Volumetric segmentation via three-dimensional active shape models

Abstract

A volumetric image segmentation algorithm has been developed and implemented by extending a 2D algorithm based on Active Shape Models. The new technique allows segmentation of 3D objects that are embedded within volumetric image data. The extension from 2D involved four components: landmarking, shape modeling, gray-level modeling, and segmentation. Algorithms and software tools have been implemented to allow a user to efficiently landmark a 3D object training set. Additional tools were built that subsequently generate models of 3D object shape and gray-level appearance based on this training data. An object segmentation strategy was implemented that optimizes these models to segment a previously unseen instance of the object. The nature of volumetric images required the development of tools to visualize the features of the models within the volumes. Results of this new 3D segmentation algorithm have been generated for synthetic as well as x-ray CT volumetric image data.