Automated lung nodule segmentation using dynamic progamming and EM based classifcation

摘要

There is provided a method for automatically segmenting lung nodules in a three-dimensional (3D) Computed Tomography (CT) volume dataset. An input is received corresponding to a user-selected point near a boundary of a nodule. A model is constructed of the nodule from the user-selected point, the model being a deformable circle having a set of parameters &bgr; that represent a shape of the nodule. Continuous parts of the boundary and discontinuities of the boundary are estimated until the set of parameters &bgr; converges, using dynamic programming and Expectation Maximization (EM). The nodule is segmented, based on estimates of the continuous parts of the boundary and the discontinuities of the boundary.