Publications by authors named "Tai-ang Liu"
In Chinese criminal law, the ages of 12, 14, 16, and 18 years old play a significant role in the determination of criminal responsibility. In this study, we developed an epiphyseal grading system based on magnetic resonance image (MRI) of the hand and wrist for the Chinese Han population and explored the feasibility of employing deep learning techniques for bone age assessment based on MRI of the hand and wrist. This study selected 282 Chinese Han Chinese males aged 6.
View Article and Find Full Text PDFBone development shows certain regularity with age. The regularity can be used to infer age and serve many fields such as justice, medicine, archaeology, etc. As a non-invasive evaluation method of the epiphyseal development stage, MRI is widely used in living age estimation.
View Article and Find Full Text PDFObjectives: To reduce the dimension of characteristic information extracted from pelvic CT images by using principal component analysis (PCA) and partial least squares (PLS) methods. To establish a support vector machine (SVM) classification and identification model to identify if there is pelvic injury by the reduced dimension data and evaluate the feasibility of its application.
Methods: Eighty percent of 146 normal and injured pelvic CT images were randomly selected as training set for model fitting, and the remaining 20% was used as testing set to verify the accuracy of the test, respectively.
In the forensic estimation of bone age, the pelvis is important for identifying the bone age of teenagers. However, studies on this topic remain insufficient as a result of lower accuracy due to the overlapping of pelvic organs in X-ray images. Segmentation networks have been used to automate the location of key pelvic areas and minimize restrictions like doubling images of pelvic organs to increase the accuracy of estimation.
View Article and Find Full Text PDFArticle Synopsis
- The study focused on using a support vector machine (SVM) to automatically classify epiphyseal growth in the distal radius and ulna of teenagers.
- X-ray images from 140 Chinese teens were analyzed, and the growth was categorized into five grades using features extracted from the images.
- Results indicated that the SVM method was successful and reliable, achieving a high level of accuracy in classifying epiphyseal growth.
[Application of PCA-SVR to NIR prediction model for tobacco chemical composition].
Guang Pu Xue Yu Guang Pu Fen Xi
December 2007
Article Synopsis
- - The study analyzed near infrared diffuse reflectance spectra from 50 tobacco samples using Principal Component Analysis (PCA) to develop calibration models for key tobacco components through Support Vector Regression (SVR).
- - The models were optimized with various parameters, and their accuracy was tested using the leave-one-out cross-validation method, yielding low RMSE values for nicotine and other sugars and nitrogen.
- - It was concluded that the SVR model outperformed other methods like Partial Least Squares and Back Propagation Neural Networks, suggesting that near infrared spectroscopy combined with SVR is effective for quickly determining the concentrations of main tobacco components.