Size Measurement Method Research Articles

Evaluation of lung function by pore size distribution characteristics of lung tissue based on non-invasive imaging

The evaluation of lung function by measuring the total amount of exhaled gas is not conducive to fully demonstrating the local lesion characteristics of the lung. If the pore size distribution characteristics of lung tissue can be measured, it will be possible to more comprehensively display the lung function characteristics, which will be beneficial for tracking or evaluating the development and rehabilitation effects of lung diseases. Can lung pore size be measured non-invasively? With this question in mind, this article surveys pore size measurement methods for small pores from literature in various fields, such as energy, materials, construction, and food. The study summarizes and introduces methods such as microscopy, small-angle X-ray scattering, computed tomography, low-temperature gas adsorption, mercury porosimetry, low-field nuclear magnetic resonance, low-temperature frozen nuclear magnetic resonance, and nerve fiber measurement. The analysis suggests that magnetic resonance micro-pore measurement technology is a potential feasible method for non-invasive pore size measurement in lung tissue.

Exploring beam size measurement based on the Talbot effect at BEPCII.

Vertical beam size measurements were carried out at Beijing Electron-Positron Collider II (BEPCII) using a phase grating and an absorption grating based on the Talbot effect. The transverse coherence of synchrotron radiation is closely related to beam size. Due to the partial coherence of the synchrotron radiation source, the coherence length can be calculated by measuring the visibility decay of interferograms recorded at different distances behind the gratings. A vertical beam size of 68.19 ± 2 µm was obtained based on the relationship between the coherence length and beam size at the 3W1 beamline of BEPCII. A comparison of the vertical emittance derived from the grating Talbot method and the synchrotron radiation visible light interferometer method was presented. The vertical emittances from the two methods are 1.41 nm rad and 1.40 nm rad, respectively. The 0.1% difference indicates that the grating Talbot method for beam size measurement is reliable. This technique has great potential for small beam size measurement of fourth-generation synchrotron radiation light sources, considering its small diffraction limitation and simple experimental setups.

基于立体视觉的猪只体尺测量系统研究

Body size of pigs is an important evaluation indicator in pig breeding. The traditional method of body size measurement is usually in manual way, which requires more employees and causes stress reactions of pigs. In response to the shortcomings of the traditional methods, this paper designed a system for measuring the body size of pigs based on stereo vision. The point cloud of both the calibration object and the pig was collected using dual KinectV2 cameras. Pre-processing was conducted using filtering and random sample consensus to remove background noise from the point clouds. As there was limited overlap between the two sides of the point clouds, the rotation matrix obtained from registering the calibration object was applied to the pig point clouds. Curve fitting and slicing were then utilized to measure the pig's body dimensions, including length, width, height, and abdominal circumference. The results of the study indicated that the mean absolute percentage error (MAPE) was 2.13% for body length, 1.02% for body width, 1.05% for body height, and 2.21% for abdominal girth. These results demonstrate the high accuracy and practical production value of the system.

Growth assessment of pure ground-glass nodules on CT: comparison of density and size measurement methods.

To investigate the differences of size and density measurements in assessing pure ground-glass nodules (pGGNs) growth, and compare the growth rates and growth proportions of the two methods during follow-up period. Ninety patients with at least 3 consecutive thin-section chest CTs and confirmed 103 pGGNs on baseline CT were enrolled retrospectively. Using the two definitions of size and density to evaluate pGGNs growth with semi-automated segmentation. Then, the two methods were compared to assess differences in pGGNs growth. For the size and density methods to assess nodule growth, 50.5% and 26.2% showed interval growth at the last CT (p < 0.001). Among the 19 nodules that grew in both size and density, the volume doubling time (VDT) of solid component (mean, 317.1; standard deviation, 224.8days) was shorter than total VDT (median, 942.8; range, 400.1-2315.9days) (p < 0.001). Of the 27 growth pGGNs assessed by the density method, the growth rates at years 1 and 2 were 25.9% and 63.0%, while the growth rates of 52 growing nodules assessed by size method were 11.5% and 48.1%, respectively. Twenty of 103 (19.4%) nodules were classified into category 4A lesions, and 7 (6.8%) were 4B lesions. Compared to size measurements, observed density increases have a higher proportion of early growth and faster growth rates in growing nodules. Clinicians need to pay close attention to the nodules of new solid components and make timely decision management.

