Department of Computer Science, National Tsing Hua University, Hsinchu 300, Taiwan.
IEEE Trans Med Imaging. 2009 Oct;28(10):1595-605. doi: 10.1109/TMI.2009.2023362.
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.
自动从脊髓磁共振（MR）图像中提取脊椎区域通常是智能脊髓 MR 图像诊断系统的第一步。在这项工作中，我们开发了一个全自动的脊椎检测和分割系统，它由三个阶段组成：基于 AdaBoost 的脊椎检测、通过稳健的曲线拟合进行检测细化，以及通过迭代归一化切割算法进行脊椎分割。为了生成一个高效且有效的脊椎检测器，我们提出了一种基于改进的 AdaBoost 算法的统计学习方法。在检测到的脊椎位置上应用稳健估计过程来拟合脊柱曲线，从而细化上述脊椎检测结果。此细化过程包括去除错误检测和恢复遗漏检测的脊椎。最后，提出了一种迭代归一化切割分割算法，从检测到的脊椎位置中分割精确的脊椎区域。在我们的实现中，基于所提出的 AdaBoost 的检测器是从 22 个脊髓 MR 体积图像中训练得到的。实验结果表明，所提出的脊椎检测和分割系统在各种测试脊髓 MR 图像上可以达到近 98%的脊椎检测率和 96%的分割精度。我们的实验还表明，所提出算法的脊椎检测和分割精度优于以前的代表性方法。所提出的脊椎检测和分割系统被证明是稳健和准确的，因此可以用于脊髓 MR 图像的高级研究和应用。
