Size Measurement Method Research Articles

Comparison of magnification corrected optic disc size by slit-lamp biomicroscopy, fundus photography, and optical coherence tomography

Abstract PURPOSE: Optic disc size measurement is essential for determining landmarks, lesions, and distances of the retina. Due to the size variability in nature and when measured with different instruments, newer methods to compare sizes between different instruments are needed. We aimed to determine the comparability of slit lamp, fundus photography, and optical coherence tomography (OCT) measurements of optic disc size after correcting for both ocular and instrument magnification. MATERIALS AND METHODS: In this prospective, observational study, optic disc size was measured by slit-lamp biomicroscopy, fundus photography, and OCT. Instrument and ocular magnification was calculated, and the measured size was corrected for both using the Littman formula. The corrected values were analyzed using Bland–Altman plots and intra-class correlation (ICC) for agreeability and correlation, respectively. Linear regression analysis was conducted to estimate the systematic errors and interconvertibility in cases of high correlation. RESULTS: Fifty-six eyes were included for the analysis. Uncorrected and corrected values for each method had excellent correlation and agreeability (ICC absolute agreement >0.75, P < 0.01). Among magnification corrected values, slit lamp and OCT had excellent correlation and agreement (ICC consistency = 0.846, confidence interval 0.75–0.91, P < 0.01). This pair had R 2 = 0.73 on linear regression (P < 0.01). Linear regression formulas for interconverting uncorrected and corrected values for the three methods had variable accuracy (R 2 from 0.47 to 0.66). CONCLUSION: Uncorrected values of different methods for disc size measurement cannot be compared directly; they need ocular and instrument magnification correction. Among the three methods compared, the best interconvertible pairs of measurement were magnification corrected slit lamp and OCT values.

The bubble size produced in a pilot HydroFloat® cell and its effects on flotation

Unlike a traditional mechanical flotation cell, Eriez’s HydroFloat® is a fluidized-bed flotation device that can recover much coarser and less liberated particles. This is a new flotation machine where the effects of parameters such as bubble size are yet to be established. To address this problem, JKMRC has built a pilot HydroFloat® rig which it deployed to a copper–gold plant in New South Wales, Australia. Tests were performed at a range of operating conditions and bubble size images were collected and analysed using a novel newly created bubble size measurement method. In conventional flotation, a finer bubble size increases flotation rates because it increases the probability of bubble-particle collision and attachment. In this pilot HydroFloat® test program, however, flotation performance was optimal at an intermediate bubble size. Using the comprehensive test program data, the operating parameters that significantly affect bubble size were established, and an empirical model to predict bubble size was developed.

Detection of moisture and size of winter melon seeds based on hyperspectral imaging and convex polygon size measurement

The level of moisture content and size of winter melon seeds affect their storage, germination and growth processes. Moderate moisture and size are essential for seed germination and growth. Therefore, detecting the moisture and size of winter melon seeds is beneficial to improve their germination rate. Traditional seed moisture testing methods are complex to operate and require destructive sample preparation or chemical treatment. Common dimensional measurement methods are also time-consuming and laborious. Hyperspectral imaging technology can acquire information about the surface and internal structure of the target separately, it can be used to quickly and non-destructively detect the moisture and size of winter melon seeds. In this study, partial least squares regression (PLSR) and partial least squares support vector machine (LSSVM) models were established to predict the moisture content of winter melon seeds by using reflection and transmittance spectral data. The models were optimized using five variable selection methods. The optimal performance of the LSSVM model based on the CARS algorithm was achieved in both single reflection and transmission spectra. The RP2 and RMSEP of the model based on the reflection spectra were 0.9667 % and 0.0215 %, respectively. The RP2 and RMSEP of the model based on the transmission spectra were 0.9644 % and 0.0223 %, respectively. In low-level data fusion, the LSSVM model based on the CARS algorithm also achieved optimal performance, but with only a little improvement compared to a single model (reflection spectra or transmission spectra), with RP2 and RMSEP of 0.9679 % and 0.0212 %, respectively. In the mid-level data fusion, the LSSVM model also based on the CARS algorithm achieved the optimal performance, and the performance of the model was further improved. The RP2 and RMSEP of the model were 0.9738 % and 0.0191 %, respectively. Finally, the image processing algorithm and the convex polygon size measurement method was proposed to measure the size of winter melon seeds. The absolute error between the calibrated winter melon seed length and width and the true length and width was less than 0.22 mm, and the relative error was less than 2 %. The results show that hyperspectral imaging technology can accurately detect the water content of winter melon seeds. Data fusion method and LSSVM model based on CARS algorithm can detect the water content of winter melon seeds more accurately. The image processing algorithm combined with the convex polygon size measurement method can be effectively used to detect the size of winter melon seeds with high precision.

