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.

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.

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.

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.

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。

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.

Micro-Scanning Error Correction Technique for an Optical Micro-Scanning Thermal Microscope Imaging System

An error correction technique for the micro-scanning instrument of the optical micro-scanning thermal microscope imaging system is proposed. The technique is based on micro-scanning technology combined with the proposed second-order oversampling reconstruction algorithm and local gradient image reconstruction algorithm. In this paper, we describe the local gradient image reconstruction model, the error correction technique, down-sampling model and the error correction principle. In this paper, we use a Lena original image and four low-resolution images obtained from the standard half-pixel displacement to simulate and verify the effectiveness of the proposed technique. In order to verify the effectiveness of the proposed technique, two groups of low-resolution thermal microscope images are collected by the actual thermal microscope imaging system for experimental study. Simulations and experiments show that the proposed technique can reduce the optical micro-scanning errors, improve the imaging effect of the system and improve the system's spatial resolution. It can be applied to other electro-optical imaging systems to improve their resolution.

Optical micro-scanning location calibration of thermal microscope imaging system

A method of micro-scanning location adaptive calibration was proposed, which was real- ized by the digital image micro-displacement estimation. With geometric calculation, this calibration method used the displacement estimation of two thermal microscope images to get the size and direc- tion of each scanning location calibration angle. And each location calibration process was repeated according to the offset given by the system beforehand. The comparison experiments of sequence oversampling reconstruction before and after the micro-scanning location calibration were done. The results showed that the calibration method effectively improved the thermal microscope imaging qual- ity.

Deep learning method for cell count from transmitted-light microscope

Automatic cell counting provides an effective tool for medical research and diagnosis.Currently,cell counting can be completed by transmitted-light microscope,however,it requires expert knowledge and the counting accuracy which is unsatisfied for overlapped cells.Further,the image-translation-based detection method has been proposed and the potential has been shown to accomplish cell counting from transmitted-light microscope,automatically and effectively.In this work,a new deep-learning(DL)-based two-stage detection method(cGAN-YOLO)is designed to further enhance the performance of cell counting,which is achieved by combining a DL-based fluorescent image translation model and a DL-based cell detection model.The various results show that cGAN-YOLO can effectively detect and count some different types of cells from the acquired transmitted-light microscope images.Compared with the previously reported YOLO-based one-stage detection method,high recognition accuracy(RA)is achieved by the cGAN-YOLO method,with an improvement of 29.80%.Furthermore,we can also observe that cGAN-YOLO obtains an improvement of 12.11%in RA compared with the previously reported image-translation-based detection method.In a word,cGAN-YOLO makes it possible to implement cell counting directly from the experimental acquired transmitted-light microscopy images with high flexibility and performance,which extends the applicability in clinical research.

Evaluation of Sub-microstructure in Concrete with Low Water-binder Ratio by SEM-BSE Image Analysis

The coarse pore system, interfacial transition zone （ITZ） between aggregate and paste matrix and volume fraction of unhydrated cement in concrete （w/c=0.3） containing mineral admixtures were quantitatively characterized by the scanning electron microscope-backscattered electron （SEM-BSE） image analysis technique. The experimental results show that compound addition of slag and fly ash decreases the coarse porosity from 10.17% to 3.74% and the threshold diameter of coarse pore size from 345 μm to 105 μm compared with concrete （w/c=0.30） without mineral admixtures; Moreover with compound addition of fly ash and slag, the volume proportion of unhydrated cement in paste matrix is reduced by 30%, the maximum amount of coarse pores in the ITZ between aggregate and paste decreases from 13.11% to 5.57% and the thickness of ITZ is reduced by 37% , compared with concrete without mineral admixtures.

X-ray phase-sensitive microscope imaging with a grating interferometer:Theory and simulation

A general theoretical framework is presented to explain the formation of the phase signal in an x-ray microscope integrated with a grating interferometer,which simultaneously enables the high spatial resolution imaging and the improved image contrast.By using this theory,several key parameters of phase contrast imaging can be predicted,for instance,the fringe visibility and period,and the conversion condition from the differential phase imaging(DPI)to the phase difference imaging(PDI).Additionally,numerical simulations are performed with certain x-ray optical components and imaging geometry.Comparison with the available experimental measurement[Appl.Phys.Lett.113063105(2018)]demonstrates the accuracy of this developed quantitative analysis method of x-ray phase-sensitive microscope imaging.

Online Investigation of Optical Properties and Craze Propagation of Drawn Polypropylene Fiber by Using Interferometric Image Analysis

Pluta polarizing interference microscope was used to follow the crazing that occur on the surface of stretched polypropylene fibres at different drawing conditions. The samples were stretched until crazing initiated, and then craze propagation was monitored as a function of drawing speed and test temperature. The effect of craze dimension on their propagation velocity was taken into account. Three-dimensional birefringence profile for crazed polypropylene fibre has been demonstrated to investigate the birefringence of crazed fibre at different test times for fixed drawing speed value. Also the mean birefringence values of crazed polypropylene fibres were calculated and the results showed that, these values increased with the areal craze density. Video images were used to calculate the craze velocity. Optical micrographs and microinterferograms were presented for demonstrations.

Oversample Reconstruction Based on a Strong Inter-Diagonal Matrix for an Optical Microscanning Thermal Microscope Imaging System

Based on a strong inter-diagonal matrix and Taylor series expansions,an oversample reconstruction method was proposed to calibrate the optical micro-scanning error. The technique can obtain regular 2 ×2 microscanning undersampling images from the real irregular undersampling images,and can then obtain a high spatial oversample resolution image. Simulations and experiments show that the proposed technique can reduce optical micro-scanning error and improve the system＇s spatial resolution. The algorithm is simple,fast and has low computational complexity. It can also be applied to other electro-optical imaging systems to improve their spatial resolution and has a widespread application prospect.