Regression Algorithm of Bone Age Estimation of Knee-joint Based on Principal Component Analysis and Support Vector Machine.
10.12116/j.issn.1004-5619.2019.02.012
- Author:
Yi Yang LEI
1
;
Yu Shu SHEN
2
;
Ya Hui WANG
3
;
Hu ZHAO
1
Author Information
1. Department of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.
2. Department of Polymer Materials, School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China.
3. Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
- Publication Type:Journal Article
- Keywords:
forensic anthropology;
age determination by skeleton;
knee joint;
support vector machine;
principal component analysis;
histogram of oriented gradient;
local binary patterns;
Uygur nationality;
adolescent
- MeSH:
Adolescent;
Adult;
Age Determination by Skeleton/methods*;
Algorithms;
Asian People/ethnology*;
China;
Female;
Humans;
Image Processing, Computer-Assisted;
Knee Joint/diagnostic imaging*;
Machine Learning;
Male;
Principal Component Analysis;
Support Vector Machine;
Young Adult
- From:
Journal of Forensic Medicine
2019;35(2):194-199
- CountryChina
- Language:English
-
Abstract:
Objective To establish a regression algorithm model that applies to bone age estimation of Xinjiang Uygur adolescents with machine learning methods such as histogram of oriented gradient （HOG）, local binary patterns （LBP）, support vector machine （SVM）, principal component analysis （PCA）. Methods DR images of knee-joints from 275 male and 225 female subjects aged 12.0-<19.0 years old were collected, PCA method was used to reduce the dimensionality of the HOG and LBP features, and support vector regression （SVR） was used to establish a knee-joint bone age estimation algorithm model. Stratified random sampling method was used to select 215 male samples, 180 female samples for the model training set. K-fold cross validation method was used to optimize parameters of the model. The remaining samples as the independent test set was used to compare the sample's true age and model estimated age, and had an accuracy rate in the statistical error range of ±0.8 and ±1.0 years, respectively. Then the mean absolute error （MAE） and root mean square error （RMSE） were calculated. Results The accuracy rate of male in the statistical error range of ±0.8 and ±1.0 year was 80.67%, 89.33%, respectively. The MAE and RMSE were 0.486 and 0.606 years, respectively. The accuracy rate of female in the statistical error range of ±0.8 and ±1.0 years was 80.19%, 90.45%, respectively. The MAE and RMSE were 0.485 and 0.590 years, respectively. Conclusion Establishment of prediction model for bone age estimation by feature dimension reduction of HOG and LBP in DR images of knee-joint based on PCA and SVM has relatively high accuracy.
