Abstract: Apparatus for navigating an instrument through an anatomical structure of a patient's body volume, comprising a table for supporting the patient and at least a first C-arm having a first X-ray source and a first X-ray detector for acquiring a first series of 2D-images of the instrument while maneuvering through said anatomical structure, and further comprising a processing unit for the images which unit connects to a memory device, whereby the memory device holds pre-determined 3D-images of the patient's anatomical structure, and the processing unit is arranged for processing the 2D-images of the instrument and the 3D-images of the anatomical structure so as to provide merged 3D-images of the instrument that in use maneuvers through said anatomical structure.

Abstract: The present invention relates to an X-ray examination apparatus and a corresponding method for acquiring X-ray image data of a region of interest by use of an imaging unit (1-3) comprising an X-ray source (2) for emitting X-ray radiation and an X-ray detector (3) for detecting X-ray radiation after penetration of said region of interest.

Abstract: The invention relates to a method of image segmentation for delineating a structure associated with a reference structure in an image. For this purpose a segmentation of the reference structure is accessed. The appearance of different tissue types is learned using the model by non parametric robust estimation that employs a fuzzy kNN classifier in two stages (outlier reduction and final estimation). The model is used to provide seed points for the segmentation. The graph cut method is adapted to perform segmentation of the sought structure. The invention further relates to a system and a computer program for image segmentation.

Abstract: The method 1 according to the invention is preferably practiced in real time and directly after a suitable acquisition 3 of the multi-dimensional dataset, which is accessed at step 5 and the images constituting the multi-dimensional dataset are classified at step 8. Preferably, for reducing an amount of data to be processed at step 6 the image data is subjected to a restrictive region of interest determination. At step 9 the classified cardiac images are subjected to a an image thinning operator so that the resulting images comprise a plurality of connected image components which are further analyzed at step 14. After the thinning step 9 a labeling step 11 is performed, where different connected components in the multi-dimensional dataset are accordingly labeled. This step is preferably followed by a region growing step 13, which is constrained by binary threshold used at step 8b. For each connected image component a factor F is computed at step 14.