TY - GEN

T1 - Using an approximation to the Euclidean skeleton for efficient collision detection and tissue deformations in surgical simulators

AU - Webster, Roger

AU - Harris, Matt

AU - Shenk, Rod

AU - Blumenstock, John

AU - Gerber, Jesse

AU - Billman, Chad

AU - Benson, Aaron

AU - Haluck, Randy

PY - 2005/1/1

Y1 - 2005/1/1

N2 - This paper describes a technique for efficient collision detection and deformation of abdominal organs in surgical simulation using an approximation of the Euclidean skeleton. Many researchers have developed surgical simulators, but one of the most difficult underlying problems is that of organ-instrument collision detection followed by the deformation of the tissue caused by the instrument. Much of the difficulty is due to the vast number of polygons in high resolution complex organ models. A high resolution gall bladder model for instance can number in the tens of thousands of polygons. Our methodology utilizes the reduction power of the skeleton to reduce computations. First, we recursively compute approximations to the Euclidean skeleton to generate a set of skeletal points for the organ. Then we pre-compute for each vertex in each polygon the associated skeleton point (minimal distance discs). A spring is then connected from each vertex to its associated skeleton point to be used in the deformation algorithm. The data structure for the organ thus stores for each skeletal point its maximum and minimum distances and the list of associated vertices. A heuristic algorithm using the skeleton structure of the instrument and the skeleton of the organ is used to determine instrument collisions with the organ.

AB - This paper describes a technique for efficient collision detection and deformation of abdominal organs in surgical simulation using an approximation of the Euclidean skeleton. Many researchers have developed surgical simulators, but one of the most difficult underlying problems is that of organ-instrument collision detection followed by the deformation of the tissue caused by the instrument. Much of the difficulty is due to the vast number of polygons in high resolution complex organ models. A high resolution gall bladder model for instance can number in the tens of thousands of polygons. Our methodology utilizes the reduction power of the skeleton to reduce computations. First, we recursively compute approximations to the Euclidean skeleton to generate a set of skeletal points for the organ. Then we pre-compute for each vertex in each polygon the associated skeleton point (minimal distance discs). A spring is then connected from each vertex to its associated skeleton point to be used in the deformation algorithm. The data structure for the organ thus stores for each skeletal point its maximum and minimum distances and the list of associated vertices. A heuristic algorithm using the skeleton structure of the instrument and the skeleton of the organ is used to determine instrument collisions with the organ.

UR - http://www.scopus.com/inward/record.url?scp=23844451821&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=23844451821&partnerID=8YFLogxK

M3 - Conference contribution

C2 - 2005138315

AN - SCOPUS:23844451821

SN - 1586034987

SN - 9781586034986

T3 - Studies in Health Technology and Informatics

SP - 596

EP - 598

BT - Medicine Meets Virtual Reality 13

PB - IOS Press

T2 - 13th Annual Conference on Medicine Meets Virtual Reality: The Magical Next Becomes the Medical Now, MMVR 2005

Y2 - 26 January 2005 through 29 January 2005

ER -