305 research outputs found
Model and Appearance Based Analysis of Neuronal Morphology from Different Microscopy Imaging Modalities
The neuronal morphology analysis is key for understanding how a brain works. This process requires the neuron imaging system with single-cell resolution; however, there is no feasible system for the human brain. Fortunately, the knowledge can be inferred from the model organism, Drosophila melanogaster, to the human system. This dissertation explores the morphology analysis of Drosophila larvae at single-cell resolution in static images and image sequences, as well as multiple microscopy imaging modalities. Our contributions are on both computational methods for morphology quantification and analysis of the influence of the anatomical aspect. We develop novel model-and-appearance-based methods for morphology quantification and illustrate their significance in three neuroscience studies.
Modeling of the structure and dynamics of neuronal circuits creates understanding about how connectivity patterns are formed within a motor circuit and determining whether the connectivity map of neurons can be deduced by estimations of neuronal morphology. To address this problem, we study both boundary-based and centerline-based approaches for neuron reconstruction in static volumes.
Neuronal mechanisms are related to the morphology dynamics; so the patterns of neuronal morphology changes are analyzed along with other aspects. In this case, the relationship between neuronal activity and morphology dynamics is explored to analyze locomotion procedures. Our tracking method models the morphology dynamics in the calcium image sequence designed for detecting neuronal activity. It follows the local-to-global design to handle calcium imaging issues and neuronal movement characteristics.
Lastly, modeling the link between structural and functional development depicts the correlation between neuron growth and protein interactions. This requires the morphology analysis of different imaging modalities. It can be solved using the part-wise volume segmentation with artificial templates, the standardized representation of neurons. Our method follows the global-to-local approach to solve both part-wise segmentation and registration across modalities.
Our methods address common issues in automated morphology analysis from extracting morphological features to tracking neurons, as well as mapping neurons across imaging modalities. The quantitative analysis delivered by our techniques enables a number of new applications and visualizations for advancing the investigation of phenomena in the nervous system
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
Training robust deep learning (DL) systems for disease detection from medical
images is challenging due to limited images covering different disease types
and severity. The problem is especially acute, where there is a severe class
imbalance. We propose an active learning (AL) framework to select most
informative samples for training our model using a Bayesian neural network.
Informative samples are then used within a novel class aware generative
adversarial network (CAGAN) to generate realistic chest xray images for data
augmentation by transferring characteristics from one class label to another.
Experiments show our proposed AL framework is able to achieve state-of-the-art
performance by using about of the full dataset, thus saving significant
time and effort over conventional methods
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
QUIS-CAMPI: Biometric Recognition in Surveillance Scenarios
The concerns about individuals security have justified the increasing number of surveillance
cameras deployed both in private and public spaces. However, contrary to popular belief,
these devices are in most cases used solely for recording, instead of feeding intelligent analysis
processes capable of extracting information about the observed individuals. Thus, even though
video surveillance has already proved to be essential for solving multiple crimes, obtaining relevant
details about the subjects that took part in a crime depends on the manual inspection
of recordings. As such, the current goal of the research community is the development of
automated surveillance systems capable of monitoring and identifying subjects in surveillance
scenarios. Accordingly, the main goal of this thesis is to improve the performance of biometric
recognition algorithms in data acquired from surveillance scenarios. In particular, we aim at
designing a visual surveillance system capable of acquiring biometric data at a distance (e.g.,
face, iris or gait) without requiring human intervention in the process, as well as devising biometric
recognition methods robust to the degradation factors resulting from the unconstrained
acquisition process.
Regarding the first goal, the analysis of the data acquired by typical surveillance systems
shows that large acquisition distances significantly decrease the resolution of biometric samples,
and thus their discriminability is not sufficient for recognition purposes. In the literature,
diverse works point out Pan Tilt Zoom (PTZ) cameras as the most practical way for acquiring
high-resolution imagery at a distance, particularly when using a master-slave configuration. In
the master-slave configuration, the video acquired by a typical surveillance camera is analyzed
for obtaining regions of interest (e.g., car, person) and these regions are subsequently imaged
at high-resolution by the PTZ camera. Several methods have already shown that this configuration
can be used for acquiring biometric data at a distance. Nevertheless, these methods
failed at providing effective solutions to the typical challenges of this strategy, restraining its
use in surveillance scenarios. Accordingly, this thesis proposes two methods to support the development
of a biometric data acquisition system based on the cooperation of a PTZ camera
with a typical surveillance camera. The first proposal is a camera calibration method capable
of accurately mapping the coordinates of the master camera to the pan/tilt angles of the PTZ
camera. The second proposal is a camera scheduling method for determining - in real-time -
the sequence of acquisitions that maximizes the number of different targets obtained, while
minimizing the cumulative transition time. In order to achieve the first goal of this thesis,
both methods were combined with state-of-the-art approaches of the human monitoring field
to develop a fully automated surveillance capable of acquiring biometric data at a distance and
without human cooperation, designated as QUIS-CAMPI system.
