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

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

*此作品的通信作者

研究成果: Conference contribution同行評審

8 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2008 5th IEEE International Symposium on Biomedical Imaging
主出版物子標題From Nano to Macro, Proceedings, ISBI
頁面125-128
頁數4
DOIs
出版狀態Published - 10 9月 2008
事件2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI - Paris, France
持續時間: 14 5月 200817 5月 2008

出版系列

名字2008 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
國家/地區France
城市Paris
期間14/05/0817/05/08

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