3,364 research outputs found
Estimating and understanding motion : from diagnostic to robotic surgery
Estimating and understanding motion from an image sequence is a central topic in computer vision. The high interest in this topic is because we are living in a world where many events that occur in the environment are dynamic. This makes motion estimation and understanding a natural component and a key factor in a widespread of applications including object recognition , 3D shape reconstruction, autonomous navigation and medica! diagnosis.
Particularly, we focus on the medical domain in which understanding the human body for clinical purposes requires retrieving the organs' complex motion patterns, which is in general a hard problem when using only image data. In this thesis, we cope with this problem by posing the question - How to achieve a realistic motion estimation to offer a better clinical understanding? We focus this thesis on answering this question by using a variational formulation as a basis to understand one of the most complex motions in the human's body, the heart motion, through three different applications: (i) cardiac motion estimation for diagnostic, (ii) force estimation and (iii) motion prediction, both for robotic surgery.
Firstly, we focus on a central topic in cardiac imaging that is the estimation of the cardiac motion. The main aim is to offer objective and understandable measures to physicians for helping them in the diagnostic of cardiovascular diseases. We employ ultrafast ultrasound data and tools for imaging motion drawn from diverse areas such as low-rank analysis and variational deformation to perform a realistic cardiac motion estimation. The significance is that by taking low-rank data with carefully chosen penalization, synergies in this complex variational problem can be created. We demonstrate how our proposed solution deals with complex deformations through careful numerical experiments using realistic and simulated data.
We then move from diagnostic to robotic surgeries where surgeons perform delicate procedures remotely through robotic manipulators without directly interacting with the patients. As a result, they lack force feedback, which is an important primary sense for increasing surgeon-patient transparency and avoiding injuries and high mental workload. To solve this problem, we follow the conservation principies of continuum mechanics in which it is clear that the change in shape of an elastic object is directly proportional to the force applied. Thus, we create a variational framework to acquire the deformation that the tissues undergo due to an applied force. Then, this information is used in a learning system to find the nonlinear relationship between the given data and the applied force. We carried out experiments with in-vivo and ex-vivo data and combined statistical, graphical and perceptual analyses to demonstrate the strength of our solution.
Finally, we explore robotic cardiac surgery, which allows carrying out complex procedures including Off-Pump Coronary Artery Bypass Grafting (OPCABG). This procedure avoids the associated complications of using Cardiopulmonary Bypass (CPB) since the heart is not arrested while performing the surgery on a beating heart. Thus, surgeons have to deal with a dynamic target that compromisetheir dexterity and the surgery's precision. To compensate the heart motion, we propase a solution composed of three elements: an energy function to estimate the 3D heart motion, a specular highlight detection strategy and a prediction approach for increasing the robustness of the solution. We conduct evaluation of our solution using phantom and realistic datasets.
We conclude the thesis by reporting our findings on these three applications and highlight the dependency between motion estimation and motion understanding at any dynamic event, particularly in clinical scenarios.L’estimació i comprensió del moviment dins d’una seqüència d’imatges és un tema central en la visió per ordinador, el que genera un gran interès perquè vivim en un entorn ple d’esdeveniments dinà mics. Per aquest motiu és considerat com un component natural i factor clau dins d’un ampli ventall d’aplicacions, el qual inclou el reconeixement d’objectes, la reconstrucció de formes tridimensionals, la navegació autònoma i el diagnòstic de malalties.
En particular, ens situem en l’à mbit mèdic en el qual la comprensió del cos humà , amb finalitats clÃniques, requereix l’obtenció de patrons complexos de moviment dels òrgans. Aquesta és, en general, una tasca difÃcil quan s’utilitzen només dades de tipus visual. En aquesta tesi afrontem el problema plantejant-nos la pregunta - Com es pot aconseguir una estimació realista del moviment amb l’objectiu d’oferir una millor comprensió clÃnica? La tesi se centra en la resposta mitjançant l’ús d’una formulació variacional com a base per entendre un dels moviments més complexos del cos humà , el del cor, a través de tres aplicacions: (i) estimació del moviment cardÃac per al diagnòstic, (ii) estimació de forces i (iii) predicció del moviment, orientant-se les dues últimes en cirurgia robòtica.
En primer lloc, ens centrem en un tema principal en la imatge cardÃaca, que és l’estimació del moviment cardÃac. L’objectiu principal és oferir als metges mesures objectives i comprensibles per ajudar-los en el diagnòstic de les malalties cardiovasculars. Fem servir dades d’ultrasons ultrarà pids i eines per al moviment d’imatges procedents de diverses à rees, com ara l’anà lisi de baix rang i la deformació variacional, per fer una estimació realista del moviment cardÃac. La importà ncia rau en que, en prendre les dades de baix rang amb una penalització acurada, es poden crear sinergies en aquest problema variacional complex. Mitjançant acurats experiments numèrics, amb dades realÃstiques i simulades, hem demostrat com les nostres propostes solucionen deformacions complexes.