Cattle Body Size Measurement Based on DUOS-PointNet+.

The common non-contact, automatic body size measurement methods based on the whole livestock point cloud are complex and prone to errors. Therefore, a cattle body measuring system is proposed. The system includes a new algorithm called dynamic unbalanced octree grouping (DUOS), based on PointNet++, and an efficient method of body size measurement based on segmentation results. This system is suitable for livestock body feature sampling. The network divides the cow into seven parts, including the body and legs. Moreover, the key points of body size are located in the different parts. It combines density measurement, point cloud slicing, contour extraction, point cloud repair, etc. A total of 137 items of cattle data are collected. Compared with some of the other models, the DUOS algorithm improves the accuracy of the segmentation task and mean intersection by 0.53% and 1.21%, respectively. Moreover, compared with the manual measurement results, the relative errors of the experimental measurement results are as follows: withers height, 1.18%; hip height, 1.34%; body length, 2.52%; thoracic circumference, 2.12%; abdominal circumference, 2.26%; and cannon circumference, 2.78%. In summary, the model is proven to have a good segmentation effect on cattle bodies and is suitable for cattle body size measurement.

Research on Size Measurement Method of Underwater Parts Based on Machine Vision

Abstract In order to measure the deformed parts under pressure, a size measurement method of underwater parts based on machine vision is proposed, whereas the relative position between the camera and target parts remains basically unchanged. Firstly, the imaging theory of the underwater camera is analyzed, and the mathematical relationship between the size of the target object and the number of pixels is established; Then, the underwater parts measurement system based on machine vision is built, and the image processing algorithm is studied to extract the target size; Thirdly, the genetic algorithm is studied to calibrate the camera measurement system; Finally, the relevant experiment is conducted on the underwater parts measurement system. Through the analysis of the results, the measurement uncertainty of the system is 0.0355mm, and the relative error is 0.1%. The underwater parts measurement system has practical value.

Point Cloud Measurement of Rubber Tread Dimension Based on RGB-Depth Camera

To achieve an accurate measurement of tread size after fixed-length cutting, this paper proposes a point-cloud-based tread size measurement method. Firstly, a mathematical model of corner points and a reprojection error is established, and the optimal solution of the number of corner points is determined by the non-dominated sorting genetic algorithm II (NSGA-II), which reduces the reprojection error of the RGB-D camera. Secondly, to address the problem of the low accuracy of the traditional pixel metric ratio measurement method, the random sampling consensus point cloud segmentation algorithm (RANSAC) and the oriented bounding box (OBB) collision detection algorithm are introduced to complete the accurate detection of the tread size. By comparing the absolute error and relative error data of several groups of experiments, the accuracy of the detection method in this paper reaches 1 mm, and the measurement deviation is between 0.14% and 2.67%, which is in line with the highest accuracy standard of the national standard. In summary, the RGB-D visual inspection method constructed in this paper has the characteristics of low cost and high inspection accuracy, which is a potential solution to enhance the pickup guidance of tread size measurement.

