An image mosaic technique with non-overlapping regions based on microscopic vision in precision assembly

Microscopic vision has been widely applied in precision assembly.To achieve sufficiently high resolution in measurements for precision assembly when the sizes of the parts involved exceed the field of view of the vision system,an image mosaic technique must be used.In this paper,a method for constructing an image mosaic with non-overlapping areas with enhanced efficiency is proposed.First,an image mosaic model for the part is created using a geometric model of the measurement system installed on a X-Y-Z precision stages with high repeatability,and a path for image acquisition is established.Second,images are captured along the same path for a specified calibration plate,and an entire image is formed based on the given model.The measurement results obtained from the specified calibration plate are utilized to identify mosaic errors and apply compensation for the part requiring measurement.Experimental results show that the maximum error is less than 4μm for a camera with pixel equivalent 2.46μm,thereby demonstrating the accuracy of the proposed method.This image mosaic technique with non-overlapping regions can simplify image acquisition and reduce the workload involved in constructing an image mosaic.

Spatial-Spectral Joint Network for Cholangiocarcinoma Microscopic Hyperspectral Image Classification

Accurate histopathology classification is a crucial factor in the diagnosis and treatment of Cholangiocarcinoma(CCA).Hyperspectral images(HSI)provide rich spectral information than ordinary RGB images,making them more useful for medical diagnosis.The Convolutional Neural Network(CNN)is commonly employed in hyperspectral image classification due to its remarkable capacity for feature extraction and image classification.However,many existing CNN-based HSI classification methods tend to ignore the importance of image spatial context information and the interdependence between spectral channels,leading to unsatisfied classification performance.Thus,to address these issues,this paper proposes a Spatial-Spectral Joint Network(SSJN)model for hyperspectral image classification that utilizes spatial self-attention and spectral feature extraction.The SSJN model is derived from the ResNet18 network and implemented with the non-local and Coordinate Attention(CA)modules,which extract long-range dependencies on image space and enhance spatial features through the Branch Attention(BA)module to emphasize the region of interest.Furthermore,the SSJN model employs Conv-LSTM modules to extract long-range depen-dencies in the image spectral domain.This addresses the gradient disappearance/explosion phenom-ena and enhances the model classification accuracy.The experimental results show that the pro-posed SSJN model is more efficient in leveraging the spatial and spectral information of hyperspec-tral images on multidimensional microspectral datasets of CCA,leading to higher classification accuracy,and may have useful references for medical diagnosis of CCA.

Two-photon live imaging of direct glia-to-neuron conversion in the mouse cortex

Over the past decade,a growing number of studies have reported transcription factor-based in situ reprogramming that can directly conve rt endogenous glial cells into functional neurons as an alternative approach for n euro regeneration in the adult mammalian central ne rvous system.Howeve r,many questions remain regarding how a terminally differentiated glial cell can transform into a delicate neuron that forms part of the intricate brain circuitry.In addition,concerns have recently been raised around the absence of astrocyte-to-neuron conversion in astrocytic lineage-tra cing mice.In this study,we employed repetitive two-photon imaging to continuously capture the in situ astrocyte-to-neuron conversion process following ecto pic expression of the neural transcription factor NeuroD1 in both prolife rating reactive astrocytes and lineage-tra ced astrocytes in the mouse cortex.Time-lapse imaging over several wee ks revealed the ste p-by-step transition from a typical astrocyte with numero us short,tapered branches to a typical neuro n with a few long neurites and dynamic growth cones that actively explored the local environment.In addition,these lineage-converting cells were able to migrate ra dially or to ngentially to relocate to suitable positions.Furthermore,two-photon Ca2+imaging and patch-clamp recordings confirmed that the newly generated neuro ns exhibited synchronous calcium signals,repetitive action potentials,and spontaneous synaptic responses,suggesting that they had made functional synaptic connections within local neural circuits.In conclusion,we directly visualized the step-by-step lineage conversion process from astrocytes to functional neurons in vivo and unambiguously demonstrated that adult mammalian brains are highly plastic with respect to their potential for neuro regeneration and neural circuit reconstruction.

