Chronological age assessment based on wrist radiograph processing – Some novel approaches

Assessing the age of an individual via bones serves as a technique in determination of individual skills. In this work, the assessment of chronological age for varying age groups of individuals is carried out using left hand wrist radiographs. The datasets employed for experimentation are preprocessed and extracted using an automated segmentation technique using bit plane level data of radiograph images. The flow of proposed work is comprised of three stages, in stage 1 preprocessing is carried out, classification of preprocessed radiographs are classified into male and female samples using convolution kernels based deep neural net. Further, distance features are extracted from the origin of carpal bones to tip of extracted phalangeal regions in the classified outcomes from stage 2 using imtool image analyzer. Finally, classification of distance features is performed using Support Vector Machines with Gaussian Kernel (SVM-GK) to label the radiographs into ages from 1 to 17. The experimentation is performed on the datasets of Pediatric Bone Age challenge of Radiological Society of North America (RSNA) of about 12000 images of 1–17 year age groups. The convergence between actual and clinically validated chronological age is also tested with Gaussian process regression model (GPRM) along with SVM. A very minimal loss of about 4.7% is occurred during classification using deep neural network. The classification accuracy is found to be 76.8% and 88.1% and 0.75 and 1.41 RMSE with respect to GPRM and SVM-GK.

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Paperboard Coating Detection Based on Full-Stokes Imaging Polarimetry

Sensors ◽

10.3390/s21010208 ◽

2020 ◽

Vol 21 (1) ◽

pp. 208

Author(s):

Javier Brugés Martelo ◽

Jan Lundgren ◽

Mattias Andersson

Keyword(s):

Food Packaging ◽

Imaging Systems ◽

Gaussian Kernel ◽

Support Vector ◽

Camera System ◽

Distance Correlation ◽

Imaging Polarimetry ◽

Packaging Industry ◽

Thickness Variations

The manufacturing of high-quality extruded low-density polyethylene (PE) paperboard intended for the food packaging industry relies on manual, intrusive, and destructive off-line inspection by the process operators to assess the overall quality and functionality of the product. Defects such as cracks, pinholes, and local thickness variations in the coating can occur at any location in the reel, affecting the sealable property of the product. To detect these defects locally, imaging systems must discriminate between the substrate and the coating. We propose an active full-Stokes imaging polarimetry for the classification of the PE-coated paperboard and its substrate (before applying the PE coating) from industrially manufactured samples. The optical system is based on vertically polarized illumination and a novel full-Stokes imaging polarimetry camera system. From the various parameters obtained by polarimetry measurements, we propose implementing feature selection based on the distance correlation statistical method and, subsequently, the implementation of a support vector machine algorithm that uses a nonlinear Gaussian kernel function. Our implementation achieves 99.74% classification accuracy. An imaging polarimetry system with high spatial resolution and pixel-wise metrological characteristics to provide polarization information, capable of material classification, can be used for in-process control of manufacturing coated paperboard.

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Classification of Time Series Using Singular Values and Wavelet Subband Analysis with ANN and SVM Classifiers

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2006.p0498 ◽

2006 ◽

Vol 10 (4) ◽

pp. 498-503 ◽

Cited By ~ 3

Author(s):

Balázs Benyó ◽

◽

Péter Somogyi ◽

Béla Paláncz ◽

◽

...

Keyword(s):

Blood Flow ◽

Singular Values ◽

Gaussian Kernel ◽

Support Vector ◽

Spectral Matrix ◽

Support Vector Classifier ◽

Brain States ◽

Cerebral Circulation Disorders ◽

Blood Flow Patterns

Oscillation of cerebral blood flow (CBF) in physiological or pathophysiological brain states is common, therefore it is promising to identify cerebral circulation disorders based on CBF signal classification. To characterize temporal blood flow patterns, we applied two feature extractions, spectral matrix and wavelet subband analysis. To distinguish between different physiological states, two different classifications have been developed - the radial basis function-based neural network and a support vector classifier with a Gaussian kernel. Feature extraction and classification are evaluated and their efficiency compared. Calculation was done using <I>Mathematica</I> 5.1 and its <I>Wavelet Application</I>.