Measurement method of spherical radius size of differential planetary gear tooth surface based on 3D vision

Abstract In order to accurately measure the spherical radius dimensions of planetary gear tooth surfaces, we propose a non-contact measurement method based on 3D vision technology. Firstly, the point cloud data of the planetary gear captured by the laser 3D profiler is preprocessed, and the tooth surface point cloud is extracted. Next, based on the structural characteristics of the planetary gear tooth surface, a uniform slicing and sampling algorithm is used to extract circular contour points where the tooth surface point cloud intersects with the slicing planes. These points are then projected onto a two-dimensional plane, where an improved RANSAC algorithm is used to precisely fit each circular cross-section. Contour points associated with significant fitting errors are removed based on a set tolerance value. Finally, an improved differential evolution algorithm is used to perform a three-dimensional spherical fitting on the contour points that meet the tolerance criteria, achieving accurate measurement of the spherical radius of the planetary gear tooth surfaces. Experimental results demonstrate that the repeatability error of the proposed method is ±0.02 mm, with a maximum absolute error of 0.026 mm and an average measurement time of 4.76 s. The method exhibits high robustness and measurement accuracy, meeting practical engineering measurement requirements.

Evaluation of Two Digital Wound Area Measurement Methods Using a Non-Randomized, Single-Center, Controlled Clinical Trial

A prospective, single-center, non-randomized, pre-marketing clinical investigation was conducted with a single group of subjects to collect skin lesion images. These images were subsequently utilized to compare the results obtained from a traditional method of wound size measurement with two novel methods developed using Machine Learning (ML) approaches. Both proposed methods automatically calculate the wound area from an image. One method employs a two-dimensional system with the assistance of an external calibrator, while the other utilizes an Augmented Reality (AR) system, eliminating the need for a physical calibration object. To validate the correlation between these methods, a gold standard measurement with digital planimetry was employed. A total of 67 wound images were obtained from 41 patients between 22 November 2022 and 10 February 2023. The conducted pre-marketing clinical investigation demonstrated that the ML algorithms are safe for both the intended user and the intended target population. They exhibit a high correlation with the gold standard method and are more accurate than traditional methods. Additionally, they meet the manufacturer’s expected use. The study validated the performance, safety, and usability of the implemented methods as a valuable tool in the measurement of skin lesions.

A dataset describing chip parameters for rock breaking by chisel pick under deep-sea hydrostatic pressure

Chip is a visual representation of rock breaking by cutter, and their related parameters are crucial for revealing the rock breaking mechanism in deep-sea mining. Based on sieving and three-dimensional size measurement methods widely used in mining engineering, this paper reports a dataset of chip parameters for rock breaking by chisel pick under deep-sea hydrostatic pressure. Specifically, we first designed an experimental setup that can accurately simulate deep-sea hydrostatic pressure, conducted rock breaking experiments and carefully collected chips. Subsequently, those chips were sieved, high-resolution images were collected, and the coarseness index (CI), chip size uniformity (n), absolute chip size (de), and fractal dimension (D) were measured. Finally, three-dimensional size (long, intermediate and short) was measured for 3064 chips with particle sizes greater than 4.75 mm. This dataset will be used by researchers to validate numerical simulations or optimize equipment structures related to deep-sea mining, including deep-sea rock mechanics, mining cutter and conveyor pipes.

Transthoracic echocardiography is a simple tool for size matching in cardiac xenotransplantation.

Preoperative size matching is essential for both allogeneic and xenogeneic heart transplantation. In preclinical pig-to-baboon xenotransplantation experiments, porcine donor organs are usually matched to recipients by using indirect parameters, such as age and total body weight. For clinical use of xenotransplantation, a more precise method of size measurement would be desirable to guarantee a "perfect match." Here, we investigated the use of transthoracic echocardiography (TTE) and described a new method to estimate organ size prior to xenotransplantation. Hearts from n = 17 genetically modified piglets were analyzed by TTE and total heart weight (THW) was measured prior to xenotransplantation into baboons between March 2018 and April 2022. Left ventricular (LV) mass was calculated according to the previously published method by Devereux etal. and a newly adapted formula. Hearts from n = 5 sibling piglets served as controls for the determination of relative LV and right ventricular (RV) mass. After explantation, THW and LV and RV mass were measured. THW correlated significantly with donor age and total body weight. The strongest correlation was found between THW and LV mass calculated by TTE. Compared to necropsy data of the control piglets, the Devereux formula underestimated both absolute and relative LV mass, whereas the adapted formula yielded better results. Combining the adapted formula and the relative LV mass data, THW can be predicted with TTE. We demonstrate reliable LV mass estimation by TTE for size matching prior to xenotransplantation. An adapted formula provides more accurate results of LV mass estimation than the generally used Devereux formula in the xenotransplantation setting. TTE measurement of LV mass is superior for the prediction of porcine heart sizes compared to conventional parameters such as age and total body weight.