Analytical performance of single particle aerosol mass spectrometer for accurate sizing and isotopic analysis of individual particles

The commercial Hexin Single particle aerosol mass spectrometer (SPAMS) has been widely used for environmental aerosol monitoring and source apportionment. However, particle size measurement is easily affected by environment pressure fluctuation and sampling orifice clogging. The capability for quantitative analysis is poor, and few isotope measurement has been reported. This paper aims to evaluate the analytical performance of SPAMS and extend its application. First, the flight time of standard particles having different densities and sizes was measured under various conditions (aerodynamic lens upstream pressure and carrier gas). We proposed a universal method for particle size calibration, measurement and correction, taking into account the effects of lens geometry (acceleration nozzle diameter), particle parameters (density, diameter, and shape factor), and operating conditions (lens upstream pressure and carrier gas). Then, isotope measurement was performed when introducing a solution droplet containing a single element. Metal oxide and metal cluster ions were observed in the mass spectrum, indicating incomplete ionization of the sample droplet. The mass discrimination effect was carefully evaluated to correct the measured isotope ratio. Results show that the achievable accuracy of the corrected isotope ratio for elements investigated was 5%. The instrumental performance was relatively poor for elements having great ionization potential or bond energy. Finally, Ag/Eu2O3 suspension and yellow cake/ethanol suspension were analyzed for size, elemental and isotopic analysis. We confirmed that the mass discrimination effect during suspension introduction could be corrected using the mass discrimination correction factor obtained during solution introduction. The Ag, Eu and U in these suspension particles were all found to be at natural abundance. The uranium in the yellow cake was identified as sodium duranyate (Na2U2O7) with volumetric equivalent diameter of approximately 65 nm. The work presented here is beneficial for instrument improvement and wide application.

A high throughput method for egg size measurement in Drosophila

Life-history traits are used as proxies of fitness in insects including Drosophila. Egg size is an adaptive and ecologically important trait potentially with genetic variation across different populations. However, the low throughput of manual measurement of egg size has hampered the widespread use of this trait in evolutionary biology and population genetics. We established a method for accurate and high throughput measurement of Drosophila egg size using large particle flow cytometry (LPFC). The size estimates using LPFC are accurate and highly correlated with the manual measurements. The measurement of egg size is high throughput (average of 214 eggs measured per minute) and viable eggs of a specific size can be sorted rapidly (average of 70 eggs per minute). Sorting by LPFC does not reduce the survival of eggs making it a suitable approach for sorting eggs for downstream analyses. This protocol can be applied to any organism within the detectable size range (10–1500 µm) of the large particle flow cytometers. We discuss the potential applications of this method and provide recommendations for optimizing the protocol for other organisms.

Nanobubble size distribution measurement by interactive force apparatus under an electric field

Nanobubbles have been applied in many fields, such as environmental cleaning, material production, agriculture, and medicine. However, the measured nanobubble sizes differed among the measurement methods, such as dynamic light scattering, particle trajectory, and resonance mass methods. Additionally, the measurement methods were limited with respect to the bubble concentration, refractive index of liquid, and liquid color. Here, a novel interactive force measurement method for bulk nanobubble size measurement was developed by measuring the force between two electrodes filled with bulk nanobubble-containing liquid under an electric field when the electrode distance was changed in the nm scale with piezoelectric equipment. The nanobubble size was measured with a bubble gas diameter and also an effective water thin film layer covered with a gas bubble that was estimated to be approximately 10 nm based on the difference between the median diameter of the particle trajectory method and this method. This method could also be applied to the solid particle size distribution measurement in a solution.

Measurement Method of Inner Surface Size of Hole Based on Distortion Correction of Endoscopic Image

Measurement Method of Inner Surface Size of Hole Based on Distortion Correction of Endoscopic Image

An improved PointNet++ point cloud segmentation model applied to automatic measurement method of pig body size

