在医学领域里，计算机X线摄影(computed radiography，CR)影像系统已经进入全新的发展阶段。图像分割在医学图像处理中占有很重要的位置，由于医学图像的一些特殊性，不同的分割方法会产生不同的效果。以CR数字胸片图像为研究对象，给出了概率松弛迭代法、K-均值聚类法和高斯曲面阈值法在胸片肋骨分割中的应用，并且对其结果给予了一定的评价。实验结果表明，几种分割方法中高斯曲面阈值法更为有效，它方便后继处理，可以得到比较完整的肋骨信息，为后期的计算机辅助诊断提供更为可靠的实验数据。

In medical domain, the Computed radiography image system comes into a brand-new development. Image segmentation takes an important place in medical image processing. Different segmentation methods used in medical images cause different effects due to the particularities of medical images. With digital chest radiographs as research objects in this paper, several segmentation methods are presented to detect the ribs in digital chest radiographs, which include iterative probabilistic relaxation, K-means clustering and Gaussian curve plane threshold methods, etc. The evaluations of their results are given in the end of the paper. The experimental results indicate that Gaussian curve plane threshold method is more effective than the others to detect the ribs in digital chest radiographs. By using it, subsequent processing can be simplified and full information of the ribs can be obtained, so more reliable experiment data can be supplied to subsequent computer aided diagnosis.