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Two-Level Classification in Determining the Age and Gender Group of a Speaker

The International Arab Journal of Information Technology ◽

10.34028/iajit/18/5/5 ◽

2021 ◽

Vol 18 (5) ◽

Author(s):

Ergün Yücesoy

Keyword(s):

Classification Accuracy ◽

Age Groups ◽

Gaussian Mixture ◽

Support Vector ◽

Optimum Number ◽

Mel Frequency Cepstral Coefficients ◽

Age And Gender ◽

Number Of Components ◽

And Gender

In this study, the classification of the speakers according to age and gender was discussed. Age and gender classes were first examined separately, and then by combining these classes a classification with a total of 7 classes was made. Speech signals represented by Mel-Frequency Cepstral Coefficients (MFCC) and delta parameters were converted into Gaussian Mixture Model (GMM) mean supervectors and classified with a Support Vector Machine (SVM). While the GMM mean supervectors were formed according to the Maximum-a-posteriori (MAP) adaptive GMM-Universal Background Model (UBM) configuration, the number of components was changed from 16 to 512, and the optimum number of components was decided. Gender classification accuracy of the system developed using aGender dataset was measured as 99.02% for two classes and 92.58% for three classes and age group classification accuracy was measured as 67.03% for female and 63.79% for male. In the classification of age and gender classes together in one step, an accuracy of 61.46% was obtained. In the study, a two-level approach was proposed for classifying age and gender classes together. According to this approach, the speakers were first divided into three classes as child, male and female, then males and females were classified according to their age groups and thus a 7-class classification was realized. This two-level approach was increased the accuracy of the classification in all other cases except when 32-component GMMs were used. While the highest improvement of 2.45% was achieved with 64 component GMMs, an improvement of 0.79 was achieved with 256 component GMMs.

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Chronological Age Assessment in Young Individuals Using Bone Age Assessment Staging and Nonradiological Aspects: Machine Learning Multifactorial Approach (Preprint)

10.2196/preprints.18846 ◽

2020 ◽

Author(s):

Ana Luiza Dallora ◽

Ola Kvist ◽

Johan Sanmartin Berglund ◽

Sandra Diaz Ruiz ◽

Martin Boldt ◽

...

Keyword(s):

Machine Learning ◽

Age Groups ◽

Bone Age ◽

Chronological Age ◽

Learning Methods ◽

Age Assessment ◽

Male And Female ◽

Machine Learning Methods ◽

Bone Age Assessment ◽

Female Subjects

BACKGROUND Bone age assessment (BAA) is used in numerous pediatric clinical settings as well as in legal settings when entities need an estimate of chronological age (CA) when valid documents are lacking. The latter case presents itself as critical as the law is harsher for adults and granted rights along with imputability changes drastically if the individual is a minor. Traditional BAA methods have drawbacks such as exposure of minors to radiation, they do not consider factors that might affect the bone age, and they mostly focus on a single region. Given the critical scenarios in which BAA can affect the lives of young individuals, it is important to focus on the drawbacks of the traditional methods and investigate the potential of estimating CA through BAA. OBJECTIVE This study aims to investigate CA estimation through BAA in young individuals aged 14-21 years with machine learning methods, addressing the drawbacks of research using magnetic resonance imaging (MRI), assessment of multiple regions of interest, and other factors that may affect the bone age. METHODS MRI examinations of the radius, distal tibia, proximal tibia, distal femur, and calcaneus were performed on 465 men and 473 women (aged 14-21 years). Measures of weight and height were taken from the subjects, and a questionnaire was given for additional information (self-assessed Tanner Scale, physical activity level, parents' origin, and type of residence during upbringing). Two pediatric radiologists independently assessed the MRI images to evaluate their stage of bone development (blinded to age, gender, and each other). All the gathered information was used in training machine learning models for CA estimation and minor versus adult classification (threshold of 18 years). Different machine learning methods were investigated. RESULTS The minor versus adult classification produced accuracies of 0.90 and 0.84 for male and female subjects, respectively, with high recalls for the classification of minors. The CA estimation for the 8 age groups (aged 14-21 years) achieved mean absolute errors of 0.95 years and 1.24 years for male and female subjects, respectively. However, for the latter, a lower error occurred only for the ages of 14 and 15 years. CONCLUSIONS This study investigates CA estimation through BAA using machine learning methods in 2 ways: minor versus adult classification and CA estimation in 8 age groups (aged 14-21 years), while addressing the drawbacks in the research on BAA. The first achieved good results; however, for the second case, the BAA was not precise enough for the classification.

