Automatic Measurement Method Research Articles

Automated Assessment of Cell Infiltration and Removal in Decellularized Scaffolds - Experimental Study in Rabbits.

Objective Semiquantitative and automated measurement of nuclear material removal and cell infiltration in decellularized tendon scaffolds (DTSs). Method 16 pure New Zealand rabbits were used, and the gastrocnemius muscle tendon was collected bilaterally from half of these animals (16 tendons collected); 4 were kept as control and 12 were submitted to the decellularization protocol (DTS). Eight of the DTSs were used as an in vivo implant in the experimental rotator cuff tear (RCT) model, and the rest, as well as the controls, were used in the semiquantitative and automated evaluation of nuclear material removal. The eight additional rabbits were used to make the experimental model of RCT and subsequent evaluation of cellular infiltration after 2 or 8 weeks, within the DTS. Results The semiquantitative and automated analysis used demonstrated a removal of 79% of nuclear material ( p < 0.001 and power > 99%) and a decrease of 88% (p < 0.001 and power >99%) in the area occupied by nuclear material after the decellularization protocol. On cell infiltration in DTS, an increase of 256% (p < 0.001 and power >99%) in the number of cells within the DTS was observed in the comparison between 2 and 8 weeks postoperatively. Conclusion The proposed semiquantitative and automated measurement method was able to objectively measure the removal of nuclear material and cell infiltration in DTS.

Artificial intelligence-based automatic assessment of lower limb torsion on MRI

Abnormal torsion of the lower limbs may adversely affect joint health. This study developed and validated a deep learning-based method for automatic measurement of femoral and tibial torsion on MRI. Axial T2-weighted sequences acquired of the hips, knees, and ankles of 93 patients (mean age, 13 ± 5 years; 52 males) were included and allocated to training (n = 60), validation (n = 9), and test sets (n = 24). A U-net convolutional neural network was trained to segment both femur and tibia, identify osseous anatomic landmarks, define pertinent reference lines, and quantify femoral and tibial torsion. Manual measurements by two radiologists provided the reference standard. Inter-reader comparisons were performed using repeated-measures ANOVA, Pearson’s r, and the intraclass correlation coefficient (ICC). Mean Sørensen-Dice coefficients for segmentation accuracy ranged between 0.89 and 0.93 and erroneous segmentations were scarce. Ranges of torsion as measured by both readers and the algorithm on the same axial image were 15.8°–18.0° (femur) and 33.9°–35.2° (tibia). Correlation coefficients (ranges, .968 ≤ r ≤ .984 [femur]; .867 ≤ r ≤ .904 [tibia]) and ICCs (ranges, .963 ≤ ICC ≤ .974 [femur]; .867 ≤ ICC ≤ .894 [tibia]) indicated excellent inter-reader agreement. Algorithm-based analysis was faster than manual analysis (7 vs 207 vs 230 s, p < .001). In conclusion, fully automatic measurement of torsional alignment is accurate, reliable, and sufficiently fast for clinical workflows.

A study of automatic measurement method of RPG (Re-tensile Plastic zone’s Generated) Load

ABSTRACT Identification of the relationship between fatigue crack propagation rate (da/dN) and stress intensity factor range (∆K) is inevitable to apply the fracture mechanics approach to assess the growth of fatigue crack growth. The relationship between da/dN and ∆K is widely applied to evaluate fatigue crack propagation behaviour. To evaluate the fatigue crack growth history under variable loading history, it is necessary to replace ∆K to the effective stress intensity factor range, which can quantitatively consider fatigue crack opening and closing behaviour. ∆K eff proposed by Elber is well known as the effective stress intensity factor range, but even if ∆K eff is applied to evaluate the fatigue crack propagation behaviour, a threshold value ((∆K eff)th) was occurred. On the other hand, it is known that fatigue cracks propagate even at ∆K eff below (∆K eff)th under variable loading history. This implies that ∆K eff is an insufficient parameter to describe fatigue crack propagation behaviour. ∆K RPG, which has a close relationship with the cyclic plastic behaviour in the vicinity of the crack tip proposed by Toyosada and Niwa, can overcome the shortcomings of ∆K eff even under complicated variable loading history including multiple frequency components. To apply the fatigue crack propagation law with ∆K RPG as a parameter, it is necessary to experimentally measure the RPG load and identify the propagation law constants (∆K RPG = C(∆K RPG) m ) C and m. A conventional method for identifying RPG loads requires the superposition of the hysteresis loop near the crack tip and its reversal loop as measured by the unloading elastic compliance method. However, advanced skills in this method, such as understanding the characteristics of measurement errors associated with loops, is required. In this study, we have proposed an automatic method for measuring the RPG load that is equivalent to manual measurement results by an expert and we have validated the automatic measurement method by comparing the results of automatic measurement using our proposed method with the past conventional measurement results for several materials and loading conditions.

