A statistical learning appproach to vertebra detection and segmentation from spinal MRI

Szu-Hao Huang*, Shang Hong Lai, Carol L. Novak

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

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.

Original languageEnglish
Title of host publication2008 5th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, Proceedings, ISBI
Pages125-128
Number of pages4
DOIs
StatePublished - 10 Sep 2008
Event2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI - Paris, France
Duration: 14 May 200817 May 2008

Publication series

Name2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI

Conference

Conference2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI
CountryFrance
CityParis
Period14/05/0817/05/08

Keywords

  • AdaBoost
  • Normalized-cut
  • RANSAC
  • Segmentation
  • Spinal MR image
  • Vertebra detection

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