人力資源與數位學習科技研究所

Abstract

　　An automatic technique of extracting vertebra regions from a spinal magnetic resonance (MR) image is normally required the first step to an intelligent spinal MR image diagnosis system. In this work, we develop a fully automatic vertebra detection and segmentation system based on a statistical machine learning approach. 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. 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. A second or higher degree curve fitting method is applied to fit a vertebra configuration model. Finally, an iterative segmentation algorithm based on a normalized-cut energy minimization is applied to segment the precise vertebra pixels from detected window. The experimental results show our system can achieve high accuracy in vertebra detection and segmentation on a number of testing 3D spinal MRI data sets.