Measurement of body size parameters and body weight prediction in beef cattle based on image analysis

The body size parameter of cattle is an important index reflecting the growth and development and health condition of cattle. The traditional manual contact measurement is not only a large workload and difficult to measure, but also prone to problems such as affecting the normal life habits of cattle. In this paper, we address this problem by proposing a contactless body size measurement method for cattle based on machine vision. Firstly, the cattle is confined to a fixed space using a position-limiting device, and images of the body of the cattle are taken from three directions: top, left, and right, using multiple cameras. Secondly, the image is segmented using a fuzzy clustering algorithm based on neighborhood adaptive local spatial information improvement, and the image is processed to extract the contour images of the top view and side view. The key points of body measurements were extracted using interval division and curvature calculation for the side view images, and the key point information was extracted using skeleton extraction and pruning for the top view images, which realized the measurements of body height(BH), rump height(RH), body slanting length(BSL), and abdominal circumference(AC) parameters of the cattle. The correlation between body size and weight data obtained by contactless methods was investigated and the modeled using one-factor linear regression, one-factor nonlinear regression, multivariate stepwise regression, RBF network fitting, BP neural network fitting, support vector machine, and particle swarm optimization-based support vector machine methods, respectively. Information on body size parameters was collected from 137 cattles, and the results showed that the maximum errors between the measured and actual values of BH, RH, BSL and AC were 5.0%, 4.4%, 3.6%, and 5.5%, respectively. Correlation of BH, RH, BSL and AC with weight obtained by non-contact methods was > 0.75. The BH parameter can be selected in the single-factor growth monitoring. The multi-body scale can reflect the growth status of cattle more comprehensively, in which RH, BSL and AC are important detection parameter; the multi-factor nonlinear model can reflect the growth characteristics of cattle more comprehensively. The contactless measurement method proposed in the paper can effectively improve the work efficiency and reduce the stress reaction of cattle, which is a long-term and effective monitoring method, and is of great significance in promoting accurate and welfare cattle rearing.

Research on Online Measurement Method for Small Workpiece Circular Holes Based on Machine Vision

<p>In modern manufacturing, due to the narrow format, short size, and complex surface structure of small-sized workpieces, automatic detection of surface defects, dimensions, etc. on small-sized workpieces faces difficulties, making it difficult to meet the requirements of fully automated production lines. Especially, there is no good solution to the problem of high-precision automatic measurement of geometric features of circles and arcs on small-sized workpieces. This article takes the engine end cover circular hole thermal component as the research object, builds a hardware platform for image acquisition of small-sized workpieces based on machine vision, proposes an online size measurement method for small-sized circular holes, and realizes the measurement of the position size and aperture size of small-sized product shell workpieces. In order to achieve the above methods, this article builds a visual detection hardware system and describes a detailed machine vision hardware selection scheme. Then, the images collected by the system are preprocessed, including image denoising, image correction, and image feature extraction. Then, for the processed images, an improved Hough transform fitting method is used to obtain the online size of features. In order to visualize the results, the detection system and software are designed according to the actual application scenario. The overall scheme of this article is efficient and reasonable, and the detection result error is within a reasonable range.</p> <p> </p>

Research on online measurement method of image target size based on binocular vision

Research on online measurement method of image target size based on binocular vision

Automated zooplankton size measurement using deep learning: Overcoming the limitations of traditional methods