Semi-automatic ultrasound curve angle measurement for adolescent idiopathic scoliosis.

Using X-ray to evaluate adolescent idiopathic scoliosis (AIS) conditions is the clinical gold standard, with potential radiation hazards. 3D ultrasound has demonstrated its validity and reliability of estimating X-ray Cobb angle (XCA) using spinous process angle (SPA), which can be automatically measured. While angle measurement with ultrasound using spine transverse process-related landmarks (UCA) shows better agreed with XCA, its automatic measurement is challenging and not available yet. This research aimed to analyze and measure scoliotic angles through a novel semi-automatic UCA method. 100 AIS subjects (age: 15.0 ± 1.9years, gender: 19M and 81 F, Cobb: 25.5 ± 9.6°) underwent both 3D ultrasound and X-ray scanning on the same day. Scoliotic angles with XCA and UCA methods were measured manually; and transverse process-related features were identified/drawn for the semi-automatic UCA method. The semi-automatic method measured the spinal curvature with pairs of thoracic transverse processes and lumbar lumps in respective regions. The new semi-automatic UCA method showed excellent correlations with manual XCA (R2 = 0.815: thoracic angles R2 = 0.857, lumbar angles R2 = 0.787); and excellent correlations with manual UCA (R2 = 0.866: thoracic angles R2 = 0.921, lumbar angles R2 = 0.780). The Bland-Altman plot also showed a good agreement against manual UCA/XCA. The MADs of semi-automatic UCA against XCA were less than 5°, which is clinically insignificant. The semi-automatic UCA method had demonstrated the possibilities of estimating manual XCA and UCA. Further advancement in image processing to detect the vertebral landmarks in ultrasound images could help building a fully automated measurement method. Level III.

Automatic measurement of axial vertebral rotation in 3D vertebral models

Axial Vertebral Rotation (AVR) is a significant indicator of adolescent idiopathic scoliosis (AIS). A host of methods are provided to measure AVR on coronal plane radiographs or 3D vertebral model. This paper provides a method of automatic AVR measurement in 3D vertebral model that is based on point cloud segmentation neural network and the tip of the spinous process searching algorithm. An improved PointNet using multi-input and attention mechanism named Multi-Input PointNet is proposed, which can segment the upper and lower endplates of the vertebral model accurately to determine the transverse plane of vertebral model. An algorithm is developed to search the tip of the spinous process according to the special structure of vertebrae. AVR angle is measured automatically using the midline of vertebral model and projection of y-axis on the transverse plane of vertebral model based on points obtained above. We compare automatic measurement results with manual measurement results on different vertebral models. The experiment shows that automatic results can achieve accuracy of manual measurement results and the correlation coefficient of them is 0.986, proving our automatic AVR measurement method performs well.