Currently, non-contact and automatic measurement of livestock body size is the research orientation of computer-aided livestock breeding and intelligent farming industry. However, directly locating the body size measurement key points on the dense overall point clouds of livestock often results in positioning deviation and affects the measurement accuracy. Additionally, various postures of livestock and the interference of different parts of livestock body are also prone to key points positioning deviations. For more accurate measurement of pig body size, we propose in our paper an improved PointNet++ point cloud segmentation model to subdivide the overall pig point clouds into various parts, such as the pig’s head, ears, trunk, limbs, and tail to localize the body measurement key points in the segmented local parts. A new body size measurement method based on segmentation results, which is integrated with the least squares, point cloud slicing, edge extraction, and polynomial fitting, is also presented in our study so that the pig body size parameters can be more accurately calculated. In our experiment, 25 live pigs and 207 groups of pig body point clouds were used for point cloud segmentation and body size measurements. Compared with manual measurements, the relative errors in our experimental results are listed as follows: body length: 2.57 %, body height (front): 2.18 %, body height (back): 2.28 %, body width (front): 4.56 %, body width (middle): 5.26 %, body width (back): 5.19 %, thoracic circumference: 2.50 %, abdominal circumference: 3.14 %, rump circumference: 2.85 %. To conclude, the new automatic measurement method based on the improved PointNet++ point cloud segmentation model with higher accuracy has a more promising application prospect thanks to its novel features, precise measurement results and stable robustness.

Automation of intercept method for grain size measurement: A topological skeleton approach

In the microstructure characterization of metallic materials, the intercept method is one of the most widely accepted approaches to determine average grain size due to its simplicity, accuracy, and the ability to handle both equiaxed and non-equiaxed grain structures. However, its manual implementation is relatively time-consuming and error-prone, and the design of automated implementations is challenging due to the requirement of recognizing, classifying, and scoring different types of intersections (between test patterns and grain boundaries) by international standards such as ASTM E112 and EN ISO 643. In this research, a novel algorithm is proposed to automate the intercept method for grain size measurement from microscopic images. Building on topological skeletons, the algorithm is able to extract continuous and closed grain boundaries from the raw image, and determine the average grain size by recognizing and classifying different types of intersections in accordance with international standards. The effectiveness and efficiency of the proposed algorithm is validated on metallographic microscope images from both high-purity iron and stainless steel. Additionally, our algorithm has been extended to automate other standard grain size measurement methods such as the planimetric method and the whole grain area method.

Optical measurement method of non-spherical particle size and concentration based on high-temperature melting technique

Optical particle counter (OPC) is always used to measure particles in high-temperature gas pipelines through gas cooling, leading to the original molten droplets condensing into irregular solid particles. To solve the problem, an optical measurement method of non-spherical particle size and concentration based on high-temperature melting technique is proposed. By establishing the theoretical dynamic model of optical measuring volume (OMV) of commercial OPC (Welas 3000, PALAS GMBH, Germany) with changing gas temperature and studying the thermal expansion characteristics of aerosol conduit, the influence of gas temperature on OPC optical sensor is analyzed, and OPC is applied at 650 °C. On this foundation, irregular glass particles are melted into spherical shape by high-temperature melting technique to realize the optical measurement of particle size and mass concentration. By comparing the online and off-line results of mass concentration, the deviation between them is less than 5%, verifying the feasibility of the proposed method.

Artificial intelligence-based measurement outperforms current methods for colorectal polyp size measurement.

An accurate polyp size estimation during colonoscopy is crucial to determine the surveillance interval and predict the risk of malignant progression. However, there is a high degree of subjectivity in estimating polyp size among endoscopists in clinical practice. We aimed to assess the efficacy of a novel method that uses artificial intelligence (AI) to measure the size of colon polyps and compare it with current approaches. Using the W-Net model for vessel segmentation and based on retinal image datasets (DRIVE, STARE, CHASE-DB, and HRF) and colonoscopy images, we developed the bifurcation-to-bifurcation (BtoB) distance measuring method and applied it to endoscopic images. Measurements were compared with those obtained by eight endoscopists (four expert and four trainees). Diagnostic ability and reliability were evaluated using Lin's concordance correlation coefficients (CCCs) and Bland-Altman analyses. For both experts and trainees, visually estimated sizes of the same polyp were significantly inconsistent depending on the camera view used (P < 0.001). Bland-Altman analyses showed that there was a trend toward underestimation of the sizes of the polyps in both groups, especially for polyps larger than 10 mm. The new technique was highly accurate and reliable in measuring the size of colon polyp (CCC, 0.961; confidence interval 0.926-0.979), clearly outperforming the visual estimation and open biopsy forceps methods. The new AI measurement method improved the accuracy and reliability of polyp size measurements in colonoscopy images. Incorporating AI might be particularly important to improve the efficiency of trainees at estimating polyp size during colonoscopy.