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Chronological Age Assessment in Young Individuals Using Bone Age Assessment Staging and Nonradiological Aspects: Machine Learning Multifactorial Approach

JMIR Medical Informatics ◽

10.2196/18846 ◽

2020 ◽

Vol 8 (9) ◽

pp. e18846

Author(s):

Ana Luiza Dallora ◽

Ola Kvist ◽

Johan Sanmartin Berglund ◽

Sandra Diaz Ruiz ◽

Martin Boldt ◽

...

Keyword(s):

Machine Learning ◽

Age Groups ◽

Bone Age ◽

Chronological Age ◽

Learning Methods ◽

Age Assessment ◽

Male And Female ◽

Machine Learning Methods ◽

Bone Age Assessment ◽

Female Subjects

Background Bone age assessment (BAA) is used in numerous pediatric clinical settings as well as in legal settings when entities need an estimate of chronological age (CA) when valid documents are lacking. The latter case presents itself as critical as the law is harsher for adults and granted rights along with imputability changes drastically if the individual is a minor. Traditional BAA methods have drawbacks such as exposure of minors to radiation, they do not consider factors that might affect the bone age, and they mostly focus on a single region. Given the critical scenarios in which BAA can affect the lives of young individuals, it is important to focus on the drawbacks of the traditional methods and investigate the potential of estimating CA through BAA. Objective This study aims to investigate CA estimation through BAA in young individuals aged 14-21 years with machine learning methods, addressing the drawbacks of research using magnetic resonance imaging (MRI), assessment of multiple regions of interest, and other factors that may affect the bone age. Methods MRI examinations of the radius, distal tibia, proximal tibia, distal femur, and calcaneus were performed on 465 men and 473 women (aged 14-21 years). Measures of weight and height were taken from the subjects, and a questionnaire was given for additional information (self-assessed Tanner Scale, physical activity level, parents' origin, and type of residence during upbringing). Two pediatric radiologists independently assessed the MRI images to evaluate their stage of bone development (blinded to age, gender, and each other). All the gathered information was used in training machine learning models for CA estimation and minor versus adult classification (threshold of 18 years). Different machine learning methods were investigated. Results The minor versus adult classification produced accuracies of 0.90 and 0.84 for male and female subjects, respectively, with high recalls for the classification of minors. The CA estimation for the 8 age groups (aged 14-21 years) achieved mean absolute errors of 0.95 years and 1.24 years for male and female subjects, respectively. However, for the latter, a lower error occurred only for the ages of 14 and 15 years. Conclusions This study investigates CA estimation through BAA using machine learning methods in 2 ways: minor versus adult classification and CA estimation in 8 age groups (aged 14-21 years), while addressing the drawbacks in the research on BAA. The first achieved good results; however, for the second case, the BAA was not precise enough for the classification.

