3D characterization and analysis of pore structure of packed ore particle beds based on computed tomography images

Methods and procedures of three-dimensional （3D） characterization of the pore structure features in the packed ore particle bed are focused. X-ray computed tomography was applied to deriving the cross-sectional images of specimens with single particle size of 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10 ram. Based on the in-house developed 3D image analysis programs using Matlab, the volume porosity, pore size distribution and degree of connectivity were calculated and analyzed in detail. The results indicate that the volume porosity, the mean diameter of pores and the effective pore size （d50） increase with the increasing of particle size. Lognormal distribution or Gauss distribution is mostly suitable to model the pore size distribution. The degree of connectivity investigated on the basis of cluster-labeling algorithm also increases with increasing the particle size approximately.

Analysis of Two-Dimensional Gel Electrophoresis Images of Protein from Posterior Silk Gland of Silkworm (Bombyx mori) on Day 1 and Day 4 in the 5th Instar Stage

The posterior silk gland （PSG） of silkworm is an important organ where fibroin is synthesized and secreted exclusively. Because fibroin constitutes 75-80% of the silk filament, the mechanism governing fibroin secretion, quality and yield of cocoon can be elucidated by the study on the PSG. Using two-dimensional gel electrophoresis （2-DE） and image analysis system, the changes in the protein composition in the PSG cell were investigated on the day 1 （D1） and day 4 （D4） in the 5th instar stage from five different strains of silkworm （Bombyx mori）. While differences at protein level between days and strains were far less than those observed at the gene level using EST analysis. The change trends in protein composition from D1 to D4 were diverse among the different strains. The results suggest that the secretion of fibroin is regulated by multiple proteins. The site of regulation and the proteins responsible for the regulation vary with the strain, which leads to differences between strains in the capacity of fibroin secretion in the PSG cell.

Unified Analysis Specific to the Medical Field in the Interpretation of Medical Images through the Use of Deep Learning

Deep learning (DL) has seen an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image. The purpose of the work converges in determining the importance of each component, describing the specificity and correlations of these elements involved in achieving the precision of interpretation of medical images using DL. The major contribution of this work is primarily to the updated characterisation of the characteristics of the constituent elements of the deep learning process, scientific data, methods of knowledge incorporation, DL models according to the objectives for which they were designed and the presentation of medical applications in accordance with these tasks. Secondly, it describes the specific correlations between the quality, type and volume of data, the deep learning patterns used in the interpretation of diagnostic medical images and their applications in medicine. Finally presents problems and directions of future research. Data quality and volume, annotations and labels, identification and automatic extraction of specific medical terms can help deep learning models perform image analysis tasks. Moreover, the development of models capable of extracting unattended features and easily incorporated into the architecture of DL networks and the development of techniques to search for a certain network architecture according to the objectives set lead to performance in the interpretation of medical images.

Wavelet Analysis of Space Solar Telescope Images

The scientific satellite SST (Space Solar Telescope) is an important research project strongly supported by the Chinese Academy of Sciences. Every day, SST acquires 50 GB of data (after processing) but only 10GB can be transmitted to the ground because of limited time of satellite passage and limited channel volume. Therefore, the data must be compressed before transmission. Wavelets analysis is a new technique developed over the last 10 years, with great potential of application. We start with a brief introduction to the essential principles of wavelet analysis, and then describe the main idea of embedded zerotree wavelet coding, used for compressing the SST images. The results show that this coding is adequate for the job.

DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

Application of Optical Flow Analysis to Shadowgraph Images of Impinging Jet

In this study, we apply the optical flow method to the time-series shadowgraph images of impinging jets using a high-speed video camera with high spatial and temporal resolution. This image analysis provides quantitative velocity vector fields in the object space without tracer particles. The analysis results clearly capture the details of the coherent vortex structure and its advection from the shear layer of the free jet. Although the results still leave challenges for the quantitative validation, the results show that this analysis method is effective for understanding the details of the physical phenomenon based on the quantitative values extracted from the shadowgraph images.

THE ANALYSIS OF REMOTE SENSING IMAGES FOR ACTIVE FAULTS AND EARTHQUAKES IN CHINA

The Landsat image information has recently been widely applied to structural geology, especially to the analysis of lineaments, owing to their macroscopic, visual and comprehensive features. The images will be more effective when applied to the interpretation of active faults. Active faults are widely ditributed in China. Much attention has been paid to the study of active faults both in China and abroad. There is certain controversy concerning the implication of the term "active fault". Strictly speaking, the term should refer only to the faults that are still active in the present day. However, the term also usually refers to the faults which have been active continually or intermittently from the Quaternary (or the end of Tertiary) to the present day. We propose that the tones and the configurations of features on Landsat images are the principal keys to the interpretation of active faults. The faults, which display the most prominent

