Image Analysis Technology Research Articles

Karst study of Jinfo Mountain based on image analysis

The KDR (karst development rate) of rocks and their PCR(porosity of carbonate rocks) are common research topics in Jinfo Mountain. The use of traditional carbonate research methods (TCRMs) for karst studies has been shown to be costly and time-consuming. Therefore, this study attempted to find a new, reliable, low-cost, and time-saving method for karst research. The Jinfo Mountain area is a typical carbonate rock area that is suitable for karst research. In this study, many images of rock samples from the Jinfo Mountain were obtained using rock-polarizing microscopes, which provided a good basis for the karst study of Jinfo Mountain. Furthermore, in this study, image analysis technology was used to find the karst development rate of rocks and their porosity. To ensure the accuracy of these research results, we compared the research results obtained using the image analysis techniques with those obtained using TCRM. The comparison showed that the image analysis technology is a feasible research techniques for studying karst in the Jinfo Mountain area. Furthermore, it has good reference significance for other karst study outside the Jinfo Mountain area.

CCGL-YOLOV5:A cross-modal cross-scale global-local attention YOLOV5 lung tumor detection model

BackgroundMultimodal medical image detection is a key technology in medical image analysis, which plays an important role in tumor diagnosis. There are different sizes lesions and different shapes lesions in multimodal lung tumor images, which makes it difficult to effectively extract key features of lung tumor lesions. MethodsA Cross-modal Cross-scale Clobal-Local Attention YOLOV5 Lung Tumor Detection Model (CCGL-YOLOV5) is proposed in this paper. The main works are as follows: Firstly, the Cross-Modal Fusion Transformer Module (CMFTM) is designed to improve the multimodal key lesion feature extraction ability and fusion ability through the interactive assisted fusion of multimodal features; Secondly, the Global-Local Feature Interaction Module (GLFIM) is proposed to enhance the interaction ability between multimodal global features and multimodal local features through bidirectional interactive branches. Thirdly, the Cross-Scale Attention Fusion Module (CSAFM) is designed to obtain rich multi-scale features through grouping multi-scale attention for feature fusion. ResultsThe comparison experiments with advanced networks are done. The Acc, Rec, mAP, F1 score and FPS of CCGL-YOLOV5 model on multimodal lung tumor PET/CT dataset are 97.83%, 97.39%, 96.67%, 97.61% and 98.59, respectively; The experimental results show that the performance of CCGL-YOLOV5 model in this paper are better than other typical models. ConclusionThe CCGL-YOLOV5 model can effectively use the multimodal feature information. There are important implications for multimodal medical image research and clinical disease diagnosis in CCGL-YOLOV5 model.

Application of Fractal Theory to the Analysis of Failure Characteristics of Low-Velocity-Impact Concrete Slabs

The fractal characteristics of low-velocity-impact concrete slabs were studied using fractal theory, and the fractal dimension value of cracks of each concrete specimen plate was calculated using box dimension as the basic principle and digital image analysis technology in conjunction with MATLAB software (R2021b) calculation functions. The energy dissipation of concrete slabs during low-velocity impact is calculated using the elastic sheet theory. The calculation results are realistic, and the energy conversion of concrete slabs during low-velocity impact is analyzed based on these results. The research findings indicate that concrete slab cracks exhibit good fractal characteristics during low-velocity impact, and their values can be utilized as a parameter to determine the extent of concrete slab failure. Moreover, the study found that the fractal dimension value of concrete slab cracks and the associated plastic deformation energy display good exponential function characteristics in the energy dissipation mechanism of low-velocity concrete slabs.

Retracted: Data Analysis of Nursing Effects in Pediatric Gastroenterology Department under High Content Image Analysis Technology.

[This retracts the article DOI: 10.1155/2022/4302331.].

A novel high-precision photogrammetric technique for monitoring subway tunnel deformation

This paper presents a low cost and high precision tunnel deformation monitoring system based on network camera photogrammetry. The system uses digital image technology to analyze and process the reflective mark of the tunnel structure monitoring section in real time, enabling real-time monitoring and early warning of deformations in the tunnel structure. This paper focuses on how to effectively improve the monitoring accuracy of image analysis technology in photogrammetric system, specifically by achieving a deformation accuracy of 0.1 mm through the use of images with a resolution of 1 mm/pix. The robustness of the analysis algorithm and methods of optimizing and reducing the computational efforts of the algorithm for real-time analysis are also discussed. The effectiveness of these methods is verified through the deformation monitoring of the Tianjin subway tunnel.

Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review.

Given the rapid development of social media, effective extraction and analysis of the contents of social media for health care have attracted widespread attention from health care providers. As far as we know, most of the reviews focus on the application of social media, and there is a lack of reviews that integrate the methods for analyzing social media information for health care. This scoping review aims to answer the following 4 questions: (1) What types of research have been used to investigate social media for health care, (2) what methods have been used to analyze the existing health information on social media, (3) what indicators should be applied to collect and evaluate the characteristics of methods for analyzing the contents of social media for health care, and (4) what are the current problems and development directions of methods used to analyze the contents of social media for health care? A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. We searched PubMed, the Web of Science, EMBASE, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library for the period from 2010 to May 2023 for primary studies focusing on social media and health care. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted. Of 16,161 identified citations, 134 (0.8%) studies were included in this review. These included 67 (50.0%) qualitative designs, 43 (32.1%) quantitative designs, and 24 (17.9%) mixed methods designs. The applied research methods were classified based on the following aspects: (1) manual analysis methods (content analysis methodology, grounded theory, ethnography, classification analysis, thematic analysis, and scoring tables) and computer-aided analysis methods (latent Dirichlet allocation, support vector machine, probabilistic clustering, image analysis, topic modeling, sentiment analysis, and other natural language processing technologies), (2) categories of research contents, and (3) health care areas (health practice, health services, and health education). Based on an extensive literature review, we investigated the methods for analyzing the contents of social media for health care to determine the main applications, differences, trends, and existing problems. We also discussed the implications for the future. Traditional content analysis is still the mainstream method for analyzing social media content, and future research may be combined with big data research. With the progress of computers, mobile phones, smartwatches, and other smart devices, social media information sources will become more diversified. Future research can combine new sources, such as pictures, videos, and physiological signals, with online social networking to adapt to the development trend of the internet. More medical information talents need to be trained in the future to better solve the problem of network information analysis. Overall, this scoping review can be useful for a large audience that includes researchers entering the field.

Study on Dynamic Contact Behavior of Multi-Component Droplet and Dust Surface

The dynamic contact behavior between multi-component droplets and the surface of iron ore dust was taken as the research object, analysis of the maximum spreading coefficient, maximum acting diameter, maximum acting area, and maximum bouncing height of solid-liquid contact, from a microscopic perspective, using high-speed photography and image analysis and processing technology. The experimental results indicate that (1) with the particle size of dust particles decreases, the solid-liquid contact behavior sequentially manifests as spread immediately after broken, retraction, negative bounce, primary bounce, and secondary bounce. (2) When the surface tension of the droplets decreases from 55.5 to 34.8 mN/m, the maximum spreading diameter of the droplet has increased by 30% to 40%, the maximum bounce heights (coefficients) decreased by 100%, 57.14%, and 53.57%, respectively, the maximum spreading coefficient of the droplet exhibits no obvious pattern. (3) With decreasing droplet surface tension, the unidirectional acting diameter and the maximum acting area increase when the dust surface size is over 100 μm. When the surface particle size is less than 100 μm, there is no significant change in the unidirectional acting diameter and maximum acting area despite decreasing surface tension. Thus, droplet diffusion is mainly influenced by particle size. These findings contribute to enhancing the theory of water mist dust removal and improving dust removal efficiency.

Self-Training with Entropy-Based Mixup for Low-Resource Chest X-ray Classification

Deep learning-based medical image analysis technology has been developed to the extent that it shows an accuracy surpassing the ability of a human radiologist in some tasks. However, data labeling on medical images requires human experts and a great deal of time and expense. Moreover, medical image data usually have an imbalanced distribution for each disease. In particular, in multilabel classification, learning with a small number of labeled data causes overfitting problems. The model easily overfits the limited number of labeled data, while it still underfits the large amount of unlabeled data. In this study, we propose a method that combines entropy-based Mixup and self-training to improve the performance of data-imbalanced chest X-ray classification. The proposed method is to apply the Mixup algorithm to limited labeled data to alleviate the data imbalance problem and perform self-training that effectively utilizes the unlabeled data while iterating this process by replacing the teacher model with the student model. Experimental results in an environment with a limited number of labeled data and a large number of unlabeled data showed that the classification performance was improved by combining entropy-based Mixup and self-training.

