522 research outputs found
Automated 3D model generation for urban environments [online]
Abstract
In this thesis, we present a fast approach to automated
generation of textured 3D city models with both high details at
ground level and complete coverage for birds-eye view.
A ground-based facade model is acquired by driving a vehicle
equipped with two 2D laser scanners and a digital camera under
normal traffic conditions on public roads. One scanner is
mounted horizontally and is used to determine the approximate
component of relative motion along the movement of the
acquisition vehicle via scan matching; the obtained relative
motion estimates are concatenated to form an initial path.
Assuming that features such as buildings are visible from both
ground-based and airborne view, this initial path is globally
corrected by Monte-Carlo Localization techniques using an aerial
photograph or a Digital Surface Model as a global map. The
second scanner is mounted vertically and is used to capture the
3D shape of the building facades. Applying a series of automated
processing steps, a texture-mapped 3D facade model is
reconstructed from the vertical laser scans and the camera
images. In order to obtain an airborne model containing the roof
and terrain shape complementary to the facade model, a Digital
Surface Model is created from airborne laser scans, then
triangulated, and finally texturemapped with aerial imagery.
Finally, the facade model and the airborne model are fused
to one single model usable for both walk- and fly-thrus. The
developed algorithms are evaluated on a large data set acquired
in downtown Berkeley, and the results are shown and discussed
Four-dimensional cardiac imaging in living embryos via postacquisition synchronization of nongated slice sequences
Being able to acquire, visualize, and analyze 3D time series
(4D data) from living embryos makes it possible to understand complex
dynamic movements at early stages of embryonic development.
Despite recent technological breakthroughs in 2D dynamic imaging,
confocal microscopes remain quite slow at capturing optical sections
at successive depths. However, when the studied motion is periodic—
such as for a beating heart—a way to circumvent this problem is to
acquire, successively, sets of 2D+time slice sequences at increasing
depths over at least one time period and later rearrange them to recover
a 3D+time sequence. In other imaging modalities at macroscopic
scales, external gating signals, e.g., an electro-cardiogram,
have been used to achieve proper synchronization. Since gating signals
are either unavailable or cumbersome to acquire in microscopic
organisms, we have developed a procedure to reconstruct volumes
based solely on the information contained in the image sequences.
The central part of the algorithm is a least-squares minimization of an
objective criterion that depends on the similarity between the data
from neighboring depths. Owing to a wavelet-based multiresolution
approach, our method is robust to common confocal microscopy artifacts.
We validate the procedure on both simulated data and in vivo
measurements from living zebrafish embryos
Reconstruction of scenes using a hand-held range imaging camera
The current nal degree project covers the development of a C++ program to reconstruct 3D
scenes from a stream of incoming RGBD images from the Kinect for Windows v2 sensor using the
PCL library and the libfreenect2 open source Kinect drivers. A background analysis is performed
to analyze state of the art registration algorithms and similar registration applications which are
already distributed.
All the computing steps are explained and detailed, from the data acquisition through the
coarse and ne alignment steps to the nal surface reconstruction algorithm. Several algorithms
are discussed for each step, giving reasons for the nal algorithm choice and the parameters set.
Finally, a brief discussion on the results and problems encountered during the development of
the project serves as the basis of a proposal of improvements for future work on the subject. The
project budget and the environmental impact are analyzed before the nal conclusion chapter
Diffeomorphic Registration of Images with Variable Contrast Enhancement
Nonrigid image registration is widely used to estimate
tissue deformations in highly deformable anatomies. Among
the existing methods, nonparametric registration algorithms
such as optical flow, or Demons, usually have the advantage of
being fast and easy to use. Recently, a diffeomorphic version
of the Demons algorithm was proposed. This provides the
advantage of producing invertible displacement fields, which
is a necessary condition for these to be physical. However,
such methods are based on the matching of intensities and
are not suitable for registering images with different contrast
enhancement. In such cases, a registration method based on the
local phase like the Morphons has to be used. In this paper, a
diffeomorphic version of the Morphons registration method is
proposed and compared to conventional Morphons, Demons,
and diffeomorphic Demons. The method is validated in the
context of radiotherapy for lung cancer patients on several
4D respiratory-correlated CT scans of the thorax with and without
variable contrast enhancement
Parallel Computation of Nonrigid Image Registration
Automatic intensity-based nonrigid image registration brings significant impact in medical applications such as multimodality fusion of images, serial comparison for monitoring disease progression or regression, and minimally invasive image-guided interventions. However, due to memory and compute intensive nature of the operations, intensity-based image registration has remained too slow to be practical for clinical adoption, with its use limited primarily to as a pre-operative too. Efficient registration methods can lead to new possibilities for development of improved and interactive intraoperative tools and capabilities.