Correlation between automatic and manual methods of right ventricle and left atrium strain quantification

Abstract Introduction Myocardial strain quantification by speckle-tracking of the right ventricle (RV) and left atrium (LA) can be performed by manual or automatic methods. Purpose The objective of our study is to evaluate the degree of correlation between manual measurement method and automatic software used in our imaging laboratory, in a population of healthy individuals and patients with cardiac transthyretin amyloidosis (ATTR). Methods Fifty-seven individuals were included, 30 patients with ATTR and 27 healthy volunteers, who underwent a transthoracic echocardiogram (TTE) from January to December 2019. Classic echocardiographic parameters and myocardial deformation were obtained according to the ASE/ EACVI guidelines. Global and free wall longitudinal strain of the RV (RVGLS, RVFWLS) and LA global strain (LAGS) analysis were obtained using speckle-tracking with two different software: QLAB Philips 10.7 and AutoSTRAIN Tomtec. Measurements analysis was performed by two experienced echocardiographers. Correlation and reproducibility analysis was performed using Pearson correlation coefficient (PCC) and intraclass correlation coefficient (ICC), respectively. Results Seventy-two percent were male and average age was 63±20 years. Linear correlation of RVGLS, RVFWLS and LAGS measurements with both methods reached statistical significance (table). This correlation was stronger and more reliable in the case of the LAGS. The attached figure shows the correlation between the different software in both groups. Conclusions AutoSTRAIN Tomtec automatic measurement method had higher reliability and correlation comparing to manual measurements performed by QLAB 10.7, especially in LA measurements. The obtained results, the application speed and the less operator's dependence of this automatic software support its routine use for RV and LA strain quantification. Funding Acknowledgement Type of funding sources: None. Correlation and reproducibility resultsLinear regression lines

A New Automatic QT-Interval Measurement Method for Wireless ECG Monitoring System Using Smartphone.

QT-interval prolongation is an important parameter for heart arrhythmia diagnosis. It is the time interval from QRS-onset to the T-end of electrocardiogram (ECG). Manual measurement of QT-interval, especially for 12-leads ECG, is time-consuming. Hence, an automatic QT-interval measurement is necessary. A new method for automatic QT-interval measurement is presented in this paper, which mainly consists of three parts, including QRS-complex detection, determination of QRS-onset, and T-end determination. The QRS-complex detection is based on the modified Pan-Tompkins algorithm. The T-end is defined based on Region of Interest (ROI) maximum limit. We compare and test our proposed QT-interval measurement method with reference measurement in term of correlation coefficient and range of 95% LoA. The correlation coefficient and the range of 95% LoA are 0.575 and 0.290, respectively. The proposed method is successfully implemented in ECG monitoring system using smartphone with high performance. The accuracy, positive predictive, and sensitivity of the QRS-complex detection in the system are 99.70%, 99.78%, and 99.92%, respectively. The range of 95% LoA for the comparison between manual and the system’s QT-interval measurement is 0.216. The results show that the proposed method is dependable on the measure of the QT-interval and outperforms the other methods in term of correlation coefficient and range of 95% LoA.

An Automatic Measurement Method of Test Beam Response Based on Spliced Images

Information such as cracks and deflections is the important basis for structural safety. Existing methods have not achieved simultaneous detection. In most existing computer vision measurement systems, the view is fixed due to the fixed position of the camera. Thus, it is difficult to obtain the structures’ overall crack and deflection information. An automatic response measurement method is proposed in this study including ( 1 ) continuous image acquisition and signal transmission system based on self-walking bracket and Internet of Things (IoT), ( 2 ) an image splicing method based on feature matching, and ( 3 ) a crack and deflection measurement method. The self-walking bracket allows the industrial camera to move at a fixed distance to obtain the continuous image of the beam. Next, the spliced image is obtained through the PCA-SIFT method with a screening mechanism. The cracks’ information is acquired by the dual network model. The simplified AKAZE feature detection algorithm and the modified RANSAC are used to track the natural features of the structures. The curve fitting is performed to obtain the deflection curve of the beam under different loads. Experimental results show that the method can directly reflect the crack and deflection information of the beam. The average deviation of width is 11.76%, average deviation of length is 8.18%, and the average deformation deviation is 4.50%, which verifies the practicability of the method and shows great potential to apply it in actual structures.

Assessment of the global noise algorithm for automatic noise measurement in head CT examinations.

