Automated lung nodule segmentation using dynamic programming and EM based classification

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 beta that represent a shape of the nodule. Continuous parts of the boundary and discontinuities of the boundary are estimated until the set of parameters beta 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.