental study of non-uniform bubbles in a plume

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Journal of Hydrodynamics 2022年第1期34卷 116-124页

作者： Xin-wei Ye Hao-jie Zhou Dong-dong Shao Xiao-jing Niu State Key Laboratory of Hydroscience and Engineering Department of Hydraulic EngineeringTsinghua UniversityBeijing100084China State Key Laboratory of Water Environment Simulation School of EnvironmentBeijing Normal UniversityBeijing100875China

The bubble plume is an important flow phenomenon, ubiquitous in many fields. The non-uniform bubbles in a plume are often simply considered as uniform and moving with a same slip velocity in an integral model. This study aims to have a better understanding of the behavior of the non-uniform bubbles in a plume. A set of experiments are conducted in a transparent water tank, in which the air plumes are generated through different injectors to vary the spectrum of the bubble size. The shadow images of the bubbles are simultaneously recorded with the illumination of a plane backlight. The algorithm used in the image analysis for tracing the bubbles is a newly developed multi-frame method based on the predictor-corrector method, with a good accuracy in estimating the size and the velocity of the overlapped bubbles based on the bubble shadow images. The bubbles are divided into several groups by their equivalent diameters with an interval of 0.5 mm. The spectrums of the bubble size and the void fraction are measured. It is found that the bubbles of different sizes show different dynamics features, resulting in a difference in the radial distribution. It is shown that the large bubbles gather in the center of the plume, while the medium bubbles spread the widest and even wider than the small bubbles. The difference can be explained by the effects of the transverse lift force and the amplitude of the bubble oscillation.

关键词： Bubble plume experiment image analysis bubble size transverse lift force