By Topic

A new approach to automatic disc localization in clinical lumbar MRI: Combining machine learning with heuristics

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)
Subarna Ghosh ; Department of Computer Science and Engineering, State University of New York (SUNY) at Buffalo, Buffalo, NY 14260 ; Manavender R. Malgireddy ; Vipin Chaudhary ; Gurmeet Dhillon

Lower back pain (LBP) is widely prevalent in people all over the world and negatively affects the quality of life due to chronic pain and change in posture. Automatic localization of intervertebral discs from lumbar MRI is the first step towards computer-aided diagnosis of lower back ailments. Till date, most of the research has been useful in determining a point within each lumbar disc, hence we go one step further and propose a localization method which outputs a tight bounding box for each disc. We use HOG (Histogram of Oriented Gradients) features along with SVM (Support Vector Machine) as classifier and successfully combine these machine learning techniques with heuristics to achieve 99% disc localization accuracy on 53 clinical cases (318 lumbar discs). We also devise our own metrics to evaluate the accuracy and tightness of our disc bounding box and compare our results with previous research.

Published in:

2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)

Date of Conference:

2-5 May 2012