Two-photon polymerization lithography for imaging optics

Optical imaging systems have greatly extended human visual capabilities,enabling the observation and understanding of diverse phenomena.Imaging technologies span a broad spectrum of wavelengths from x-ray to radio frequencies and impact research activities and our daily lives.Traditional glass lenses are fabricated through a series of complex processes,while polymers offer versatility and ease of production.However,modern applications often require complex lens assemblies,driving the need for miniaturization and advanced designs with micro-and nanoscale features to surpass the capabilities of traditional fabrication methods.Three-dimensional(3D)printing,or additive manufacturing,presents a solution to these challenges with benefits of rapid prototyping,customized geometries,and efficient production,particularly suited for miniaturized optical imaging devices.Various 3D printing methods have demonstrated advantages over traditional counterparts,yet challenges remain in achieving nanoscale resolutions.Two-photon polymerization lithography(TPL),a nanoscale 3D printing technique,enables the fabrication of intricate structures beyond the optical diffraction limit via the nonlinear process of two-photon absorption within liquid resin.It offers unprecedented abilities,e.g.alignment-free fabrication,micro-and nanoscale capabilities,and rapid prototyping of almost arbitrary complex 3D nanostructures.In this review,we emphasize the importance of the criteria for optical performance evaluation of imaging devices,discuss material properties relevant to TPL,fabrication techniques,and highlight the application of TPL in optical imaging.As the first panoramic review on this topic,it will equip researchers with foundational knowledge and recent advancements of TPL for imaging optics,promoting a deeper understanding of the field.By leveraging on its high-resolution capability,extensive material range,and true 3D processing,alongside advances in materials,fabrication,and design,we envisage disruptive solutions to current challenges and a promising incorporation of TPL in future optical imaging applications.

Deep learning of rock microscopic images for intelligent lithology identification: Neural network comparison and selection

An intelligent lithology identification method is proposed based on deep learning of the rock microscopic images.Based on the characteristics of rock images in the dataset,we used Xception,MobileNet_v2,Inception_ResNet_v2,Inception_v3,Densenet121,ResNet101_v2,and ResNet-101 to develop microscopic image classification models,and then the network structures of seven different convolutional neural networks(CNNs)were compared.It shows that the multi-layer representation of rock features can be represented through convolution structures,thus better feature robustness can be achieved.For the loss function,cross-entropy is used to back propagate the weight parameters layer by layer,and the accuracy of the network is improved by frequent iterative training.We expanded a self-built dataset by using transfer learning and data augmentation.Next,accuracy(acc)and frames per second(fps)were used as the evaluation indexes to assess the accuracy and speed of model identification.The results show that the Xception-based model has the optimum performance,with an accuracy of 97.66%in the training dataset and 98.65%in the testing dataset.Furthermore,the fps of the model is 50.76,and the model is feasible to deploy under different hardware conditions and meets the requirements of rapid lithology identification.This proposed method is proved to be robust and versatile in generalization performance,and it is suitable for both geologists and engineers to identify lithology quickly.

Automatic Leukaemia Segmentation Approach for Blood Cancer Classification Using Microscopic Images

Leukaemia is a type of blood cancer that is caused by undeveloped White Blood Cells(WBC),and it is also called a blast blood cell.In the marrow of human bones,leukaemia is developed and is responsible for blood cell generation with leukocytes and WBC,and if any cell gets blasted,then it may become a cause of death.Therefore,the diagnosis of leukaemia in its early stages helps greatly in the treatment along with saving human lives.Subsequently,in terms of detection,image segmentation techniques play a vital role,and they turn out to be the important image processing steps for the extraction of feature patterns from the Acute Lymphoblastic Leukaemia(ALL)type of blood cancer.Moreover,the image segmentation technique focuses on the division of cells by segmenting a microscopic image into background and cancer blood cell nucleus,which is well-known as the Region Of Interest(ROI).As a result,in this article,we attempt to build a segmentation technique capable of solving blood cell nucleus segmentation issues using four distinct scenarios,including K-means,FCM(Fuzzy Cmeans),K-means with FFA(Firefly Algorithm),and FCM with FFA.Also,we determine the most effective method of blood cell nucleus segmentation,which we subsequently use for the Leukaemia classification model.Finally,using the Convolution Neural Network(CNN)as a classifier,we developed a leukaemia cancer classification model from the microscopic images.The proposed system’s classification accuracy is tested using the CNN to test the model on the ALL-IDB dataset and equate it to the current state of the art.In terms of experimental analysis,we observed that the accuracy of the model is near to 99%,and it is far better than other existing models that are designed to segment and classify the types of leukaemia cancer in terms of ALL.