A Perspective on AI-Based Image Analysis and Utilization Technologies in Building Engineering: Recent Developments and New Directions

Artificial Intelligence (AI) is a trending topic in many research areas. In recent years, even building, civil, and structural engineering have also started to face with several new techniques and technologies belonging to this field, such as smart algorithms, big data analysis, deep learning practices, etc. This perspective paper collects the last developments on the use of AI in building engineering, highlighting what the authors consider the most stimulating scientific advancements of recent years, with a specific interest in the acquisition and processing of photographic surveys. Specifically, the authors want to focus both on the applications of artificial intelligence in the field of building engineering, as well as on the evolution of recently widespread technological equipment and tools, emphasizing their mutual integration. Therefore, seven macro-categories have been identified where these issues are addressed: photomodeling; thermal imaging; object recognition; inspections assisted by UAVs; FEM and BIM implementation; structural monitoring; and damage identification. For each category, the main new innovations and the leading research perspectives are highlighted. The article closes with a brief discussion of the primary results and a viewpoint for future lines of research.

Human motion analysis and measurement techniques: current application and developing trend

Human motion analysis and measurement technology have been widely used in the fields of medical treatment, sports science, and rehabilitation. In clinical practice, motion analysis has been applied in the diagnosis and individualized treatment planning of various musculoskeletal diseases, and it is also an important objective scientific method to evaluate the therapeutic effect and the effectiveness of medical equipment. This study aimed to introduce the common modern motion capture measurement technology and equipment, the clinical application and limitations of motion analysis, and the possible development trend of motion analysis measuring techniques in the future. Motion analysis and measurement systems and medical image measurement and analysis technology have made landmark improvements over the past few decades in terms of orthopaedical biomechanics. Nevertheless, limitations still exist, both subjective and objective. All these drawbacks have promoted the exploration of the integrated methods that have now been widely used in motion analysis. The results of the case study about the subject-specific finite element modeling of the foot and sports shoe complex have also shown great consistency. Nevertheless, several possible future directions for motion analysis measuring techniques still exist. In the future, the progress of motion analysis and measurement methods will simultaneously drive the progress of orthopedics, rehabilitation, precision personalized medicine, and medical engineering.

RGB-T image analysis technology and application: A survey

RGB-Thermal infrared (RGB-T) image analysis has been actively studied in recent years. In the past decade, it has received wide attention and made a lot of important research progress in many applications. This paper provides a comprehensive review of RGB-T image analysis technology and application, including several hot fields: image fusion, salient object detection, semantic segmentation, pedestrian detection, object tracking, and person re-identification. The first two belong to the preprocessing technology for many computer vision tasks, and the rest belong to the application direction. This paper extensively reviews 400+ papers spanning more than 10 different application tasks. Furthermore, for each specific task, this paper comprehensively analyzes the various methods and presents the performance of the state-of-the-art methods. This paper also makes an in-deep analysis of challenges for RGB-T image analysis as well as some potential technical improvements in the future.

Monitoring System of Deep Learning Video Image Analysis Technology Based on Smart Agriculture

Monitoring System of Deep Learning Video Image Analysis Technology Based on Smart Agriculture

Relationships between pretreatment methods and properties of ion-electroactive polymers based on image analysis technology

In recent years, ion-electroactive polymers have attracted much attention because of their good flexibility, but the effect mechanisms of their pretreatment on the electrode and actuation property are not clear. In this paper, the intrinsic fractal characteristics of the interface contact of ion-electroactive polymers are characterized based on two-dimensional scanning images. The main objective of this study is to investigate the effects of different pretreatment methods on the property of ion-electroactive polymers. Based on the image analysis technology, the contact contour of the interface between the electrode and the polymer membrane was extracted using the processing software, and the fractal dimension and Hurst index of the interface were calculated by programming the calculation code using the root mean square (RMS) method and the rescaled range analysis (RRA) method. The obtained fractal parameters were then applied to the derivation of the capacitance at the interface and the effect of the fractal dimension on the displacement was discussed. The results show that the vertical preprocessing of the ion-electroactive polymer can significantly increase the capacitance and thus facilitate the output of the actuation displacement. It is a feasible way to analyze the fractal characteristics of interface by two-dimensional image analysis technology.

Retracted: Application of Multimodal Fusion Technology in Image Analysis of Pretreatment Examination of Patients with Spinal Injury.

[This retracts the article DOI: 10.1155/2022/4326638.].