PurposeThe global noise (GN) algorithm has been previously introduced as a method for automatic noise measurement in clinical CT images. The accuracy of the GN algorithm has been assessed in abdomen CT examinations, but not in any other body part until now. This work assesses the GN algorithm accuracy in automatic noise measurement in head CT examinations.MethodsA publicly available image dataset of 99 head CT examinations was used to evaluate the accuracy of the GN algorithm in comparison to reference noise values. Reference noise values were acquired using a manual noise measurement procedure. The procedure used a consistent instruction protocol and multiple observers to mitigate the influence of intra‐ and interobserver variation, resulting in precise reference values. Optimal GN algorithm parameter values were determined. The GN algorithm accuracy and the corresponding statistical confidence interval were determined. The GN measurements were compared across the six different scan protocols used in this dataset. The correlation of GN to patient head size was also assessed using a linear regression model, and the CT scanner's X‐ray beam quality was inferred from the model fit parameters.ResultsAcross all head CT examinations in the dataset, the range of reference noise was 2.9–10.2 HU. A precision of ±0.33 HU was achieved in the reference noise measurements. After optimization, the GN algorithm had a RMS error 0.34 HU corresponding to a percent RMS error of 6.6%. The GN algorithm had a bias of +3.9%. Statistically significant differences in GN were detected in 11 out of the 15 different pairs of scan protocols. The GN measurements were correlated with head size with a statistically significant regression slope parameter (p < 10‒7). The CT scanner X‐ray beam quality estimated from the slope parameter was 3.5 cm water HVL (2.8–4.8 cm 95% CI).ConclusionThe GN algorithm was validated for application in head CT examinations. The GN algorithm was accurate in comparison to reference manual measurement, with errors comparable to interobserver variation in manual measurement. The GN algorithm can detect noise differences in examinations performed on different scanner models or using different scan protocols. The trend in GN across patients of different head sizes closely follows that predicted by a physical model of X‐ray attenuation.

An Automated Measurement Method for the Endodontic Working Width of Lower Molars by Means of Parametric Models Using Cone-beam Computed Tomographcy and Micro–Computed Tomography

IntroductionA new method for the approximation of the root canal's cross-sectional shape and its working width using cone-beam computed tomographic (CBCT) or micro–computed tomographic (micro-CT) imaging was introduced. MethodsScanned data from 29 extracted human mandibular first and second molar distal root canals without instrumentation were reconstructed and analyzed with a self-developed measurement algorithm. The 3-dimensional volume models were sliced perpendicular to the vertical axis. Using different 2-dimensional parametric models, the contour of each root canal slice was approximated and used to determine the canal's cross-sectional dimensions. The measurements of minor width, major width, and the root canal's conicity were statistically analyzed using analysis of variance. ResultsThe measured minor and major widths of the investigated root canals were significantly higher (probability value P < .05) when evaluated by CBCT images than the results obtained from micro-CT data. Both dimensions increased starting from the apical foramen (P < .01). The narrowest measured canal widths were 0.19–0.24 mm for CBCT imaging and 0.09–0.21 mm for micro-CT imaging in the apical part. The maximum values for conicity were between 13% and 17% in the cervical third. ConclusionsThe 3-dimensional imaging data from CBCT and micro-CT imaging enabled a valuable anatomic assessment of the root canal's cross-sectional working width along the canal up to the physiological foramen in order to determine an adequate apical diameter as well as the correct measured taper in the cervical and medial part.

Approaches for the digital measurement of rock mass discontinuities

Rock mass characterization plays an essential role in rock engineering activities. Presently several approaches for digitally measuring rock mass discontinuities are available. This study summarizes some of the main technological approaches, together with comparative quantitative evaluations of discontinuity measurements. For the steeply inclined Engelswand outcrop in Tyrol, Austria, an Unmanned Aerial Vehicle was used to acquire a series of photographic images in order to generate a true color georeferenced point cloud and derivative 3D models. Discontinuity orientations were measured by manual, assisted and automated digital approaches. Orientations of individual discontinuity planes and the statistics of the overall discontinuity structure were evaluated. The manual and assisted measurement methods provide compatible results and are considered highly relevant in the context of rock engineering. While automated measurement methods are able to provide an overall indication of the dominating discontinuity pattern, discrimination of individual discontinuity sets is not feasible. While this is a major limiting factor in terms of extracting useful data for rock engineering practice, a dual digital measurement approach is considered prudent when large data sets are being evaluated. This involves conducting rapid automated measurements to obtain an overall representation of the rock mass structure, followed by supplementary manual or assisted measurements to more precisely extract pertinent structural data.