In this thesis, we propose an efficient parallel implementation for intensity-based three-dimensional nonrigid image registration on a commodity graphics processing unit. Optimization techniques are developed to accelerate the compute-intensive mutual information computation. The study is performed on the hierarchical volume subdivision-based algorithm, which is inherently faster than other nonrigid registration algorithms and structurally well-suited for data-parallel computation platforms. The proposed implementation achieves more than 50-fold runtime improvement over a standard implementation on a CPU. The execution time of nonrigid image registration is reduced from hours to minutes while retaining the same level of registration accuracy
Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues
Light propagating in tissue attains a spectrum that varies with location due
to wavelength-dependent fluence attenuation by tissue optical properties, an
effect that causes spectral corruption. Predictions of the spectral variations
of light fluence in tissue are challenging since the spatial distribution of
optical properties in tissue cannot be resolved in high resolution or with high
accuracy by current methods. Spectral corruption has fundamentally limited the
quantification accuracy of optical and optoacoustic methods and impeded the
long sought-after goal of imaging blood oxygen saturation (sO2) deep in
tissues; a critical but still unattainable target for the assessment of
oxygenation in physiological processes and disease. We discover a new principle
underlying light fluence in tissues, which describes the wavelength dependence
of light fluence as an affine function of a few reference base spectra,
independently of the specific distribution of tissue optical properties. This
finding enables the introduction of a previously undocumented concept termed
eigenspectra Multispectral Optoacoustic Tomography (eMSOT) that can effectively
account for wavelength dependent light attenuation without explicit knowledge
of the tissue optical properties. We validate eMSOT in more than 2000
simulations and with phantom and animal measurements. We find that eMSOT can
quantitatively image tissue sO2 reaching in many occasions a better than
10-fold improved accuracy over conventional spectral optoacoustic methods.
Then, we show that eMSOT can spatially resolve sO2 in muscle and tumor;
revealing so far unattainable tissue physiology patterns. Last, we related
eMSOT readings to cancer hypoxia and found congruence between eMSOT tumor sO2
images and tissue perfusion and hypoxia maps obtained by correlative
histological analysis
Efficient 3D Segmentation, Registration and Mapping for Mobile Robots
Sometimes simple is better! For certain situations and tasks, simple but robust methods can achieve the same or better results in the same or less time than related sophisticated approaches. In the context of robots operating in real-world environments, key challenges are perceiving objects of interest and obstacles as well as building maps of the environment and localizing therein. The goal of this thesis is to carefully analyze such problem formulations, to deduce valid assumptions and simplifications, and to develop simple solutions that are both robust and fast. All approaches make use of sensors capturing 3D information, such as consumer RGBD cameras. Comparative evaluations show the performance of the developed approaches. For identifying objects and regions of interest in manipulation tasks, a real-time object segmentation pipeline is proposed. It exploits several common assumptions of manipulation tasks such as objects being on horizontal support surfaces (and well separated). It achieves real-time performance by using particularly efficient approximations in the individual processing steps, subsampling the input data where possible, and processing only relevant subsets of the data. The resulting pipeline segments 3D input data with up to 30Hz. In order to obtain complete segmentations of the 3D input data, a second pipeline is proposed that approximates the sampled surface, smooths the underlying data, and segments the smoothed surface into coherent regions belonging to the same geometric primitive. It uses different primitive models and can reliably segment input data into planes, cylinders and spheres. A thorough comparative evaluation shows state-of-the-art performance while computing such segmentations in near real-time. The second part of the thesis addresses the registration of 3D input data, i.e., consistently aligning input captured from different view poses. Several methods are presented for different types of input data. For the particular application of mapping with micro aerial vehicles where the 3D input data is particularly sparse, a pipeline is proposed that uses the same approximate surface reconstruction to exploit the measurement topology and a surface-to-surface registration algorithm that robustly aligns the data. Optimization of the resulting graph of determined view poses then yields globally consistent 3D maps. For sequences of RGBD data this pipeline is extended to include additional subsampling steps and an initial alignment of the data in local windows in the pose graph. In both cases, comparative evaluations show a robust and fast alignment of the input data
Fast fully automatic myocardial segmentation in 4D cine cardiac magnetic resonance datasets
Dissertação de mestrado integrado em Engenharia BiomédicaCardiovascular diseases (CVDs) are the leading cause of death in the world, representing
30% of all global deaths. Among others, assessment of the left ventricular (LV) morphology and
global function using non-invasive cardiac imaging is an interesting technique for diagnosis and
treatment follow-up of patients with CVDs. Nowadays, cardiac magnetic resonance (CMR)
imaging is the gold-standard technique for the quantification of LV volumes, mass and ejection
fraction, requiring the delineation of endocardial and epicardial contours of the left ventricle from
cine MR images. In clinical practice, the physicians perform this segmentation manually, being a
tedious, time consuming and unpractical task. Even though several (semi-)automated methods
have been presented for LV CMR segmentation, fast, automatic and optimal boundaries
assessment is still lacking, usually requiring the physician to manually correct the contours.