Development of Feature Extraction System for WADA Kunibo's works Focusing on Color

This study proposes a system to extract the characteristics of the works of Kunibo Wada, an artist from Kagawa Prefecture, Japan, using image analysis technology. It is difficult for young designers to learn the style of Kunibo’s works of art simply by visiting art museums and viewing his works. Therefore, we have developed a system that helps young designers to understand the style of Kunibo’s works more easily than they have been able to do in the past. The system analyzes Kunibo’s style using artificial intelligence and helps young designers to understand his style by classifying works with similar styles. In particular, we developed an algorithm to extract features related to accent colors and "Kasumi" a technique for emphasizing objects. The results of evaluation experiments showed that both the precision rate and recall rate were sufficient for young designers to understand the style, which was the objective of the project. We expect that young designers will be inspired by the style of existing works of art to transmit culture from a new perspective, and to redesign and develop their works across fields.

Retracted: Deep Learning Technology in Pathological Image Analysis of Breast Tissue.

[This retracts the article DOI: 10.1155/2021/9610830.].

The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use.

Artificial intelligence (AI)-based diagnostic technology using medical images can be used to increase examination accessibility and support clinical decision-making for screening and diagnosis. To determine a machine learning algorithm for diabetes complications, a literature review of studies using medical image-based AI technology was conducted using the National Library of Medicine PubMed, and the Excerpta Medica databases. Lists of studies using diabetes diagnostic images and AI as keywords were combined. In total, 227 appropriate studies were selected. Diabetic retinopathy studies using the AI model were the most frequent (85.0%, 193/227 cases), followed by diabetic foot (7.9%, 18/227 cases) and diabetic neuropathy (2.7%, 6/227 cases). The studies used open datasets (42.3%, 96/227 cases) or directly constructed data from fundoscopy or optical coherence tomography (57.7%, 131/227 cases). Major limitations in AI-based detection of diabetes complications using medical images were the lack of datasets (36.1%, 82/227 cases) and severity misclassification (26.4%, 60/227 cases). Although it remains difficult to use and fully trust AI-based imaging analysis technology clinically, it reduces clinicians' time and labor, and the expectations from its decision-support roles are high. Various data collection and synthesis data technology developments according to the disease severity are required to solve data imbalance.

A review of different deep learning techniques for sperm fertility prediction

<abstract> <p>Sperm morphology analysis (SMA) is a significant factor in diagnosing male infertility. Therefore, healthy sperm detection is of great significance in this process. However, the traditional manual microscopic sperm detection methods have the disadvantages of a long detection cycle, low detection accuracy in large orders, and very complex fertility prediction. Therefore, it is meaningful to apply computer image analysis technology to the field of fertility prediction. Computer image analysis can give high precision and high efficiency in detecting sperm cells. In this article, first, we analyze the existing sperm detection techniques in chronological order, from traditional image processing and machine learning to deep learning methods in segmentation and classification. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. Finally, the future development direction and challenges of sperm cell detection are discussed. We have summarized 44 related technical papers from 2012 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of fertility prediction and provide a reference for researchers in other fields.</p> </abstract>

GPS 기반 드론과 손상 객체 거리 값 산출 방안

Recently, the growth rate of old facilities in Korea is expected to reach 20% in 2025 and 74.2% in 2040, and social issues and efficiency issues of inspection methods are emerging due to the increase in old facilities. Accordingly, research to find a solution is being actively conducted in the country and research organizations, and research on damage detection and evaluation of facilities using drone-based inspection methods or digital image analysis technology is being actively conducted for maintenance and safety inspection. However, most damage assessment studies using existing digital image analysis technology are not appropriate for inspection drone-based facilities to increase the efficiency of existing inspection methods using a method of maintaining a fixed distance and acquiring data. Therefore, in this study, we present a method for calculating the distance from the damaged object based on GPS of the image acquired through the drone so that the efficiency of the existing inspection method can be improved and damage evaluation can be carried out based on the image acquired by the drone.

Burning Skin Detection System in Human Body

Early accurate burn depth diagnosis is crucial for selecting the appropriate clinical intervention strategies and assessing burn patient prognosis quality. However, with limited diagnostic accuracy, the current burn depth diagnosis approach still primarily relies on the empirical subjective assessment of clinicians. With the quick development of artificial intelligence technology, integration of deep learning algorithms with image analysis technology can more accurately identify and evaluate the information in medical images. The objective of the work is to detect and classify burn area in medical images using an unsupervised deep learning algorithm. The main contribution is to developing computations using one of the deep learning algorithm. To demonstrate the effectiveness of the proposed framework, experiments are performed on the benchmark to evaluate system stability. The results indicate that, the proposed system is simple and suits real life applications. The system accuracy was 75%, when compared with some of the state-of-the-art techniques.