A preliminary study on an image analysis based method for lowest detectable signal measurements in Pulsed Wave Doppler ultrasounds

&lt;span lang="EN-GB"&gt;Nowadays, Doppler system performance evaluation is a widespread issue because a shared worldwide standard is still awaited. Among the recommended Doppler test parameters, the lowest detectable signal could be considered mandatory in Quality Control (QC) protocols for Pulsed Wave (PW) Doppler. Such parameter is defined as the minimum signal level that can be clearly distinguished from noise and therefore, it is considered as related to PW Doppler sensitivity. The present study focuses on proposing and validating a novel image analysis based method for the estimation of the Lowest Detectable Signal in the spectrogram image (LDS&lt;sub&gt;IMG&lt;/sub&gt;), namely Automatic Doppler Sensitivity Measurement Method (ADSMM), as well as to compare its results with the outcomes retrieved from the Naked Eye Doppler Sensitivity Method (NEDSM), based on the mean judgment of three independent observers. Data have been collected from a Doppler flow phantom, through three ultrasound systems for general purpose imaging, equipped with two linear array probes each and with two configuration settings. Results are globally compatible among the proposed methods, US systems and settings. Further studies could be carried out on a higher number of US diagnostic systems, Doppler frequencies and observers, as well as with different probe and phantom models.&lt;/span&gt;

Measuring blast fragmentation at Nui Phao open-pit mine, Vietnam using the Mask R-CNN deep learning model

ABSTRACT Blast fragmentation size distribution is one of the most critical factors in evaluating the blasting results and affecting the downstream mining and processing operations in open-pit mines. Image-based methods are widely applied to address the problem but require heavy user interaction and experience. This study deployed a deep learning model Mask R-CNN to develop an automatic measurement method of blast fragmentation. The model was trained using images captured from real blasting sites in Nui Phao open-pit mine in Vietnam. The trained model reported high average precision scores (Intersection over Union, IoU = 0.5) 92% and 83% for bounding box and segmentation masks, respectively. The results lay a solid technical basis for the automated measurement of blast fragmentation in open-pit mines.

A cost-effective planning method for automatic measurement based on task similarity and octopus optimization

Measurement planning is vital for automatic and digitalized aircraft assembly, and in particular, for the key execution sequences. In this article, a novel planning method that considers both order and efficiency is proposed. General rules are defined for the analysis of measurement tasks and their elements. A Laplace kernel function is then utilized for similarity quantification, and based on iterative two-step elementary transformations of the similarity matrix, the clustering of measurement tasks is achieved. A virtual task is proposed to bridge task clusters, and the preliminary sequences are obtained naturally. Given that the adjacent association results may not be the most efficient, octopus optimization is proposed to solve this special case of the traveling salesman problem. The measurement tasks and clusters are shops and cities, respectively. The octopus has multiple tentacles that traverse all shops, and each tentacle carries one feasible execution order. Experiments performed using aircraft inspection indicate that the proposed method can output multiple orderly measurement sequences. Compared with the preliminary sequences, the efficiency is obviously improved in terms of a decrease in the total measurement time. In addition, the optimal sequences are more even, and the duty ratio of the measurement device is decreased.

Industrial robot-based system design of thickness scanning measurement using ultrasonic

Abstract. Wall thickness is one of the core indicators for measuring the quality of large thin-walled parts such as rocket siding and aircraft skin. However, the traditional handheld thickness measurement method has high labor intensity, low efficiency and poor accuracy consistency. Therefore, an in situ ultrasonic automatic scanning thickness measurement method for large thin-walled parts based on industrial robots is proposed. This “industrial robot + ultrasound” integrated function is a compact system, and a set of innovative methodological or technical solutions is presented, such as (i) TCP and UDP communication protocols being constructed to realize a high-speed and stable communication relationship between the upper computer, robot motion controller and ultrasonic thickness measurement unit; (ii) a coupling gap adjustment method based on eddy-current sensors being adopted to ensure the adaptability of the ultrasonic probe to surface topography of the measured part during scanning measurement; and (iii) a multi-sensor coordinate unified model and coupling gap state discrimination model being established for robot-aided thickness measurement. To verify the feasibility of the proposed method, a series of calibrations and experiments were designed based on the KUKA robot platform and the developed ultrasonic pulse measurement system. Finally, the industrial robot-based ultrasonic thickness scanning measurement has been built and tested for performing the measurement of a rocket tank wall.

