Learning-Based Vertebra Detection and Iterative Normalized-Cut Segmentation for Spinal MRI

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

129 Scopus citations


Automatic extraction of vertebra regions from a spinal magnetic resonance (MR) 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 system, which consists of three stages; namely, AdaBoost-based vertebra detection, detection refinement via robust curve fitting, and vertebra segmentation by an iterative normalized cut algorithm. In order to produce an efficient and effective vertebra detector, a statistical learning approach based on an improved AdaBoost algorithm is proposed. A robust estimation procedure is applied on the detected vertebra locations to fit a spine curve, thus refining the above vertebra detection results. This refinement process involves removing the false detections and recovering the miss-detected vertebrae. Finally, an iterative normalized-cut segmentation algorithm is proposed to segment the precise vertebra regions from the detected vertebra locations. In our implementation, the proposed AdaBoost-based detector is trained from 22 spinal MR volume images. The experimental results show that the proposed vertebra detection and segmentation system can achieve nearly 98% vertebra detection rate and 96% segmentation accuracy on a variety of testing spinal MR images. Our experiments also show the vertebra detection and segmentation accuracies by using the proposed algorithm are superior to those of the previous representative methods. The proposed vertebra detection and segmentation system is proved to be robust and accurate so that it can be used for advanced research and application on spinal MR images.

Original languageEnglish
Article number4967966
Pages (from-to)1595-1605
Number of pages11
JournalIEEE Transactions on Medical Imaging
Issue number10
StatePublished - 1 Oct 2009


  • Adaptive boosting (AdaBoost) learning
  • Magnetic resonance imaging (MRI)
  • Normalized-cut segmentation
  • Spine
  • Vertebra detection

Fingerprint Dive into the research topics of 'Learning-Based Vertebra Detection and Iterative Normalized-Cut Segmentation for Spinal MRI'. Together they form a unique fingerprint.

Cite this