Contextual picking of volumetric structures

This paper presents a novel method for the interactive identification of contextual interest points within volumetric data by picking on a direct volume rendered image. In clinical diagnostics the points of interest are often located in the center of anatomical structures. In order to derive the volumetric position which allows a convenient examination of the intended structure, the system automatically extracts contextual meta information from the DICOM (Digital Imaging and Communications in Medicine) images and the setup of the medical workstation. Along a viewing ray for a volumetric picking, the ray profile is analyzed for structures which are similar to predefined templates from a knowledge base. We demonstrate with our results that the obtained position in 3D can be utilized to highlight a structure in 2D slice views, to interactively calculate centerlines of tubular objects, or to place labels at contextually-defined volumetric positions.

[1]  Jerry L. Prince,et al.  Gradient vector flow: a new external force for snakes , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Stefan Bruckner,et al.  LiveSync++: enhancements of an interaction metaphor , 2008, Graphics Interface.

[3]  Joseph Ross Mitchell,et al.  Sketch-based volumetric seeded region growing , 2006, SBM'06.

[4]  Pere-Pau Vázquez,et al.  Mutual text-image queries , 2007, SCCG.

[5]  Bernhard Preim,et al.  Enhancing Slice-based Visualizations of Medical Volume Data , 2006, EuroVis.

[6]  C. Parisot.  The DICOM standard , 1995, The International Journal of Cardiac Imaging.

[7]  Frank Nielsen,et al.  Volume catcher , 2005, I3D '05.

[8]  Eduard Gröller,et al.  Feature peeling , 2007, GI '07.

[9]  Milan Sonka,et al.  Intrathoracic airway trees: segmentation and airway morphology analysis from low-dose CT scans , 2005, IEEE Transactions on Medical Imaging.

[10]  Dorin Comaniciu,et al.  Vessel detection by mean shift based ray propagation , 2001, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001).

[11]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

[12]  Horst Bischof,et al.  A Novel Approach for Detection of Tubular Objects and Its Application to Medical Image Analysis , 2008, DAGM-Symposium.

[13]  Jörg-Stefan Praßni,et al.  Shape-based transfer functions for volume visualization , 2010, 2010 IEEE Pacific Visualization Symposium (PacificVis).

[14]  Stefan Bruckner,et al.  LiveSync: Deformed Viewing Spheres for Knowledge-Based Navigation , 2007, IEEE Transactions on Visualization and Computer Graphics.

[15]  Horst Bischof,et al.  Extracting Curve Skeletons from Gray Value Images for Virtual Endoscopy , 2008, MIAR.

[16]  Carl-Fredrik Westin,et al.  Tissue Classification Based on 3D Local Intensity Structures for Volume Rendering , 2000, IEEE Trans. Vis. Comput. Graph..

[17]  Philippe C. Cattin,et al.  Automatic Segmentation of the Vessel Lumen from 3D CTA Images of Aortic Dissection , 2006, Bildverarbeitung für die Medizin.

[18]  Jörg-Stefan Praßni,et al.  Stroke-Based Transfer Function Design , 2008, VG/PBG@SIGGRAPH.

[19]  Bernhard Preim,et al.  METK - The Medical Exploration Toolkit , 2008, Bildverarbeitung für die Medizin.