Geometrically deformed models: a method for extracting closed geometric models form volume data

TLDR: This work proposes a new approach to the problem of generating a simple topologically-closed geometric model from a point-sampled volume data set, called Geometrically Deformed Model or GDM, which is created by placing a 'seed' model in thevolume data set.

Abstract: We propose a new approach to the problem of generating a simple topologically-closed geometric model from a point-sampled volume data set. We call such a model a Geometrically Deformed Model or GDM. A GDM is created by placing a 'seed' model in the volume data set. The model is then deformed by a relaxation process that minimizes a set of constraints that provides a measure of how well the model fits the features in the data. Constraints are associated with each vertex in the model that control local deformation, interaction between the model and the data set, and the shape and topology of the model. Once generated, a GDM can be used for visualization, shape recognition, geometric measurements, or subjected to a series of geometric operations. This technique is of special importance because of the advent of nondestructive sensing equipment (CT, MRI) that generates point samples of true three-dimensional objects.