Revisiting Sphere Unfolding Relationships for the Stereological Analysis of Segmented Digital Microstructure Images

Sphere unfolding relationships are revisited with a specific focus on the analysis of segmented digital images of microstructures. Since the features of such images are most easily quantified by counting pixels, the required equations are re-derived in terms of the histogram of areas (instead of diameters or radii) as inputs and it is shown that a substitution can be made that simplifies the calculation. A practical method is presented for utilizing negative number fraction bins (which sometimes arise from erroneous assumptions and/or insufficient numbers of observations) for the creation of error bars. The complete algorithm can be implemented in a spreadsheet. The derived unfolding equations are explored using both linear and logarithmic binning schemes, and the pros and cons of both binning schemes are illustrated using simulated data. The effects of the binning schemes on the stereological results are demonstrated and discussed with reference to their consequences for practical materials characterization situations, allowing for the suggestion of guidelines for proper application of this, and other, distribution-free stereological methods.

Geometric prior guided hybrid deep neural network for facial beauty analysis

Facial beauty analysis is an important topic in human society.It may be used as a guidance for face beautification applications such as cosmetic surgery.Deep neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable performance.However,most existing DNN-based models regard facial beauty analysis as a normal classification task.They ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty analysis.To be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the decision.Inspired by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial parts.Additionally,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two branches.In model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric features.Experiments performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid network.To the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research

With the advancement of retinal imaging,hyperreflective foci(HRF)on optical coherence tomography(OCT)images have gained significant attention as potential biological biomarkers for retinal neuroinflammation.However,these biomarkers,represented by HRF,present pose challenges in terms of localization,quantification,and require substantial time and resources.In recent years,the progress and utilization of artificial intelligence(AI)have provided powerful tools for the analysis of biological markers.AI technology enables use machine learning(ML),deep learning(DL)and other technologies to precise characterization of changes in biological biomarkers during disease progression and facilitates quantitative assessments.Based on ophthalmic images,AI has significant implications for early screening,diagnostic grading,treatment efficacy evaluation,treatment recommendations,and prognosis development in common ophthalmic diseases.Moreover,it will help reduce the reliance of the healthcare system on human labor,which has the potential to simplify and expedite clinical trials,enhance the reliability and professionalism of disease management,and improve the prediction of adverse events.This article offers a comprehensive review of the application of AI in combination with HRF on OCT images in ophthalmic diseases including age-related macular degeneration(AMD),diabetic macular edema(DME),retinal vein occlusion(RVO)and other retinal diseases and presents prospects for their utilization.

Pre-training transformer with dual-branch context content module for table detection in document images

Background Document images such as statistical reports and scientific journals are widely used in information technology.Accurate detection of table areas in document images is an essential prerequisite for tasks such as information extraction.However,because of the diversity in the shapes and sizes of tables,existing table detection methods adapted from general object detection algorithms,have not yet achieved satisfactory results.Incorrect detection results might lead to the loss of critical information.Methods Therefore,we propose a novel end-to-end trainable deep network combined with a self-supervised pretraining transformer for feature extraction to minimize incorrect detections.To better deal with table areas of different shapes and sizes,we added a dualbranch context content attention module(DCCAM)to high-dimensional features to extract context content information,thereby enhancing the network's ability to learn shape features.For feature fusion at different scales,we replaced the original 3×3 convolution with a multilayer residual module,which contains enhanced gradient flow information to improve the feature representation and extraction capability.Results We evaluated our method on public document datasets and compared it with previous methods,which achieved state-of-the-art results in terms of evaluation metrics such as recall and F1-score.https://github.com/Yong Z-Lee/TD-DCCAM.

Assessment and Visualization of Ki67 Heterogeneity in Breast Cancers through Digital Image Analysis

The Ki67 index (KI) is a standard clinical marker for tumor proliferation;however, its application is hindered by intratumoral heterogeneity. In this study, we used digital image analysis to comprehensively analyze Ki67 heterogeneity and distribution patterns in breast carcinoma. Using Smart Pathology software, we digitized and analyzed 42 excised breast carcinoma Ki67 slides. Boxplots, histograms, and heat maps were generated to illustrate the KI distribution. We found that 30% of cases (13/42) exhibited discrepancies between global and hotspot KI when using a 14% KI threshold for classification. Patients with higher global or hotspot KI values displayed greater heterogenicity. Ki67 distribution patterns were categorized as randomly distributed (52%, 22/42), peripheral (43%, 18/42), and centered (5%, 2/42). Our sampling simulator indicated analyzing more than 10 high-power fields was typically required to accurately estimate global KI, with sampling size being correlated with heterogeneity. In conclusion, using digital image analysis in whole-slide images allows for comprehensive Ki67 profile assessment, shedding light on heterogeneity and distribution patterns. This spatial information can facilitate KI surveys of breast cancer and other malignancies.

