Paper Info
Title
Constructing detailed solid and smooth surfaces from voxel data for neurosurgical simulation
Abstract
This paper deals with a neurosurgical simulation system with precise volume rendering and smooth tactile sensation. In the system, the Octree based hierarchical representation of volume data with continuous tri-cubic parametric functions, called volumetric implicit functions, and smooth boundary surfaces are introduced to provide detailed solid and smooth tactile sensation in an interactive environment. The volume data represented as voxel data, which are created from CT or MRI images, are divided into sub-volume until volumetric implicit functions can approximate voxel values accurately. An Octree manages the divided volume and parameters of the implicit functions in a hierarchical manner. Furthermore, smooth boundary surfaces are constructed by fitting points on a level surface of the implicit functions. In order to render more detailed solid than voxel precision when objects are zoomed up, sub-sampled voxels are generated by using the implicit functions. As for the tactile sensation, haptic device, PHANToM, is used to actualize a smooth reaction force which is calculated by the surface normal and the distance from a position of an instrument to the nearest surface. Incision with tactile sensation can be executed by making voxels underlying the instrument transparent, when a reaction force is greater than a limit. Several experiments reveal the effectiveness of the proposed methods.
Year
DOI
Venue
2005
10.1007/11424857_109
ICCSA (3)
Keywords
Field
DocType
precise volume rendering,volumetric implicit function,neurosurgical simulation,tactile sensation,smooth reaction force,smooth surface,divided volume,smooth boundary surface,approximate voxel,volume data,implicit function,smooth tactile sensation,voxel data,haptic device,volume rendering
Voxel,Computer vision,Parametric equation,Volume rendering,Curve fitting,Computer science,Implicit function,Artificial intelligence,Smoothness,Normal,Octree
Conference
Volume
ISSN
ISBN
3482
0302-9743
3-540-25862-0
Citations 
PageRank 
References 
0
0.34
8
Authors
2
Name
Order
Citations
PageRank
Mayumi Shimizu100.34
Yasuaki Nakamura2105140.45