The QUIS-CAMPI system is the basis for pursuing the second goal of this thesis. The analysis
of the performance of the state-of-the-art biometric recognition approaches shows that these
approaches attain almost ideal recognition rates in unconstrained data. However, this performance
is incongruous with the recognition rates observed in surveillance scenarios. Taking into
account the drawbacks of current biometric datasets, this thesis introduces a novel dataset comprising
biometric samples (face images and gait videos) acquired by the QUIS-CAMPI system at a
distance ranging from 5 to 40 meters and without human intervention in the acquisition process.
This set allows to objectively assess the performance of state-of-the-art biometric recognition
methods in data that truly encompass the covariates of surveillance scenarios. As such, this set
was exploited for promoting the first international challenge on biometric recognition in the wild. This thesis describes the evaluation protocols adopted, along with the results obtained
by the nine methods specially designed for this competition. In addition, the data acquired by
the QUIS-CAMPI system were crucial for accomplishing the second goal of this thesis, i.e., the
development of methods robust to the covariates of surveillance scenarios. The first proposal
regards a method for detecting corrupted features in biometric signatures inferred by a redundancy
analysis algorithm. The second proposal is a caricature-based face recognition approach
capable of enhancing the recognition performance by automatically generating a caricature
from a 2D photo. The experimental evaluation of these methods shows that both approaches
contribute to improve the recognition performance in unconstrained data.A crescente preocupação com a segurança dos indivĂduos tem justificado o crescimento
do nĂşmero de câmaras de vĂdeo-vigilância instaladas tanto em espaços privados como pĂşblicos.
Contudo, ao contrário do que normalmente se pensa, estes dispositivos são, na maior parte dos
casos, usados apenas para gravação, não estando ligados a nenhum tipo de software inteligente
capaz de inferir em tempo real informações sobre os indivĂduos observados. Assim, apesar de a
vĂdeo-vigilância ter provado ser essencial na resolução de diversos crimes, o seu uso está ainda
confinado Ă disponibilização de vĂdeos que tĂŞm que ser manualmente inspecionados para extrair
informações relevantes dos sujeitos envolvidos no crime. Como tal, atualmente, o principal
desafio da comunidade cientĂfica Ă© o desenvolvimento de sistemas automatizados capazes de
monitorizar e identificar indivĂduos em ambientes de vĂdeo-vigilância.
Esta tese tem como principal objetivo estender a aplicabilidade dos sistemas de reconhecimento
biomĂ©trico aos ambientes de vĂdeo-vigilância. De forma mais especifica, pretende-se
1) conceber um sistema de vĂdeo-vigilância que consiga adquirir dados biomĂ©tricos a longas distâncias
(e.g., imagens da cara, Ăris, ou vĂdeos do tipo de passo) sem requerer a cooperação dos
indivĂduos no processo; e 2) desenvolver mĂ©todos de reconhecimento biomĂ©trico robustos aos
fatores de degradação inerentes aos dados adquiridos por este tipo de sistemas.
No que diz respeito ao primeiro objetivo, a análise aos dados adquiridos pelos sistemas tĂpicos
de vĂdeo-vigilância mostra que, devido Ă distância de captura, os traços biomĂ©tricos amostrados
não são suficientemente discriminativos para garantir taxas de reconhecimento aceitáveis.
Na literatura, vários trabalhos advogam o uso de câmaras Pan Tilt Zoom (PTZ) para adquirir
imagens de alta resolução à distância, principalmente o uso destes dispositivos no modo masterslave.
Na configuração master-slave um módulo de análise inteligente seleciona zonas de interesse
(e.g. carros, pessoas) a partir do vĂdeo adquirido por uma câmara de vĂdeo-vigilância
e a câmara PTZ é orientada para adquirir em alta resolução as regiões de interesse. Diversos
métodos já mostraram que esta configuração pode ser usada para adquirir dados biométricos
à distância, ainda assim estes não foram capazes de solucionar alguns problemas relacionados
com esta estratĂ©gia, impedindo assim o seu uso em ambientes de vĂdeo-vigilância. Deste modo,
esta tese propõe dois métodos para permitir a aquisição de dados biométricos em ambientes de
vĂdeo-vigilância usando uma câmara PTZ assistida por uma câmara tĂpica de vĂdeo-vigilância. O
primeiro é um método de calibração capaz de mapear de forma exata as coordenadas da câmara
master para o ângulo da câmara PTZ (slave) sem o auxĂlio de outros dispositivos Ăłticos. O
segundo método determina a ordem pela qual um conjunto de sujeitos vai ser observado pela
câmara PTZ. O método proposto consegue determinar em tempo-real a sequência de observações
que maximiza o nĂşmero de diferentes sujeitos observados e simultaneamente minimiza o
tempo total de transição entre sujeitos. De modo a atingir o primeiro objetivo desta tese, os
dois métodos propostos foram combinados com os avanços alcançados na área da monitorização
de humanos para assim desenvolver o primeiro sistema de vĂdeo-vigilância completamente automatizado
e capaz de adquirir dados biométricos a longas distâncias sem requerer a cooperação
dos indivĂduos no processo, designado por sistema QUIS-CAMPI.
