Title
A statistical learning appproach to vertebra detection and segmentation from spinal MRI
Abstract
Automatically extracting vertebra regions from a spinal magnetic resonance image is normally required as the first step to an intelligent spinal MR image diagnosis system. In this work, we develop a fully automatic vertebra detection and segmentation method. Our system consists of three stages; namely, AdaBoost-based vertebra detection, detection refinement via robust curve fitting, and vertebra segmentation by an iterative normalized cut algorithm. We proposed an efficient and effective vertebra detector, which is trained by the improved AdaBoost algorithm, to locate the initial vertebra positions. Then, a robust estimation procedure is applied to fit all the vertebrae as a polynomial spinal curve to refine the vertebra detection results. Finally, an iterative segmentation algorithm based on normalized-cut energy minimization is applied to extract the precise vertebra regions from the detected windows. The experimental results show our system can achieve high accuracy on a number of testing 3D spinal MRI data sets.
Year
DOI
Venue
2008
10.1109/ISBI.2008.4540948
ISBI
Keywords
Field
DocType
spinal mri,neurophysiology,statistical analysis,learning (artificial intelligence),normalized-cut energy minimization,image segmentation,polynomial spinal curve,iterative segmentation algorithm,adaboost,vertebra detection,biomedical mri,vertebra segmentation,segmentation,ransac,statistical learning,normalized-cut,medical image processing,adaboost algorithm,spinal mr image,curve fitting,learning artificial intelligence,magnetic resonance image,magnetic resonance,robust estimator,magnetic resonance imaging,spine,polynomials,energy minimization,detectors,robustness
Computer vision,Data set,AdaBoost,Curve fitting,Pattern recognition,Polynomial,RANSAC,Computer science,Segmentation,Image segmentation,Artificial intelligence,Vertebra
Conference
ISSN
ISBN
Citations 
1945-7928
978-1-4244-2003-2
5
PageRank 
References 
Authors
0.56
9
3
Name
Order
Citations
PageRank
Szu-Hao Huang114311.87
Shang-Hong Lai21169124.03
Carol L. Novak314623.88