Ph.D. - Medical Image Registration and Surgery Simulation
Real-time deformation for surgery simulation
Real-time deformable models, using finite
element models of linear elasticity, are developed
for use in surgery simulation. The time consumption of
the finite element method is reduced dramatically,
by the use of condensation techniques, explicit
inversion of the stiffness matrix, and the use
of selective matrix vector multiplication.
Medical image registration
A new and faster algorithm for non-rigid registration using
viscous fluid models is presented. This algorithm replaces
the core part of the original algorithm with multi-resolution
convolution using a new filter, which implements the linear
elasticity operator. Using the filter results in a speedup
of at least an order of magnitude. Use of convolution
hardware is expected to improve the performance even more.
Mandibular growth for time registration of mandibles
Non-rigid registration using a physically valid model
of bone growth is also presented. Using medical knowledge
about the growth processes of the mandibular bone,
a registration algorithm for time sequence images of
the mandible is developed. Since this registration
algorithm models the actual development of the
mandible, it is possible to simulate the development.
3D visualization - the Mvox software
is a software package for medical researchers that want to analyze volumetric
medical images in 3D. Using advanced rendering algorithms and the computer
graphics hardware in SGI workstations, Mvox produces eye-opening 3D
Cranio-facial surgery simulation
of cranio-facial surgery is difficult and at the same time essential to the
outcome. In this project I developed a methodology for modeling the deformation
of soft tissue in the face of a cranio-facial patient in response to changes in
topology of the underlying bones.
2D and 3D modeling using Active Nets/Cubes
topic of this project is deformable models in general. I have implemented a
range of the physical models described in the literature: 2D & 3D active
contours and 2D active nets.
Rigid medical image registration
Rigid image registration using voxel similarity measures
are reviewed, and new measures based on Grey Level Co-occurrence
Matrices (GLCM) are introduced. These measures are
using CT, MR, and cryo-section images from the
Visible Human data set.
The results show that mutual information remains the best
generally applicable measure. But for specific modality
combinations the new GLCM measures show considerable