PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Skeletal Growth Estimation Using Radiographic Image Processing and Analysis
Sasan Mahmoodi, Bayan Sharif, Graeme Chester, J. Owen and R. Lee
IEEE Transactions on Information Technology in Biomedicine Volume 4, Number 4, pp. 292-297, 2000.

Abstract

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:4251
Deposited By:Sasan Mahmoodi
Deposited On:20 December 2008