The Primary Study of Microscopical Focus Problem in the Color Image of Pigmented Spots

Pigmented spot is an important branch in the science of skin. But when processing those images, the microscopical focusing problem arises. It affects the image recognition later. In order to find the best method to solve it, comparison and analysis are given to various existing methods of image fusion in this paper . The conclusion is wavelet transform based on pixel-level.

Adaptive Gradient-Based and Anisotropic Diffusion Equation Filtering Algorithm for Microscopic Image Preprocessing

In image acquisition process, the quality of microscopic images will be degraded by electrical noise, quantizing noise, light illumination etc. Hence, image preprocessing is necessary and important to improve the quality. The background noise and pulse noise are two common types of noise existing in microscopic images. In this paper, a gradient-based anisotropic filtering algorithm was proposed, which can filter out the background noise while preserve object boundary effectively. The filtering performance was evaluated by comparing that with some other filtering algorithms.

Microscopic cracking behaviors of rocks under uniaxial compression with microscopic multiphase heterogeneity by deep learning

Cracking behaviors of rocks significantly affect the safety and stability of the explorations of underground space and deep resources.To understand deeply the microscopic cracking process and mechanical property of rocks,X-ray micro-computed tomography(X-μCT)is applied to capture the rock microstructures.The digital color difference UNet(DCD-UNet)-based deep learning algorithm with 3D reconstruction is proposed to reconstruct the multiphase heterogeneity microstructure models of rocks.The microscopic cracking and mechanical properties are studied based on the proposed microstructure-based peridynamic model.Results show that the DCD-UNet algorithm is more effective to recognize and to represent the microscopic multiphase heterogeneity of rocks.As damage characteristic index of multiphase rocks increases,transgranular cracks in the same grain phase,transgranular and intergranular cracks of pore-grain phase,intergranular and secondary transgranular cracks and transgranular crack between different grains propagate.The ultimate microscopic failure modes of rocks are mainly controlled by the transgranular cracks-based T1-shear,T3-shear,T1-tension,T2-tension and T3-tension failures,and the intergranular cracks-based T1-tension,T1-shear and T3-shear failures under uniaxial compression.

A simple Bessel module with tunable focal depth and constant resolution for commercial two-photon microscope

The volumetric imaging of two-photon microscopy expands the focal depth and improves the throughput,which has unparalleled superiority for three-dimension samples,especially in neuroscience.However,emerging in volumetric imaging is still largely customized,which limits the integration with commercial two-photon systems.Here,we analyzed the key parameters that modulate the focal depth and lateral resolution of polarized annular imaging and proposed a volumetric imaging module that can be directly integrated into commercial two-photon systems using conventional optical elements.This design incorporates the beam diameter adjustment settings of commercial two-photon systems,allowing flexibility to adjust the depth of focus while maintaining the same lateral resolution.Further,the depth range and lateral resolution of the design were verified,and the imaging throughput was demonstrated by an increase in the number of imaging neurons in the awake mouse cerebral cortex.

Spatial-spectral identication of abnormal leukocytes based on microscopic hyperspectral imaging technology

Screening and diagnosing of abnormal Leukocytes are crucial for the diagnosis of immune diseases and Acute Lymphoblastic Leukemia(ALL).As the deterioration of abnormal leukocytes is mainly due to the changes in the chromatin distribution,which signicantly affects the absorption and reflection of light,the spectral feature is proved to be important for leukocytes classication and identication.This paper proposes an accurate identication method for healthy and abnormal leukocytes based on microscopic hyperspectral imaging(HSI)technology which combines the spectral information.The segmentation of nucleus and cytoplasm is obtained by the morphological watershed algorithm.Then,the spectral features are extracted and combined with the spatial features.Based on this,the support vector machine(SVM)is applied for classication ofve types of leukocytes and abnormal leukocytes.Compared with different classication methods,the proposed method utilizes spectral features which highlight the differences between healthy leukocytes and abnormal leukocytes,improving the accuracy in the classication and identication of leukocytes.This paper only selects one subtype of ALL for test,and the proposed method can be applied for detection of other leukemia in the future.