In the present work, we propose a novel fast fully automatic 3D+time LV segmentation
framework for CMR datasets. The proposed framework presents three conceptual blocks: 1) an
automatic 2D mid-ventricular initialization and segmentation; 2) an automatic stack initialization
followed by a 3D segmentation at the end-diastolic phase; and 3) a tracking procedure to
delineate both endo and epicardial contours throughout the cardiac cycle. In each block, specific
CMR-targeted algorithms are proposed for the different steps required. Hereto, we propose
automatic and feasible initialization procedures. Moreover, we adapt the recent B-spline Explicit
Active Surfaces (BEAS) framework to the properties of CMR image segmentation by integrating
dedicated energy terms and making use of a cylindrical coordinate system that better fits the
topology of CMR data. At last, two tracking methods are presented and compared.
The proposed framework has been validated on 45 4D CMR datasets from a publicly
available database and on a large database from an ongoing multi-center clinical trial with 318
4D datasets. In the technical validation, the framework showed competitive results against the
state-of-the-art methods, presenting leading results in both accuracy and average computational
time in the common database used for comparative purposes. Moreover, the results in the large
scale clinical validation confirmed the high feasibility and robustness of the proposed framework
for accurate LV morphology and global function assessment. In combination with the low
computational burden of the method, the present methodology seems promising to be used in
daily clinical practice.As doenças cardiovasculares (DCVs) são a principal causa de morte no mundo,
representando 30% destas a nível global. Na prática clínica, uma técnica empregue no
diagnóstico de pacientes com DCVs é a avaliação da morfologia e da função global do ventrículo
esquerdo (VE), através de técnicas de imagiologia não-invasivas. Atualmente, a ressonância
magnética cardíaca (RMC) é a modalidade de referência na quantificação dos volumes, massa e
fração de ejeção do VE, exigindo a delimitação dos contornos do endocárdio e epicárdio a partir
de imagens dinâmicas de RMC. Na prática clínica diária, o método preferencial é a segmentação
manual. No entanto, esta é uma tarefa demorada, sujeita a erro humano e pouco prática. Apesar
de até à data diversos métodos (semi)-automáticos terem sido apresentados para a
segmentação do VE em imagens de RMC, ainda não existe um método capaz de avaliar
idealmente os contornos de uma forma automática, rápida e precisa, levando a que geralmente
o médico necessite de corrigir manualmente os contornos.
No presente trabalho é proposta uma nova framework para a segmentação automática
do VE em imagens 3D+tempo de RMC. O algoritmo apresenta três blocos principais: 1) uma
inicialização e segmentação automática 2D num corte medial do ventrículo; 2) uma inicialização
e segmentação tridimensional no volume correspondente ao final da diástole; e 3) um algoritmo
de tracking para obter os contornos ao longo de todo o ciclo cardíaco. Neste sentido, são
propostos procedimentos de inicialização automática com elevada robustez. Mais ainda, é
proposta uma adaptação da recente framework “B-spline Explicit Active Surfaces” (BEAS) com a
integração de uma energia específica para as imagens de RMC e utilizando uma formulação
cilíndrica para tirar partido da topologia destas imagens. Por último, são apresentados e
comparados dois algoritmos de tracking para a obtenção dos contornos ao longo do tempo.
A framework proposta foi validada em 45 datasets de RMC provenientes de uma base de
dados disponível ao público, bem como numa extensa base de dados com 318 datasets para
uma validação clínica. Na avaliação técnica, a framework proposta obteve resultados
competitivos quando comparada com outros métodos do estado da arte, tendo alcançado
resultados de precisão e tempo computacional superiores a estes. Na validação clínica em larga
escala, a framework provou apresentar elevada viabilidade e robustez na avaliação da morfologia
e função global do VE. Em combinação com o baixo custo computacional do algoritmo, a
presente metodologia apresenta uma perspetiva promissora para a sua aplicação na prática
clínica diária
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