Després passem del diagnòstic a la cirurgia robòtica, on els cirurgians realitzen procediments delicats remotament, a través de manipuladors robòtics, sense interactuar directament amb els pacients. Com a conseqüència, no tenen la percepció de la força com a resposta, que és un sentit primari important per augmentar la transparència entre el cirurgià i el pacient, per evitar lesions i per
reduir la cà rrega de treball mental. Resolem aquest problema seguint els principis de conservació de la mecà nica del medi continu, en els quals està clar que el canvi en la forma d’un objecte elà stic és directament proporcional a la força aplicada. Per això hem creat un marc variacional que adquireix la deformació que pateixen els teixits per l’aplicació d’una força. Aquesta informació s’utilitza en un sistema d’aprenentatge, per trobar la relació no lineal entre les dades donades i la força aplicada. Hem dut a terme experiments amb dades in-vivo i ex-vivo i hem combinat l’anà lisi estadÃstic, grà fic i de percepció que demostren la robustesa de la nostra solució. Finalment, explorem la cirurgia cardÃaca robòtica, la qual cosa permet realitzar procediments complexos, incloent la cirurgia coronà ria sense bomba (off-pump coronary artery bypass grafting o OPCAB). Aquest procediment evita les complicacions associades a l’ús de circulació extracorpòria (Cardiopulmonary Bypass o CPB), ja que el cor no s’atura mentre es realitza la cirurgia. Això
comporta que els cirurgians han de tractar amb un objectiu dinà mic que compromet la seva destresa i la precisió de la cirurgia. Per compensar el moviment del cor, proposem una solució composta de tres elements: un funcional d’energia per estimar el moviment tridimensional del cor, una estratègia de detecció de les reflexions especulars i una aproximació basada en mètodes de predicció, per tal d’augmentar la robustesa de la solució. L’avaluació de la nostra solució s’ha dut
a terme mitjançant conjunts de dades sintètiques i realistes.
La tesi conclou informant dels nostres resultats en aquestes tres aplicacions i posant de relleu la dependència entre l’estimació i la comprensió del moviment en qualsevol esdeveniment dinà mic, especialment en escenaris clÃnics.Postprint (published version
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
Photorealistic retrieval of occluded facial information using a performance-driven face model
Facial occlusions can cause both human observers and computer algorithms
to fail in a variety of important tasks such as facial action analysis and
expression classification. This is because the missing information is not
reconstructed accurately enough for the purpose of the task in hand. Most
current computer methods that are used to tackle this problem implement
complex three-dimensional polygonal face models that are generally timeconsuming
to produce and unsuitable for photorealistic reconstruction of
missing facial features and behaviour.
In this thesis, an image-based approach is adopted to solve the occlusion
problem. A dynamic computer model of the face is used to retrieve the
occluded facial information from the driver faces. The model consists of a
set of orthogonal basis actions obtained by application of principal
component analysis (PCA) on image changes and motion fields extracted
from a sequence of natural facial motion (Cowe 2003). Examples of
occlusion affected facial behaviour can then be projected onto the model to
compute coefficients of the basis actions and thus produce photorealistic
performance-driven animations.
Visual inspection shows that the PCA face model recovers aspects of
expressions in those areas occluded in the driver sequence, but the expression is generally muted. To further investigate this finding, a database
of test sequences affected by a considerable set of artificial and natural
occlusions is created. A number of suitable metrics is developed to measure
the accuracy of the reconstructions. Regions of the face that are most
important for performance-driven mimicry and that seem to carry the best
information about global facial configurations are revealed using Bubbles,
thus in effect identifying facial areas that are most sensitive to occlusions.
Recovery of occluded facial information is enhanced by applying an
appropriate scaling factor to the respective coefficients of the basis actions
obtained by PCA. This method improves the reconstruction of the facial
actions emanating from the occluded areas of the face. However, due to the
fact that PCA produces bases that encode composite, correlated actions,
such an enhancement also tends to affect actions in non-occluded areas of
the face. To avoid this, more localised controls for facial actions are
produced using independent component analysis (ICA). Simple projection
of the data onto an ICA model is not viable due to the non-orthogonality of
the extracted bases. Thus occlusion-affected mimicry is first generated using
the PCA model and then enhanced by accordingly manipulating the
independent components that are subsequently extracted from the mimicry.
This combination of methods yields significant improvements and results in
photorealistic reconstructions of occluded facial actions
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