Zooplankton size is a crucial indicator in marine ecosystems, reflecting demographic structure, species diversity and trophic status. Traditional methods for measuring zooplankton size, which involve direct sampling and microscopic analysis, are laborious and time-consuming. In situ imaging systems are useful sampling tools; however, the variation in angles, orientations, and image qualities presented considerable challenges to early machine learning models tasked with measuring sizes.. Our study introduces a novel, efficient, and precise deep learning-based method for zooplankton size measurement. This method employs a deep residual network with an adaptation: replacing the fully connected layer with a convolutional layer. This modification allows for the generation of an accurate predictive heat map for size determination. We validated this automated approach against manual sizing using ImageJ, employing in-situ images from the PlanktonScope. The focus was on three zooplankton groups: copepods, appendicularians, and shrimps. An analysis was conducted on 200 individuals from each of the three groups. Our automated method's performance was closely aligned with the manual process, demonstrating a minimal average discrepancy of just 1.84%. This significant advancement presents a rapid and reliable tool for zooplankton size measurement. By enhancing the capacity for immediate and informed ecosystem-based management decisions, our deep learning-based method addresses previous challenges and opens new avenues for research and monitoring in zooplankton.

Exploration of tumor size measurement methods in preoperative breast cancer assessment using whole-body silicon photomultiplier PET: feasibility and first results

PurposeWhole-body silicon photomultiplier positron emission tomography (WB SiPM PET) could be used to diagnose breast cancer spread before lumpectomy. We aimed to investigate the method of measuring the tumor size by WB SiPM PET as a basis for diagnosing breast cancer spread in the breast.Materials and methodsWe retrospectively reviewed 35 breast cancer lesions in 32 patients who underwent WB SiPM PET/CT in the prone position as preoperative breast cancer examinations from September 2020 to March 2022.In all cases, a 20-mm spherical VOI was placed in the normal mammary gland to measure the mean standardised uptake value (SUVmean) and the standard deviation (SD) of 18F-fluorodeoxyglucose (FDG) uptake. We prepared four types of candidates (SUVmean + 2 SD, SUVmean + 3 SD, 1.5 SUVmean + 2 SD, 1.5 SUVmean + 3 SD) for thresholds for delineating tumor contours on PET images. On the semiautomatic viewer soft, the maximum tumor sizes were measured at each of the four thresholds and compared with the pathological tumor sizes, including the extensive intraductal component (EIC).ResultsThe lesion detection sensitivity was 97% for WB SiPM PET. PET detected 34 lesions, excluding 4-mm ductal carcinomas in situ (DCIS). PET measurements at the '1.5 SUVmean + 2 SD' threshold demonstrated values closest to the pathological tumor sizes, including EIC. Moreover, '1.5 SUVmean + 2 SD' had the highest concordance (63%).ConclusionsThe study demonstrated that among various PET thresholds, the '1.5 SUVmean + 2 SD' threshold exhibited the best performance. However, even with this threshold, the concordance rate was limited to only 63%.

Retracted: Metro Wheel Set Size Measurement Method Based on CDD Image Recognition

Retracted: Metro Wheel Set Size Measurement Method Based on CDD Image Recognition

Study on laser spot size measurement by scanning-slit method based on back-injection interferometry

Spot size is an important parameter of the laser, which not only represents the resolution of laser, but is also involved in the calculation of other parameters. Nowadays, CCD imaging systems, scanning imaging systems, and other sensors are used to measure the laser spot size. But they are all lacking flexibility when measuring the spot size in different locations, not to mention their high cost. In this study, a new spot size measurement device based on laser back-injection interferometry was presented. The photodiode integrated with the laser diode was used to collect the feedback laser, then the laser spot size was calculated by the feedback current. A commercial CCD imaging system was used to provide the laser spot size as a reference. Results show that our spot size measurement device could measure the spot size (Full Width Half Maximum) of 5 laser diode modules both in the x (Gaussian-like profile) and y (top-hat-like profile) direction through scanning-slit. Though there are variations between the scanning-slit results and spot sizes from the CCD imaging system due to the diffuse and specular reflection, the accuracy of the spot size measurement device ranges from 96.07 % to 99.46 %, which proves the reliability of our device. It is believed that our device could provide an alternate method for laser spot size measurement, which is cost-effective, easy to operate, and accurate.