Monitoring microenvironment of Hep G2 cell apoptosis using two-photon fluorescence lifetime imaging microscopy

Apoptosis is very important for the maintenance of cellular homeostasis and is closely related to the occurrence and treatment of many diseases.Mitochondria in cells play a crucial role in programmed cell death and redox processes.Nicotinamide adenine dinucleotide(NAD(P)H)is the primary producer of energy in mitochondria,changing NAD(P)H can directly reflect the physiological state of mitochondria.Therefore,NAD(P)H can be used to evaluate metabolic response.In this paper,we propose a noninvasive detection method that uses two-photon fluorescence lifetime imaging microscopy(TP-FLIM)to characterize apoptosis by observing the binding kinetics of cellular endogenous NAD(P)H.The result shows that the average fluorescence lifetime of NAD(P)H and the fluorescence lifetime of protein-bound NAD(P)H will be affected by the changing pH,serum content,and oxygen concentration in the cell culture environment,and by the treatment with reagents such as H2O2 and paclitaxel.Taxol(PTX).This noninvasive detection method realized the dynamic detection of cellular endogenous substances and the assessment of apoptosis.

Retrieving analog images from a scanning electron microscopewith a synchronous data acquisition method

In this work,an old scanning electron microscope(SEM)is refurbished to enhance its image processing capability.How to digitally sample and process an analog image is also presented.An NI PCI-6259 multiple input/output data acquisition(DAQ)board is used to acquire signals originally being sent to an analog display,and then convert the signals into a digital image.Two output channels are used for raster scan of the horizontal and verticle axes of the image buffer,while one input channel is used to read the brightness signals at various coordinate points.Synchronous method is used to maximize the DAQ speed.Finally,the digitally buffered images are read out to display and saved in a hard drive.The hardware and software designs of this work are explained in great detail,which can serve as a very good example for fast synchronous DAQ,advanced virtual instrument design and structural driver programming with LabVIEW.

In vivo imaging of the neuronal response to spinal cord injury:a narrative review

Deciphering the neuronal response to injury in the spinal cord is essential for exploring treatment strategies for spinal cord injury(SCI).However,this subject has been neglected in part because appropriate tools are lacking.Emerging in vivo imaging and labeling methods offer great potential for observing dynamic neural processes in the central nervous system in conditions of health and disease.This review first discusses in vivo imaging of the mouse spinal cord with a focus on the latest imaging techniques,and then analyzes the dynamic biological response of spinal cord sensory and motor neurons to SCI.We then summarize and compare the techniques behind these studies and clarify the advantages of in vivo imaging compared with traditional neuroscience examinations.Finally,we identify the challenges and possible solutions for spinal cord neuron imaging.

Image-Pro Plus软件在小麦淀粉粒显微图像分析中的应用

以小麦胚乳淀粉粒的光学显微镜和扫描电子显微镜图像为研究对象,运用image-pro plus(IPP)图像分析软件研究了小麦淀粉粒面积、圆度、长轴、短轴、最大弗雷特直径和最小弗雷特直径等几何特征,并根据最大轴长实现了图像中淀粉粒的抽出及自动排序。与Smileview和Image J图像分析软件相比,IPP具有在淀粉粒SEM图像分析中误差最小、能测量的颗粒物质参数最多的优点。

用Image J分析水稻胚乳淀粉粒表面几何特征的方法

以水稻淀粉粒光学显微镜和扫描电子显微镜图像为研究对象,介绍了图像分析软件Image J在淀粉粒表面特征分析中的应用。图像分析结果以轮廓图(outline)和拟合椭圆(ellipse fitting)两种方式呈现,分析指标选择了面积、周长、长轴、短轴、圆度及最大切直径。最后还对颗粒图像的获取和图像前处理进行了探讨。

Femtosecond Two-Photon Detachment of Cu^- Studied By Photoelectron Imaging

The wavelength dependence of photoelectron angular distributions （PADs） of two-photon detachment of Cu^- has been directly studied by using the photoelectron map imaging. Results show that for the laser field intensity of 6.0×10^10W/cm^2, PADs exhibit dramatic change with the external field wavelength. Comparison between the experimental observation and the lowest-order perturbation theory prediction indicates that the pattern of PADs can be explained by the interference of the s and d partial waves in the final state. Relative contri- butions of s and d partial waves in the two-photon detachment at different laser wavelengths are obtained.