Development and validation of a deep learning-based automatic auscultatory blood pressure measurement method

Manual auscultatory is the gold standard for clinical non-invasive blood pressure (BP) measurement, but its usage is decreasing as it requires substantial professional skills and training, and its environmental concerns related to mercury toxicity. As an alternative, automatic oscillometric technique has been used as one of the most common methods for BP measurement, however, it only estimates BPs based on empirical equations. To overcome these problems, this study aimed to develop a deep learning-based automatic auscultatory BP measurement method, and clinically validate its performance. A deep learning-based method that utilized time-frequency characteristics and temporal dependence of segmented Korotkoff sound (KorS) signals and employed convolutional neural network (CNN) and long short-term memory (LSTM) network was developed and trained using KorS and cuff pressure signals recorded from 314 subjects. The BPs determined by the manual auscultatory method was used as the reference for each measurement. The measurement error and BP category classification performance of our proposed method were then validated on a separate dataset of 114 subjects. Its performance in comparison with the oscillometric method was also comprehensively analyzed. The deep learning method achieved measurement errors of 0.2 ± 4.6 mmHg and 0.1 ± 3.2 mmHg for systolic BP and diastolic BP, respectively, and achieved high sensitivity, specificity and accuracy (all > 90 %) in classifying hypertensive subjects, which were better than those of the traditional oscillometric method. This validation study demonstrated that deep learning-based automatic auscultatory BP measurement can be developed to achieve high measurement accuracy and high BP category classification performance.

Investigation of clinically acceptable agreement between two methods of automatic measurement of limb occlusion pressure: a randomised trial

BackgroundDevelopment of automatic, pneumatic tourniquet technology and use of personalised tourniquet pressures has improved the safety and accuracy of surgical tourniquet systems. Personalisation of tourniquet pressure requires accurate measurement of limb occlusion pressure (LOP), which can be measured automatically through two different methods. The ‘embedded LOP’ method measures LOP using a dual-purpose tourniquet cuff acting as both patient sensor and pneumatic effector. The ‘distal LOP’ method measures LOP using a distal sensor applied to the patient’s finger or toe of the operating limb, using photoplethysmography to detect volumetric changes in peripheral blood circulation. The distal LOP method has been used clinically for many years; the embedded LOP method was developed recently with several advantages over the distal LOP method. While both methods have clinically acceptable accuracy in comparison to LOP measured using the manual Doppler ultrasound method, these two automatic methods have not been directly compared. The purpose of this study is to investigate if the embedded and distal methods of LOP measurement have clinically acceptable agreement. The differences in pairs of LOP measurement in the upper and lower limbs of 81 healthy individuals were compared using modified Bland and Altman analysis. In surgery, it is common for cuff pressure to deviate from the pressure setpoint due to limb manipulation. Surgical tourniquet systems utilise a ± 15 mmHg pressure alarm window, whereby if the cuff pressure deviates from the pressure setpoint by > 15 mmHg, an audiovisual alarm is triggered. Therefore, if the difference (bias) ± SE, 95% CI of the bias and SD of differences ± SE in LOP measurement between the embedded and distal methods were all within ±15 mmHg, this would demonstrate that the two methods have clinically acceptable agreement.ResultsLOP measurement using the embedded LOP method was − 0.81 ± 0.75 mmHg (bias ± standard error) lower than the distal LOP method. The 95% confidence interval of the bias was − 2.29 to 0.66 mmHg. The standard deviation of the differences ± standard error was 10.35 ± 0.49 mmHg. These results show that the embedded and distal methods of LOP measurement demonstrate clinically acceptable agreement.ConclusionsThe findings of this study demonstrate clinically acceptable agreement between the embedded and distal methods of LOP measurement. The findings support the use of the embedded LOP method of automatic LOP measurement using dual-purpose tourniquet cuffs to enable accurate, effective and simple prescription of personalised tourniquet cuff pressures in a clinical setting.

