Abstract: The invention relates to a method for segmentation of a three-dimensional structure in a three-dimensional data set, especially a medical data set. The method uses a three-dimensional deformable model, wherein the surface of the model consists of a net of polygonal meshes. The meshes are split into groups, and a feature term is assigned to each group. After the model has been placed over the structure of interest, the deformable model is recalculated in consideration of the feature terms of each group.

Abstract: An object, such as an example blood vessel, in a two or three dimensional image data set is segmented. An adaptable model, such as an example cylinder model, is defined around a starting point in the example blood vessel and is adapted or fit to the blood vessel. A plurality of candidate next active points are defined around the starting point and the adaptable model is defined around each candidate point. The models around the candidate points are adapted to the blood vessel. Based on results of the fitted models, a next active point is selected. In this manner, the blood vessel is segmented by adapting a series of cylinder models to an inner surface of the blood vessel.

Abstract: The invention relates to a method and to an apparatus for visualizing a sequence of volume images of a moving object. Methods and apparatus of this kind are used in cases where a sequence of three-dimensional volume images is to be rendered as a two-dimensional image, for example, for a viewer. The invention utilizes the fact that usually only the volume values of a part of the voxels are relevant for the derivation of a two-dimensional image from a volume image. In the case of a sequence of volume images of a moving object, the derivations of the two dimensional images can be accelerated by storing the voxels which are relevant for the visualization during the visualization of a first volume image and by deriving the relevant two-dimensional image during the visualization of a second volume image exclusively from the volume values of the stored voxels and from voxels neighboring such stored voxels. The selection of volume values of neighboring voxels for use is dependent on the motion of the object.

Abstract: A method of segmenting a selected region from a multi-dimensional dataset, which method comprises the steps of setting up a shape model representing the general outline of the selected region and setting up an adaptive mesh. The adaptive mesh represents an approximate contour of the selected region. The adaptive mesh is initialized on the basis of the shape model. Furthermore, the adaptive mesh is deformed in dependence on the shape model and on feature information of the selected region.

Abstract: The invention relates to a computationally efficient method for the automated detection of intensity transitions in 2D or 3D image data. Contrasting boundaries in the image are indicated as global or local maxima of a gradient integral function, which is calculated by applying a Laplace operator to the intensity values of each pixel or voxel of the image data set. Only one pass through the image data set is required if the gradient integral function is computed by means of a cumulative histogram technique. The detected intensity thresholds can advantageously be employed for the specification of rendering parameters for visualization purposes. The method of the invention is also well-suited for the rendering and measurement of lung nodules, as the detection of correct intensity thresholds turns out to be crucial for the reproducible and consistent interpretation of medical image data.

Abstract: The invention relates to a method and an apparatus for segmentation of an object in a 2D or 3D image data set by extracting a path along the object.

Abstract: The invention relates to a method of segmenting a three-dimensional structure, contained in an object, from one or more two-dimensional images which represent a slice of the object. The method utilizes a deformable model whose surface is formed by a network of meshes which connect network points on the surface of the model to one another. First there are determined the meshes which intersect at least one image and a point on the surface of the structure to be segmented is searched along a search line which traverses the mesh and extends in the image. Subsequently, the position of the network points of the model is calculated anew. These steps are repeated a number of times and the model ultimately obtained, that is, after several deformations, is considered to be the segmentation of the three-dimensional structure from the two-dimensional images.

Abstract: A method of segmenting a selected region from a multi-dimensional dataset, which method comprises the steps of setting up a shape model representing the general outline of the selected region and setting up an adaptive mesh. The adaptive mesh represents an approximate contour of the selected region. The adaptive mesh is initialized on the basis of the shape model. Furthermore, the adaptive mesh is deformed in dependence on the shape model and on feature information of the selected region.