Skeletal growth estimation using radiographic image processing and analysis.
An automated knowledge-based vision system for skeletal growth estimation in children is reported in this paper. Images were obtained from hand radiographs of 32 male and 25 female children of age 1-16 yr. Phalanx bones were automatically localized and segmented using hierarchical inferences and active shape models, respectively. A number of shape descriptors were obtained from the segmented bone contour to quantify skeletal growth. From these descriptors, a feature vector was selected for a regression model and a Bayesian estimator. The estimation accuracy was 84% for females and 82% for males. This level of accuracy is comparable to that of expert pediatric radiologists, which suggests that the proposed approach has a potential application in pediatric medicine.
Mahmoodi
S
S
Department of Electrical and Electronic Engineering, University of Newcastle, Newcastle upon Tyne, UK.
Sharif
B S
BS
Chester
E G
EG
Owen
J P
JP
Lee
R
R
D000293:Adolescent; D000365:Age Determination by Skeleton / Q000379:methods*; D001499:Bayes Theorem; D001846:Bone Development; D001842:Bone and Bones / Q000000981:diagnostic imaging; D002648:Child; D002675:Child, Preschool; D005260:Female; D006225:Hand / Q000000981:diagnostic imaging; D006801:Humans; D007223:Infant; D008297:Male; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*