Measurement of Submicron Particle Size Using Scattering Angle-Corrected Polarization Difference with High Angular Resolution

The particle size of submicron particles significantly affects their properties; thus, the accurate measurement of submicron particle size is essential to ensure its excellent properties. Polarized light scattering is an important tool for measuring the particle size of the ensemble of particles in suspension. However, in the existing measurement systems, the polarized scattered light is detected using a CCD detector or an array of single-point detectors. The CCD detector misses a large part of the polarized scattered light due to its narrow detection range of scattering angles, and the array of single-point detectors has the problem of low angular resolution due to the limited number of detectors. According to the above problems, this paper designs a submicron particle size measurement method based on the polarization difference in polarized scattered light with high angular resolution. The vertically and horizontally polarized scattered light was acquired with high angular resolution (angular separation = 2°) over a scattering angle range of 50°–110° using a photomultiplier coupled with a turntable. The scattering angle of the acquired vertically and horizontally polarized scattered light were corrected to eliminate the scattering angle deviations caused by obliquely incident light, and then the polarization difference in the vertically and horizontally polarized scattered light was computed, from which the submicron particle size distribution was inverted subsequently. Experiments were performed using polystyrene microsphere standard particles with particle sizes of 350 nm, 200 nm, and 100 nm. The experimental results show that (1) the Pearson correlation coefficient of the linearly fitted curve of the corrected polarization difference to the theoretical polarization difference is larger than 0.997, and the slope and intercept of the linearly fitted curve are, respectively, close to 1 and 0, indicating that the corrected polarization difference is highly consistent with the theoretical polarization difference; (2) the mean relative error and coefficient of variation of the particle size distribution parameter D50 obtained from the polarization difference with high angular resolution (angular separation = 2°) are better than those of the parameter D50 obtained from the polarization difference with low angular resolution (angular separation = 12°), indicating better accuracy and repeatability of the particle size distribution inverted from the polarization difference with high angular resolution; and (3) for the particle size distribution parameters D10, D50, and D90 obtained from the scattering angle-corrected polarization difference with high angular resolution, the deviation of the measured values from the average value are all smaller than the thresholds given in the international standard, indicating a good repeatability of the proposed method.

An intelligent vocabulary size measurement method for second language learner

This paper presents a new method for accurately measuring the vocabulary size of second language (L2) learners. Traditional vocabulary size tests (VSTs) are limited in capturing a tester’s vocabulary and are often population-specific. To overcome these issues, we propose an intelligent vocabulary size measurement method that utilizes massive robot testers. They are equipped with randomized and word-frequency-based vocabularies to simulate L2 learners’ variant vocabularies. An intelligent vocabulary size test (IVST) is developed to precisely measure vocabulary size for any population. The robot testers “take” the IVST, which dynamically generates quizzes with varying levels of difficulty adapted to the estimated tester’s vocabulary size in real-time using an artificial neural network (ANN) through iterative learning. The effectiveness of the IVST is factually verified by their visible vocabularies. Additionally, we apply a long short-term memory (LSTM) model to further enhance the method’s performance. The proposed method has demonstrated high reliability and effectiveness, achieving accuracies of 98.47% for the IVST and 99.87% for the IVST with LSTM. This novel approach provides a more precise and reliable method for measuring vocabulary size in L2 learners compared to traditional VSTs, offering potential benefits to language learners and educators.

A novel express method for determining WC grain sizes and its use for updating dependencies of coercivity and hardness on WC mean grain size in hardmetals

A new express method for measurements of WC grain sizes in WC-Co hardmetals was developed. This method is as precise as the EBSD grain size measurement method but much faster and thereby less expensive. The new measurement method is based on a peculiar procedure of hardmetal samples' preparation, recording microstructure images and their segmentation followed by determining WC mean grain sizes by use of a specially developed software; the measurement error is less than ±3%. Dependencies of coercivity and hardness on the WC mean grain size of WC-10 wt% Co hardmetal samples were established by the new express measurement method. With the aid of this method the dependencies reported in literature were updated as a result of more precisely determined values of the WC mean grain size compared to those established in literature by conventional methods based on SEM measurements.