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Classification of Soils into Hydrologic Groups Using Machine Learning

Data ◽

10.3390/data5010002 ◽

2019 ◽

Vol 5 (1) ◽

pp. 2 ◽

Cited By ~ 4

Author(s):

Shiny Abraham ◽

Chau Huynh ◽

Huy Vu

Keyword(s):

Machine Learning ◽

Water Conservation ◽

Large Scale ◽

Performance Metrics ◽

Gaussian Kernel ◽

Support Vector ◽

K Nearest Neighbors ◽

Group B ◽

Soil Groups

Hydrologic soil groups play an important role in the determination of surface runoff, which, in turn, is crucial for soil and water conservation efforts. Traditionally, placement of soil into appropriate hydrologic groups is based on the judgement of soil scientists, primarily relying on their interpretation of guidelines published by regional or national agencies. As a result, large-scale mapping of hydrologic soil groups results in widespread inconsistencies and inaccuracies. This paper presents an application of machine learning for classification of soil into hydrologic groups. Based on features such as percentages of sand, silt and clay, and the value of saturated hydraulic conductivity, machine learning models were trained to classify soil into four hydrologic groups. The results of the classification obtained using algorithms such as k-Nearest Neighbors, Support Vector Machine with Gaussian Kernel, Decision Trees, Classification Bagged Ensembles and TreeBagger (Random Forest) were compared to those obtained using estimation based on soil texture. The performance of these models was compared and evaluated using per-class metrics and micro- and macro-averages. Overall, performance metrics related to kNN, Decision Tree and TreeBagger exceeded those for SVM-Gaussian Kernel and Classification Bagged Ensemble. Among the four hydrologic groups, it was noticed that group B had the highest rate of false positives.

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Biological Maturation, Body Morphology and Physical Performance in 8-16 Year-Old Obese Girls from Montes Claros – Mg

Journal of Human Kinetics ◽

10.2478/hukin-2014-0102 ◽

2014 ◽

Vol 43 (1) ◽

pp. 169-176 ◽

Cited By ~ 2

Author(s):

Alex S. Freitas ◽

António J.B. Figueiredo ◽

Andréia L. R. de Freitas ◽

Vinícius D. Rodrigues ◽

Alexandre A. C. da Cunha ◽

...

Keyword(s):

Body Composition ◽

Physical Performance ◽

Handgrip Strength ◽

Age Groups ◽

Bone Age ◽

Chronological Age ◽

Age At Menarche ◽

Anthropometric Measures ◽

Body Morphology ◽

Biological Maturation

Abstract Measurements of maturity depend on the biological system considered since differences are often found in performance and body size in subjects of the same chronological age. The objective of this study was to identify associations between biological maturation, body morphology and physical performance in girls aged from 8.0 to 15.9 year-old and to verify the bone age in obese girls and compare it with chronological age. For that purpose 2040 (11.9 ± 2.3 years) school girls from Montes Claros, participated in this study. Regular anthropometric measures as height and body mass were taken. Triceps, biceps, subscapular, abdominal, suprailiac and calf skinfolds were also registered. Physical performance was assessed trough the test of a standing long jump, handgrip strength and 20 m multistage shuttle run. Maturational status, the average age at menarche and identification of PHV (maturity off set) were determined by means of the retrospective method. Girls with the BMI above the 95th percentile got their bone age evaluated through X-ray of the left hand/wrist, in accordance with the FELS method. It was possible to find an average age at menarche of 11.30 ± 0.70, while the average age at PHV was 12.17 ± 0.71 years of age. It was observed that both body composition and physical performance showed a tendency to increase with advancing age. However, when controlling the effect of maturation, despite having higher values in body composition the post-menarche girls group did not show higher levels of physical performance. In all age groups, obese girls showed mean rates of bone age higher than chronologic age (12.25 ± 2.09 and 14.09 ± 2.35, respectively, p=0.000). Chronological age should be used with caution when evaluating obese teenagers as it may underestimate biological age.

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Helicopter main reduction planetary gear fault diagnosis method based on SVDD

International Journal of Applied Electromagnetics and Mechanics ◽

10.3233/jae-209316 ◽

2020 ◽

Vol 64 (1-4) ◽

pp. 137-145

Author(s):

Yubin Xia ◽

Dakai Liang ◽

Guo Zheng ◽

Jingling Wang ◽

Jie Zeng

Keyword(s):

Fault Diagnosis ◽

Planetary Gear ◽

Gaussian Kernel ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Energy Characteristics ◽

Gear Fault ◽

Channel Information ◽

Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

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