These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Regression Algorithm of Bone Age Estimation of Knee-joint Based on Principal Component Analysis and Support Vector Machine.
    Author: Lei YY, Shen YS, Wang YH, Zhao H.
    Journal: Fa Yi Xue Za Zhi; 2019 Apr; 35(2):194-199. PubMed ID: 31135114.
    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. 题目: 基于主成分分析和支持向量机实现膝关节骨龄评估回归算法. 摘要: 目的 通过方向梯度直方图（histogram of oriented gradient，HOG）、局部二值模式（local binary patterns，LBP）、支持向量机（support vector machine，SVM）以及主成分分析（principal component analysis，PCA）等机器学习方法构建适用于我国维吾尔族青少年骨龄评估的回归算法模型。 方法 采集维吾尔族12.0~<19.0岁青少年的膝关节DR摄片图像，其中男性样本275例、女性样本225例，采用PCA法对提取的HOG与LBP特征图像进行降维，再以支持向量回归（support vector regression，SVR）算法构建膝关节骨龄评估算法模型。采用随机分层抽样法分别选取男性样本215例、女性样本180例作为SVR模型训练集，并用K折交叉验证法优化模型参数。剩余样本作为独立测试集，将模型预报年龄与样本真实年龄相比，统计误差范围分别在±0.8岁、±1.0岁的准确率，同时计算平均绝对误差（mean absolute error，MAE）与均方根误差（root mean square error，RMSE）。 结果 男性年龄误差范围在±0.8岁及±1.0岁的准确率分别为80.67%和89.33%，MAE为0.486岁，RMSE为0.606岁；女性年龄误差范围在±0.8岁及±1.0岁的准确率分别为80.19%和90.45%，MAE为0.485岁，RMSE为0.590岁。 结论 基于PCA与SVM对膝关节DR摄片图像HOG及LBP特征降维建立骨龄的预报模型，具有较高的准确性。. 关键词: 法医人类学；年龄测定，骨骼；膝关节；支持向量机；主成分分析；方向梯度直方图；局部二值模式；维吾尔族；青少年.
    [Abstract] [Full Text] [Related] [New Search]