A Surgeon’s Habits-Based Novel Master Manipulator for the Vascular Interventional Surgical Master-Slave Robotic System

A master-slave vascular interventional surgical robotic system (MSVISRs) promises to treat cardio-cerebrovascular diseases without damaging the health of surgeons. Although many scholars are engaged in the research on the slave manipulator for the MSVISRs, there are very few studies on the master manipulator. In this study, a surgeon&#x2019;s habits-based novel master manipulator is developed. This master manipulator imitates the operating habits of surgeons in surgery and uses a non-contact method with an optical mouse sensor to measure the operating displacements. The novelty of this approach is that this master manipulator with smaller size and non-contact displacement measurement method can provide the axial force and the circumferential force feedback independently. In this master manipulator, the rigid-flexible coupling structure is used to provide the axial force feedback. In addition, the brush DC motor is used to give the circumferential force feedback. Furthermore, the proposed master manipulator is calibrated and evaluated by experiments. The calibration experiments show that the maximum identification ranges for the axial force and circumferential force in the proposed master manipulator are 0.247-4.5N and 0.52-4.3N, respectively. Moreover, the evaluation experiments show that the maximum error is 0.8 mm in the axial direction. As compared with other master manipulator, the comprehensive properties of this novel master manipulator are shown to be better and more promising.

Metro Wheel Set Size Measurement Method Based on CDD Image Recognition

In the process of train operation, the contact friction between wheel and track will change the size of wheel set. In order to ensure the safe operation of train, it is of great significance to detect the change of wheel set size timely and accurately. On the basis of summarizing the research at home and abroad, the online dynamic detection technology of wheel set size is studied. Firstly, the overall architecture of the wheel set size online detection system is studied, the working principle of each detection module is explained, and the installation parameters of the laser displacement sensor are designed. Secondly, the machine learning algorithm of online dynamic detection technology of wheel set size is studied. The passing wheel sets are photographed by CCD camera, and then the three-dimensional model is reconstructed by point cloud data, and then the parameters of each part are measured and calculated according to the model. The wheel set size can be obtained.

Design of a multi-sensor information acquisition system for mannequin reconstruction and human body size measurement under clothes

Although the measurement of human sizes and the reconstruction of mannequins have been extensively studied, there has been little research on systems applicable for measurement and reconstruction under clothes. This work presents a method for three-dimensional (3D) mannequin reconstruction and human body size measurement based on information fusion of multiple sensors. For this purpose, a multi-sensor information acquisition system (MIAS) is developed allowing one to acquire 11 key size data of the human body under clothes. The MIAS is based on the proposed parametric mannequin method and the Laplace mesh deformation technique, thus allowing one to reconstruct the mannequin conforming to the MIAS measurements. All mannequins reconstructed by this method have the same topology, which will greatly facilitate subsequent processing, such as garment design, customization and virtual fitting. The results of the reconstruction experiments show that our system can reconstruct a mannequin that matches the size of the feature parts very well. In addition, performed comparison experiments with manual and laser measurements shows that the measurement results of our system are close to those of manual and laser measurements. This provides a viable method for measuring key body sizes under clothes and reconstructing the corresponding 3D mannequin.

Study on Body Size Measurement Method of Goat and Cattle under Different Background Based on Deep Learning

The feasibility of using depth sensors to measure the body size of livestock has been extensively tested. Most existing methods are only capable of measuring the body size of specific livestock in a specific background. In this study, we proposed a unique method of livestock body size measurement using deep learning. By training the data of cattle and goat with same feature points, different animal sizes can be measured under different backgrounds. First, a novel penalty function and an autoregressive model were introduced to reconstruct the depth image with super-resolution, and the effect of distance and illumination on the depth image was reduced. Second, under the U-Net neural network, the characteristics exhibited by the attention module and the DropBlock were adopted to improve the robustness of the background and trunk segmentation. Lastly, this study initially exploited the idea of human joint point location to accurately locate the livestock body feature points, and the livestock was accurately measured. According to the results, the average accuracy of this method was 93.59%. The correct key points for detecting the points of withers, shoulder points, shallowest part of the chest, highest point of the hip bones and ischia tuberosity had the percentages of 96.7%, 89.3%, 95.6%, 90.5% and 94.5%, respectively. In addition, the mean relative errors of withers height, hip height, body length and chest depth were only 1.86%, 2.07%, 2.42% and 2.72%, respectively.