O sistema QUIS-CAMPI representa o ponto de partida para iniciar a investigação relacionada
com o segundo objetivo desta tese. A análise do desempenho dos métodos de reconhecimento
biométrico do estado-da-arte mostra que estes conseguem obter taxas de reconhecimento
quase perfeitas em dados adquiridos sem restrições (e.g., taxas de reconhecimento
maiores do que 99% no conjunto de dados LFW). Contudo, este desempenho nĂŁo Ă© corroborado pelos resultados observados em ambientes de vĂdeo-vigilância, o que sugere que os conjuntos
de dados atuais nĂŁo contĂŞm verdadeiramente os fatores de degradação tĂpicos dos ambientes de
vĂdeo-vigilância. Tendo em conta as vulnerabilidades dos conjuntos de dados biomĂ©tricos atuais,
esta tese introduz um novo conjunto de dados biomĂ©tricos (imagens da face e vĂdeos do tipo de
passo) adquiridos pelo sistema QUIS-CAMPI a uma distância máxima de 40m e sem a cooperação
dos sujeitos no processo de aquisição. Este conjunto permite avaliar de forma objetiva o desempenho
dos mĂ©todos do estado-da-arte no reconhecimento de indivĂduos em imagens/vĂdeos
capturados num ambiente real de vĂdeo-vigilância. Como tal, este conjunto foi utilizado para
promover a primeira competição de reconhecimento biométrico em ambientes não controlados.
Esta tese descreve os protocolos de avaliação usados, assim como os resultados obtidos por 9
métodos especialmente desenhados para esta competição. Para além disso, os dados adquiridos
pelo sistema QUIS-CAMPI foram essenciais para o desenvolvimento de dois métodos para
aumentar a robustez aos fatores de degradação observados em ambientes de vĂdeo-vigilância. O
primeiro Ă© um mĂ©todo para detetar caracterĂsticas corruptas em assinaturas biomĂ©tricas atravĂ©s
da análise da redundância entre subconjuntos de caracterĂsticas. O segundo Ă© um mĂ©todo de
reconhecimento facial baseado em caricaturas automaticamente geradas a partir de uma Ăşnica
foto do sujeito. As experiências realizadas mostram que ambos os métodos conseguem reduzir
as taxas de erro em dados adquiridos de forma nĂŁo controlada
3D shape matching and registration : a probabilistic perspective
Dense correspondence is a key area in computer vision and medical image analysis. It has applications in registration and shape analysis. In this thesis, we develop a technique to recover dense correspondences between the surfaces of neuroanatomical objects over heterogeneous populations of individuals. We recover dense correspondences based on 3D shape matching. In this thesis, the 3D shape matching problem is formulated under the framework of Markov Random Fields (MRFs). We represent the surfaces of neuroanatomical objects as genus zero voxel-based meshes. The surface meshes are projected into a Markov random field space. The projection carries both geometric and topological information in terms of Gaussian curvature and mesh neighbourhood from the original space to the random field space. Gaussian curvature is projected to the nodes of the MRF, and the mesh neighbourhood structure is projected to the edges. 3D shape matching between two surface meshes is then performed by solving an energy function minimisation problem formulated with MRFs. The outcome of the 3D shape matching is dense point-to-point correspondences. However, the minimisation of the energy function is NP hard. In this thesis, we use belief propagation to perform the probabilistic inference for 3D shape matching. A sparse update loopy belief propagation algorithm adapted to the 3D shape matching is proposed to obtain an approximate global solution for the 3D shape matching problem. The sparse update loopy belief propagation algorithm demonstrates significant efficiency gain compared to standard belief propagation. The computational complexity and convergence property analysis for the sparse update loopy belief propagation algorithm are also conducted in the thesis. We also investigate randomised algorithms to minimise the energy function. In order to enhance the shape matching rate and increase the inlier support set, we propose a novel clamping technique. The clamping technique is realized by combining the loopy belief propagation message updating rule with the feedback from 3D rigid body registration. By using this clamping technique, the correct shape matching rate is increased significantly. Finally, we investigate 3D shape registration techniques based on the 3D shape matching result. Based on the point-to-point dense correspondences obtained from the 3D shape matching, a three-point based transformation estimation technique is combined with the RANdom SAmple Consensus (RANSAC) algorithm to obtain the inlier support set. The global registration approach is purely dependent on point-wise correspondences between two meshed surfaces. It has the advantage that the need for orientation initialisation is eliminated and that all shapes of spherical topology. The comparison of our MRF based 3D registration approach with a state-of-the-art registration algorithm, the first order ellipsoid template, is conducted in the experiments. These show dense correspondence for pairs of hippocampi from two different data sets, each of around 20 60+ year old healthy individuals
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