Non-invasive imaging of pathological scars using a portable handheld two-photon microscope

Background:Pathological scars are a disorder that can lead to various cosmetic,psychological,and functional problems,and no effective assessment methods are currently available.Assessment and treatment of pathological scars are based on cutaneous manifestations.A two-photon microscope(TPM)with the potential for real-time non-invasive assessment may help determine the under-surface pathophysiological conditions in vivo.This study used a portable handheld TPM to image epidermal cells and dermal collagen structures in pathological scars and normal skin in vivo to evaluate the effectiveness of treatment in scar patients.Methods:Fifteen patients with pathological scars and three healthy controls were recruited.Imaging was performed using a portable handheld TPM.Five indexes were extracted from two dimensional(2D)and three dimensional(3D)perspectives,including collagen depth,dermo-epidermal junction(DEJ)contour ratio,thickness,orientation,and occupation(proportion of collagen fibers in the field of view)of collagen.Two depth-dependent indexes were computed through the 3D second harmonic generation image and three morphology-related indexes from the 2D images.We assessed index differences between scar and normal skin and changes before and after treatment.Results:Pathological scars and normal skin differed markedly regarding the epidermal morphological structure and the spectral characteristics of collagen fibers.Five indexes were employed to distinguish between normal skin and scar tissue.Statistically significant differences were found in average depth(t=9.917,P<0.001),thickness(t=4.037,P<0.001),occupation(t=2.169,P<0.050),orientation of collagen(t=3.669,P<0.001),and the DEJ contour ratio(t=5.105,P<0.001).Conclusions:Use of portable handheld TPM can distinguish collagen from skin tissues;thus,it is more suitable for scar imaging than reflectance confocal microscopy.Thus,a TPM may be an auxiliary tool for scar treatment selection and assessing treatment efficacy.

Effects of microscopic pore structure heterogeneity on the distribution and morphology of remaining oil

Waterflooding experiments were performed using Micro-CT on four cores of different pore structures from Donghe sandstone reservoirs in the Tarim Basin. The water, oil and grains were accurately separated by the advanced image processing technology, the pore network model was established, and parameters such as the number of throats and the throat size distribution were calculated to characterize the microscopic heterogeneity of pore structure, the flow of oil phase during displacement, and the morphology and distribution of remaining oil after displacement. The cores with the same macroscopic porosity-permeability have great differences in microscopic heterogeneity of pore structure. Both macro porosity-permeability and micro heterogeneity of pore structure have an influence on the migration of oil phase and the morphology and distribution of remaining oil. When the heterogeneity is strong, the water phase will preferentially flow through the dominant paths and the remaining oil clusters will be formed in the small pores. The more the number of oil clusters(droplets) formed during displacement process, the smaller the average volume of cluster is, and the remaining oil is dominated by the cluster continuous phase with high saturation. The weaker the heterogeneity, the higher the pore sweep efficiency is, and the remaining oil clusters are mainly trapped in the form of non-continuous phase. The distribution and morphology of micro remaining oil are related to the absolute permeability, capillary number and micro-heterogeneity. So, the identification plate of microscopic residual oil continuity distribution established on this basis can describe the relationship between these three factors and distribution of remaining oil and identify the continuity of the remaining oil distribution accurately.

A Leukocyte image fast scanning based on max–min distance clustering

A leukocyte image fast scanning method based on max min distance clustering is proposed.Because of the lower proportion and uneven distribution of leukocytes in human peripheral blood,there will not be any leukocyte in lager quantity of the captured images if we directly scan the blood smear along an ordinary zigzag scanning routine with high power(100^(x))objective.Due to the larger field of view of low power(10^(x))objective,the captured low power blood smear images can be used to locate leukocytes.All of the located positions make up a specific routine,if we scan the blood smear along this routine with high power objective,there will be definitely leukocytes in almost all of the captured images.Considering the number of captured images is still large and some leukocytes may be redundantly captured twice or more,a leukocyte clustering method based on max-min distance clustering is developed to reduce the total number of captured images as well as the number of redundantly captured leukocytes.This method can improve the scanning eficiency obviously.The experimental results show that the proposed method can shorten scanning time from 8.0-14.0min to 2.54.0 min while extracting 110 nonredundant individual high power leukocyte images.