Comparison of four methods to -determine cage width in tibial tuberosity advancement.

The objective of this study was to test a method for determining the width of the tibial tuberosity advancement (TTA) cage without the need for extension of the stifle joint, while producing preoperative radiographs. TTA cage size was determined by applying 4 different methods using radiograph images in mediolateral direction of fully extended stifles of dogs (n=43), with and without naturally occurring cranial cruciate ligament disease: parallel shift (PS) method, common tangent (CT) method, «2,1» method (cage size = 2,1 × tibia plateau length - tibial tuberosity width), and margo cranialis (MC) method (cage size = length of MC/6*1,75). Two new methods, «2,1», and MC were compared to the existing CT and PS methods. All 4 methods resulted in reliable cage sizes. Intraclass correlation coefficients showed an excellent reliability of the CT and PS to the «2,1» method and a good reliability in regards to the MC method. In conclusion, TTA cage size of the tibia anatomy alone can be determined on radiographs without the need of full extension of the stifle joint. Based on the results, two different methods of cage size measurement are recommended in order to increase diagnostic accuracy and to prevent the insertion of suboptimal cages.

Understanding the key factors determining rural domestic waste treatment behavior in China: a meta-analysis.

The growing interest of policy-makers, environmental stakeholders, and academics in rural domestic waste treatment has made it a recent focus of research in the field of rural human settlements. Many studies have been conducted to understand the related factors; however, their results are inconsistent. Therefore, different from previous studies, the study systematically analyzed and summarized empirical studies on rural domestic waste treatment and explored the key factors that fostered and impeded it. It also examined the reasons for differences in research level and trends in key factors over time. A random-effects meta-analysis of 24 studies revealed that education, political status, perceived value, behavioral attitude, subjective norm, garbage collection facilities and services, environmental knowledge and propaganda, and government policy and regulation had a significant positive correlation with rural domestic waste treatment; perceived value had the highest impact, followed by behavioral attitude. All of the variables had significant heterogeneity. Some of the heterogeneity can be explained by differences in research methods, sample size, and variable measurement methods. An increasing trend was observed in the effects of perceived value, behavioral attitude, and subjective norm. Finally, based on these results, the study provided suggestions on policy and academic aspects.

An Automatic Detection and Online Quality Inspection Method for Workpiece Surface Cracks based on Machine Vision

The wide application of intelligent manufacturing technologies imposes higher requirements for the quality inspection of industrial products; however, the existing industrial product quality inspection methods generally have a few shortcomings such as requiring many inspectors, too complicated methods, difficulty in realizing standardized monitoring, and the low inspection efficiency, etc. Targeting at these problems, this paper proposed an automatic detection and online quality inspection method for workpiece surface cracks based on the machine vision technology. At first, it proposed a vision-field environment calibration method, gave the specific method for workpiece shape feature recognition and size measurement based on machine vision, and achieved the on-line monitoring of workpiece quality problems such as feature defects and size deviations. Then, this study integrated the multi-scale attention module and the up-sampling module that can restore the locations of image pixels based on the high-level and low-level hybrid feature maps, built a workpiece crack extraction network, and realized workpiece crack feature extraction, crack type classification, and damage degree division. At last, experimental results verified the effectiveness of the proposed method, and this paper provided a reference for the application of machine vision technology in other fields.

A Novel Scratch Detection and Measurement Method for Automotive Stamping Parts

In this paper, a novel scratch detection and measurement method for automotive stamping parts is proposed to detect and measure scratches. This method uses the geometric characteristics of the point cloud for the detection and measurement of scratches and does not require registration with a standard point cloud model. In this method, a scratch localization algorithm called bilateral weight integration is proposed. Then a scratch complement algorithm called NRPCA-RG is proposed to ensure that the complete scratch is obtained. Finally, we propose an improved PCA-based scratch size measurement method called P-NRPCA to measure the length, width and depth of scratches. We conducted an experimental study on two stamping parts with scratches and compared with other methods. The results prove the practicality and effectiveness of the method.