7,520 research outputs found
Mesh-to-raster based non-rigid registration of multi-modal images
Region of interest (ROI) alignment in medical images plays a crucial role in
diagnostics, procedure planning, treatment, and follow-up. Frequently, a model
is represented as triangulated mesh while the patient data is provided from CAT
scanners as pixel or voxel data. Previously, we presented a 2D method for
curve-to-pixel registration. This paper contributes (i) a general
mesh-to-raster (M2R) framework to register ROIs in multi-modal images; (ii) a
3D surface-to-voxel application, and (iii) a comprehensive quantitative
evaluation in 2D using ground truth provided by the simultaneous truth and
performance level estimation (STAPLE) method. The registration is formulated as
a minimization problem where the objective consists of a data term, which
involves the signed distance function of the ROI from the reference image, and
a higher order elastic regularizer for the deformation. The evaluation is based
on quantitative light-induced fluoroscopy (QLF) and digital photography (DP) of
decalcified teeth. STAPLE is computed on 150 image pairs from 32 subjects, each
showing one corresponding tooth in both modalities. The ROI in each image is
manually marked by three experts (900 curves in total). In the QLF-DP setting,
our approach significantly outperforms the mutual information-based
registration algorithm implemented with the Insight Segmentation and
Registration Toolkit (ITK) and Elastix
Retrieval and Registration of Long-Range Overlapping Frames for Scalable Mosaicking of In Vivo Fetoscopy
Purpose: The standard clinical treatment of Twin-to-Twin Transfusion Syndrome
consists in the photo-coagulation of undesired anastomoses located on the
placenta which are responsible to a blood transfer between the two twins. While
being the standard of care procedure, fetoscopy suffers from a limited
field-of-view of the placenta resulting in missed anastomoses. To facilitate
the task of the clinician, building a global map of the placenta providing a
larger overview of the vascular network is highly desired. Methods: To overcome
the challenging visual conditions inherent to in vivo sequences (low contrast,
obstructions or presence of artifacts, among others), we propose the following
contributions: (i) robust pairwise registration is achieved by aligning the
orientation of the image gradients, and (ii) difficulties regarding long-range
consistency (e.g. due to the presence of outliers) is tackled via a bag-of-word
strategy, which identifies overlapping frames of the sequence to be registered
regardless of their respective location in time. Results: In addition to visual
difficulties, in vivo sequences are characterised by the intrinsic absence of
gold standard. We present mosaics motivating qualitatively our methodological
choices and demonstrating their promising aspect. We also demonstrate
semi-quantitatively, via visual inspection of registration results, the
efficacy of our registration approach in comparison to two standard baselines.
Conclusion: This paper proposes the first approach for the construction of
mosaics of placenta in in vivo fetoscopy sequences. Robustness to visual
challenges during registration and long-range temporal consistency are
proposed, offering first positive results on in vivo data for which standard
mosaicking techniques are not applicable.Comment: Accepted for publication in International Journal of Computer
Assisted Radiology and Surgery (IJCARS
Feature-Based Models for Three-Dimensional Data Fitting.
There are numerous techniques available for fitting a surface to any supplied data set. The feature-based modeling technique takes advantage of the known, geometric shape of the data by deforming a model having this generic shape to approximate the data. The model is constructed as a rational B-spline surface with characteristic features superimposed on its definition. The first step in the fitting process is to align the model with a data set using the center of mass, principal axes and/or landmarks. Using this initial orientation, the position, rotation and scale parameters are optimized using a Newton-type optimization of a least squares cost function. Once aligned, features embedded within the model, corresponding to pertinent characteristics of the shape, are used to improve the fit of the model to the data. Finally, the control vertex weights and positions of the rational B-spline model are optimized to approximate the data to within a specified tolerance. Since the characteristic features are defined within the model a creation, important measures are easily extracted from a data set, once fit. The feature-based modeling approach is demonstrated in two-dimensions by the fitting of five facial, silhouette profiles and in three-dimensions by the fitting of eleven human foot scans. The algorithm is tested for sensitivity to data distribution and structure and the extracted measures are tested for repeatability and accuracy. Limitations within the current implementation, future work and potential applications are also provided
Fundamental remote sensing science research program. Part 1: Status report of the mathematical pattern recognition and image analysis project
The Mathematical Pattern Recognition and Image Analysis (MPRIA) Project is concerned with basic research problems related to the study of the Earth from remotely sensed measurement of its surface characteristics. The program goal is to better understand how to analyze the digital image that represents the spatial, spectral, and temporal arrangement of these measurements for purposing of making selected inference about the Earth
A poroelastic model coupled to a fluid network with applications in lung modelling
Here we develop a lung ventilation model, based a continuum poroelastic
representation of lung parenchyma and a 0D airway tree flow model. For the
poroelastic approximation we design and implement a lowest order stabilised
finite element method. This component is strongly coupled to the 0D airway tree
model. The framework is applied to a realistic lung anatomical model derived
from computed tomography data and an artificially generated airway tree to
model the conducting airway region. Numerical simulations produce
physiologically realistic solutions, and demonstrate the effect of airway
constriction and reduced tissue elasticity on ventilation, tissue stress and
alveolar pressure distribution. The key advantage of the model is the ability
to provide insight into the mutual dependence between ventilation and
deformation. This is essential when studying lung diseases, such as chronic
obstructive pulmonary disease and pulmonary fibrosis. Thus the model can be
used to form a better understanding of integrated lung mechanics in both the
healthy and diseased states
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Morphable Face Models - An Open Framework
In this paper, we present a novel open-source pipeline for face registration
based on Gaussian processes as well as an application to face image analysis.
Non-rigid registration of faces is significant for many applications in
computer vision, such as the construction of 3D Morphable face models (3DMMs).
Gaussian Process Morphable Models (GPMMs) unify a variety of non-rigid
deformation models with B-splines and PCA models as examples. GPMM separate
problem specific requirements from the registration algorithm by incorporating
domain-specific adaptions as a prior model. The novelties of this paper are the
following: (i) We present a strategy and modeling technique for face
registration that considers symmetry, multi-scale and spatially-varying
details. The registration is applied to neutral faces and facial expressions.
(ii) We release an open-source software framework for registration and
model-building, demonstrated on the publicly available BU3D-FE database. The
released pipeline also contains an implementation of an Analysis-by-Synthesis
model adaption of 2D face images, tested on the Multi-PIE and LFW database.
This enables the community to reproduce, evaluate and compare the individual
steps of registration to model-building and 3D/2D model fitting. (iii) Along
with the framework release, we publish a new version of the Basel Face Model
(BFM-2017) with an improved age distribution and an additional facial
expression model
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