Fractal analysis of polyferric chloride-humic acid(PFC-HA) flocs in different topological spaces

The fractal dimensions in different topological spaces of polyferric chloride-humic acid （PFC-HA） flocs, formed in flocculating different kinds of humic acids （HA） water at different initial pH （9.0, 7.0, 5.0） and PFC dosages, were calculated by effective densitymaximum diameter, image analysis, and N2 absorption-desorption methods, respectively. The mass fractal dimensions （De） of PFC-HA floes were calculated by bi-logarithm relation of effective density with maximum diameter and Logan empirical equation. The Df value was more than 2.0 at initial pH of 7,0, which was 11% and 13% higher than those at pH 9.0 and 5.0, respecively, indicating the most compact flocs formed in flocculated HA water at initial pH of 7.0. The image analysis for those flocs indicates that after flocculating the HA water at initial pH greater than 7.0 with PFC flocculant, the fractal dimensions of D2 （logA vs. logdL） and D3 （logVsphere vs. logdL） of PFC-HA floes decreased with the increase of PFC dosages, and PFC-HA floes showed a gradually looser structure. At the optimum dosage of PFC, the D2 （logA vs. logdL） values of the flocs show 14%-43% difference with their corresponding Dr, and they even had different tendency with the change of initial pH values. However, the D2 values of the floes formed at three different initial pH in HA solution had a same tendency with the corresponding Df. Based on fractal Frenkel-Halsey-HiU （FHH） adsorption and desorption equations, the pore surface fractal dimensions （Ds） for dried powders of PFC-HA flocs formed in HA water with initial pH 9.0 and 7.0 were all close to 2.9421, and the Ds values of flocs formed at initial pH 5.0 were less than 2.3746. It indicated that the pore surface fractal dimensions of PFC-HA floes dried powder mainly show the irregularity from the mesopore-size distribution and marcopore-size distribution.

Use of high-resolution X-ray computed tomography and 3D image analysis to quantify mineral dissemination and pore space in oxide copper ore particles

Mineral dissemination and pore space distribution in ore particles are important features that influence heap leaching performance. To quantify the mineral dissemination and pore space distribution of an ore particle, a cylindrical copper oxide ore sample (I center dot 4.6 mm x 5.6 mm) was scanned using high-resolution X-ray computed tomography (HRXCT), a nondestructive imaging technology, at a spatial resolution of 4.85 mu m. Combined with three-dimensional (3D) image analysis techniques, the main mineral phases and pore space were segmented and the volume fraction of each phase was calculated. In addition, the mass fraction of each mineral phase was estimated and the result was validated with that obtained using traditional techniques. Furthermore, the pore phase features, including the pore size distribution, pore surface area, pore fractal dimension, pore centerline, and the pore connectivity, were investigated quantitatively. The pore space analysis results indicate that the pore size distribution closely fits a log-normal distribution and that the pore space morphology is complicated, with a large surface area and low connectivity. This study demonstrates that the combination of HRXCT and 3D image analysis is an effective tool for acquiring 3D mineralogical and pore structural data.

Segmentation algorithm of complex ore images based on templates transformation and reconstruction

Lots of noises and heterogeneous objects with various sizes coexist in a complex image,such as an ore image;the classical image thresholding method cannot effectively distinguish between ores.To segment ore objects with various sizes simultaneously,two adaptive windows in the image were chosen for each pixel;the gray value of windows was calculated by Otsu＇s threshold method.To extract the object skeleton,the definition principle of distance transformation templates was proposed.The ores linked together in a binary image were separated by distance transformation and gray reconstruction.The seed region of each object was picked up from the local maximum gray region of the reconstruction image.Starting from these seed regions,the watershed method was used to segment ore object effectively.The proposed algorithm marks and segments most objects from complex images precisely.

Review of Remotely Sensed Imagery Classification Patterns Based on Object-oriented Image Analysis

With the wide use of high-resolution remotely sensed imagery, the object-oriented remotely sensed informa- tion classification pattern has been intensively studied. Starting with the definition of object-oriented remotely sensed information classification pattern and a literature review of related research progress, this paper sums up 4 developing phases of object-oriented classification pattern during the past 20 years. Then, we discuss the three aspects of method- ology in detail, namely remotely sensed imagery segmentation, feature analysis and feature selection, and classification rule generation, through comparing them with remotely sensed information classification method based on per-pixel. At last, this paper presents several points that need to be paid attention to in the future studies on object-oriented RS in- formation classification pattern: 1) developing robust and highly effective image segmentation algorithm for multi-spectral RS imagery; 2) improving the feature-set including edge, spatial-adjacent and temporal characteristics; 3) discussing the classification rule generation classifier based on the decision tree; 4) presenting evaluation methods for classification result by object-oriented classification pattern.