Prediction of military combat clothing size using decision trees and 3D body scan data

AimTo determine how well decision tree models can predict tailor-assigned uniform sizes using anthropometry data from the New Zealand Defence Force Anthropometry Survey (NZDFAS). This information may inform automatic sizing systems for military personnel. MethodsAnthropometric data from two separate samples of the New Zealand Defence Force military were used. Data on Army personnel from the NZDFAS (n = 583) were used to develop a series of shirt- and trouser-size prediction models based on decision trees. Different combinations of physical, automatic, and post-processed measurements (the latter two derived from a 3D body scan) were trialled, and the models with the highest cross-validation accuracy were retained. The accuracy of these models were then tested on an independent sample of Army recruits (n = 154). ResultsThe automated measurement method (measurements derived automatically by the body scanner software) were the best predictors of shirt size (58.1% accuracy) and trouser size (61.7%), with body weight and waist girth being the strongest predictors. Clothing sizes that were incorrectly predicted by the model where generally one size above or below the tailor-predicted size. ConclusionsAnthropometry measurements, when used with decision tree models, show promise for classifying clothing size. Methodological changes such as fitting gender-specific models, using additional anthropometry variables, and testing other data mining techniques are avenues for future work. More research is required before fully automated body scanning is a viable option for obtaining fast and accurate clothing sizes for military clothing and logistics departments.

Evaluation comparison of wave amount measurement results in brass-plated tire steel cord using RMSE and cosine similarity

&lt;span lang="IN"&gt;In the production process, quality checking is very important, one of which is on the wire. In the process of making brass-coated steel tire straps sometimes produce quality goods not in accordance with the desired standard values. Checks that are carried out manually have low efficiency and quite high errors occur. So it is necessary to check by measuring the wavelength on the brass plated steel cord automatically. In this study, used 3 automatic measurement methods using 2 evaluations, namely RMSE and Cosine Similarity. The results showed the best measurement using RMSE with method 2. Whereas the worst method uses RMSE with method 1. The smallest RMSE value is 0.0098 and the largest RMSE is 0.0966. The lowest Cosine Similarity value is 0.1253, while the highest Cosine Similarity value is 0.2079.&lt;/span&gt;

Automatic Measurement of Morphological Traits of Typical Leaf Samples.

It is still a challenging task to automatically measure plants. A novel method for automatic plant measurement based on a hand-held three-dimensional (3D) laser scanner is proposed. The objective of this method is to automatically select typical leaf samples and estimate their morphological traits from different occluded live plants. The method mainly includes data acquisition and processing. Data acquisition is to obtain the high-precision 3D mesh model of the plant that is reconstructed in real-time during data scanning by a hand-held 3D laser scanner (ZGScan 717, made in Zhongguan Automation Technology, Wuhan, China). Data processing mainly includes typical leaf sample extraction and morphological trait estimation based on a multi-level region growing segmentation method using two leaf shape models. Four scale-related traits and six corresponding scale-invariant traits can be automatically estimated. Experiments on four groups of different canopy-occluded plants are conducted. Experiment results show that for plants with different canopy occlusions, 94.02% of typical leaf samples can be scanned well and 87.61% of typical leaf samples can be automatically extracted. The automatically estimated morphological traits are correlated with the manually measured values EF (the modeling efficiency) above 0.8919 for scale-related traits and EF above 0.7434 for scale-invariant traits). It takes an average of 196.37 seconds (186.08 seconds for data scanning, 5.95 seconds for 3D plant model output, and 4.36 seconds for data processing) for a plant measurement. The robustness and low time cost of the proposed method for different canopy-occluded plants show potential applications for real-time plant measurement and high-throughput plant phenotype.