32,484 research outputs found
A fast and robust patient specific Finite Element mesh registration technique: application to 60 clinical cases
Finite Element mesh generation remains an important issue for patient
specific biomechanical modeling. While some techniques make automatic mesh
generation possible, in most cases, manual mesh generation is preferred for
better control over the sub-domain representation, element type, layout and
refinement that it provides. Yet, this option is time consuming and not suited
for intraoperative situations where model generation and computation time is
critical. To overcome this problem we propose a fast and automatic mesh
generation technique based on the elastic registration of a generic mesh to the
specific target organ in conjunction with element regularity and quality
correction. This Mesh-Match-and-Repair (MMRep) approach combines control over
the mesh structure along with fast and robust meshing capabilities, even in
situations where only partial organ geometry is available. The technique was
successfully tested on a database of 5 pre-operatively acquired complete femora
CT scans, 5 femoral heads partially digitized at intraoperative stage, and 50
CT volumes of patients' heads. The MMRep algorithm succeeded in all 60 cases,
yielding for each patient a hex-dominant, Atlas based, Finite Element mesh with
submillimetric surface representation accuracy, directly exploitable within a
commercial FE software
A statistical shape model for deformable surface
This short paper presents a deformable surface registration scheme which is based on the statistical shape
modelling technique. The method consists of two major processing stages, model building and model
fitting. A statistical shape model is first built using a set of training data. Then the model is deformed and
matched to the new data by a modified iterative closest point (ICP) registration process. The proposed
method is tested on real 3-D facial data from BU-3DFE database. It is shown that proposed method can
achieve a reasonable result on surface registration, and can be used for patient position monitoring in
radiation therapy and potentially can be used for monitoring of the radiation therapy progress for head and
neck patients by analysis of facial articulation
GOGMA: Globally-Optimal Gaussian Mixture Alignment
Gaussian mixture alignment is a family of approaches that are frequently used
for robustly solving the point-set registration problem. However, since they
use local optimisation, they are susceptible to local minima and can only
guarantee local optimality. Consequently, their accuracy is strongly dependent
on the quality of the initialisation. This paper presents the first
globally-optimal solution to the 3D rigid Gaussian mixture alignment problem
under the L2 distance between mixtures. The algorithm, named GOGMA, employs a
branch-and-bound approach to search the space of 3D rigid motions SE(3),
guaranteeing global optimality regardless of the initialisation. The geometry
of SE(3) was used to find novel upper and lower bounds for the objective
function and local optimisation was integrated into the scheme to accelerate
convergence without voiding the optimality guarantee. The evaluation
empirically supported the optimality proof and showed that the method performed
much more robustly on two challenging datasets than an existing
globally-optimal registration solution.Comment: Manuscript in press 2016 IEEE Conference on Computer Vision and
Pattern Recognitio
Part-to-whole Registration of Histology and MRI using Shape Elements
Image registration between histology and magnetic resonance imaging (MRI) is
a challenging task due to differences in structural content and contrast. Too
thick and wide specimens cannot be processed all at once and must be cut into
smaller pieces. This dramatically increases the complexity of the problem,
since each piece should be individually and manually pre-aligned. To the best
of our knowledge, no automatic method can reliably locate such piece of tissue
within its respective whole in the MRI slice, and align it without any prior
information. We propose here a novel automatic approach to the joint problem of
multimodal registration between histology and MRI, when only a fraction of
tissue is available from histology. The approach relies on the representation
of images using their level lines so as to reach contrast invariance. Shape
elements obtained via the extraction of bitangents are encoded in a
projective-invariant manner, which permits the identification of common pieces
of curves between two images. We evaluated the approach on human brain
histology and compared resulting alignments against manually annotated ground
truths. Considering the complexity of the brain folding patterns, preliminary
results are promising and suggest the use of characteristic and meaningful
shape elements for improved robustness and efficiency.Comment: Paper accepted at ICCV Workshop (Bio-Image Computing
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