Micromorphological Analysis of Soil Structure under No Tillage Management in the Black Soil Zone of Northeast China

The structure of the ＂black soil＂ in Northeast China has been greatly deteriorated by long-term intensive conventional mouldboard plow tillage （CT） practices. In this study, micro- morphological observation and image analysis of soil thin sections were conducted to evaluate the impacts of 21 years （1986-2007） of no tillage （NT） on soil structure as compared to CT in an experiment near Gongzhuling City, Jilin Province. Soil organic matter （SOM）, wet aggregate stability and saturated hydraulic conductivity （Ks） were also analyzed. Total SOM was not significantly affected by tillage systems, but fresher SOM was observed in the surface layer under NT. The aggregates under NT showed different hierarchies in the form of crumbs, and the mean weight diameter （MWD） of NT was significant higher than that of CT in the surface layer. Platy and blocky aggregates were frequently observed in the lower layers under CT practice. The compound pore structure with intertwined intra- and inter- aggregates pores under NT was well developed in a layer from 0-5 cm to 20-25 era. While under CT system, more inter-aggregate pores and fewer intra- aggregate pores were observed, and planes and channels were frequently found in the 20-25 cm layer, where maeroporosity decreased significantly and a plow pan was evident. The Ks values of NT weresignificantly lower at o-5 cm but significantly higher at 20-95 cm compared with CT, which showed the same trend with macroporosity. These results confirmed that long-term CT practice fragmented the tillage layer soil and compacted the lower layer soil and formed a plow pan. While long-term NT practice in the black soil region favored soil aggregation and a stable porous soil structure was formed, which are important to the water infiltration and prevent soil erosion.

Image Analysis on Detachment Process of Dust Cake on Ceramic Candle Filter

Based on the analysis of high-speed video images, the detachment behavior of dust cake from the ceramic candle filter surface during pulse cleaning process is investigated. The influences of the dust cake loading,the reservoir pressure, and the filtration velocity on the cleaning effectiveness are analyzed. Experimental results show that there exists an optimum dust cake thickness for pulse-cleaning process. For thin dust cake, the patchy cleaning exists and the cleaning efficiency is low; if the dust cake is too thick, the pressure drop across the dust cake becomes higher and a higher reservoir pressure may be needed. At the same time there also exists an optimum reservoir pressure for a given filtration condition.

Correlative Analysis on the Relationship between PMI and DNA Degradation of Cell Nucleus in Human Different Tissues

Summary： To determining the postmortem interval （PMI） through quantitative analysis of the DNA degradation of cell nucleus in human brain and spleen by using image analysis technique （IAT）. The brain and spleen tissues from 32 cadavers with known PMI were collected, subjected to cell smear every 1 h within the first 5-36 h after death, stained by Feulgen-Van＇s staining, Three indices reflecting DNA in brain cells （astrocytes） and splenic lymphocytes, including integral optical density （IOD）, average optical density （AOD）, average gray （AG） were measured by employing the mage analysis instrument. The results showed that IOD and AOD declined and AG increased with the prolongation of dead time within 5 36 h. A correlation between the PMI and gray parameters （IOD,AOD and AG） was identified and the corresponding regression equation was obtained. The parameters （IOD, AOD and AG） were proved to be effective quantitative indicators for accurate estimation of PMI within 5-36 h after death.

Fluorescence Microscopic Image Analysis of Nucleic Acids Based on The Capillary Flow Directed Assembly Ring of Neutral Red-nucleic Acid Supramolecular Complexes

It is critical to establish a direct and precise method with a high sensitivity and selectivity in analytical chemistry. In this research, making use of a well known phenomenon of capillary flow, we have proposed an image analysis method of nucleic acids at the price of a small amount of sample. When a droplet of the supramolecular complex solution, formed by neutral red and nucleic acids(NA) under an approximate neutral condition, was placed on the hydrophobic surface of dimethyl dichlorosilane pretreated glass slides, and it was evaporated, the supramolecular complex exhibited the periphery of the droplet due to the capillary effect, and accumulated there to form a red capillary flow directed assembly ring(CFDAR). A typical CFDAR has an outer diameter of (2 r ) about 1.18 mm and a ring width(2 δ ) of about 41 μm. Depending on the experimental conditions, a variety of CFDAR can be assembled. The experimental results are in agreement with our former theoretical discussion. It was found that when a droplet volume is 0.1 μL, the fluorescence intensity of the CFDAR formed by the NR NA is in proportion to the content of calf thymus DNA in the range of 0-0.28 ng, fish sperm DNA of 0-0.24 ng and yeast RNA of 0-0.16 ng with the limit of detection(3 σ ) of 1 7, 1.4 and 0.9 pg, respectively for the three nucleic acids.