By Topic

A support vector machines classifier to assess the severity of idiopathic scoliosis from surface topography

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
L. Ramirez ; Dept. of Electr. & Comput. Eng., Univ. of Alberta, Edmonton, Alta., Canada ; N. G. Durdle ; V. J. Raso ; D. L. Hill

A support vector machines (SVM) classifier was used to assess the severity of idiopathic scoliosis (IS) based on surface topographic images of human backs. Scoliosis is a condition that involves abnormal lateral curvature and rotation of the spine that usually causes noticeable trunk deformities. Based on the hypothesis that combining surface topography and clinical data using a SVM would produce better assessment results, we conducted a study using a dataset of 111 IS patients. Twelve surface and clinical indicators were obtained for each patient. The result of testing on the dataset showed that the system achieved 69-85% accuracy in testing. It outperformed a linear discriminant function classifier and a decision tree classifier on the dataset

Published in:

IEEE Transactions on Information Technology in Biomedicine  (Volume:10 ,  Issue: 1 )