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Articular human joint modelling
Copyright @ Cambridge University Press 2009.The work reported in this paper encapsulates the theories and algorithms developed to drive the core analysis modules of the software which has been developed to model a musculoskeletal structure of anatomic joints. Due to local bone surface and contact geometry based joint kinematics, newly developed algorithms make the proposed modeller different from currently available modellers. There are many modellers that are capable of modelling gross human body motion. Nevertheless, none of the available modellers offer complete elements of joint modelling. It appears that joint modelling is an extension of their core analysis capability, which, in every case, appears to be musculoskeletal motion dynamics. It is felt that an analysis framework that is focused on human joints would have significant benefit and potential to be used in many orthopaedic applications. The local mobility of joints has a significant influence in human motion analysis, in understanding of joint loading, tissue behaviour and contact forces. However, in order to develop a bone surface based joint modeller, there are a number of major problems, from tissue idealizations to surface geometry discretization and non-linear motion analysis. This paper presents the following: (a) The physical deformation of biological tissues as linear or non-linear viscoelastic deformation, based on spring-dashpot elements. (b) The linear dynamic multibody modelling, where the linear formulation is established for small motions and is particularly useful for calculating the equilibrium position of the joint. This model can also be used for finding small motion behaviour or loading under static conditions. It also has the potential of quantifying the joint laxity. (c) The non-linear dynamic multibody modelling, where a non-matrix and algorithmic formulation is presented. The approach allows handling complex material and geometrical nonlinearity easily. (d) Shortest path algorithms for calculating soft tissue line of action geometries. The developed algorithms are based on calculating minimum âsurface massâ and âsurface covarianceâ. An improved version of the âsurface covarianceâ algorithm is described as âresidual covarianceâ. The resulting path is used to establish the direction of forces and moments acting on joints. This information is needed for linear or non-linear treatment of the joint motion. (e) The final contribution of the paper is the treatment of the collision. In the virtual world, the difficulty in analysing bodies in motion arises due to body interpenetrations. The collision algorithm proposed in the paper involves finding the shortest projected ray from one body to the other. The projection of the body is determined by the resultant forces acting on it due to soft tissue connections under tension. This enables the calculation of collision condition of non-convex objects accurately. After the initial collision detection, the analysis involves attaching special springs (stiffness only normal to the surfaces) at the âpotentially colliding pointsâ and motion of bodies is recalculated. The collision algorithm incorporates the rotation as well as translation. The algorithm continues until the joint equilibrium is achieved. Finally, the results obtained based on the software are compared with experimental results obtained using cadaveric joints
Joint Object and Part Segmentation using Deep Learned Potentials
Segmenting semantic objects from images and parsing them into their
respective semantic parts are fundamental steps towards detailed object
understanding in computer vision. In this paper, we propose a joint solution
that tackles semantic object and part segmentation simultaneously, in which
higher object-level context is provided to guide part segmentation, and more
detailed part-level localization is utilized to refine object segmentation.
Specifically, we first introduce the concept of semantic compositional parts
(SCP) in which similar semantic parts are grouped and shared among different
objects. A two-channel fully convolutional network (FCN) is then trained to
provide the SCP and object potentials at each pixel. At the same time, a
compact set of segments can also be obtained from the SCP predictions of the
network. Given the potentials and the generated segments, in order to explore
long-range context, we finally construct an efficient fully connected
conditional random field (FCRF) to jointly predict the final object and part
labels. Extensive evaluation on three different datasets shows that our
approach can mutually enhance the performance of object and part segmentation,
and outperforms the current state-of-the-art on both tasks
A Benchmark and Evaluation of Non-Rigid Structure from Motion
Non-Rigid structure from motion (NRSfM), is a long standing and central
problem in computer vision, allowing us to obtain 3D information from multiple
images when the scene is dynamic. A main issue regarding the further
development of this important computer vision topic, is the lack of high
quality data sets. We here address this issue by presenting of data set
compiled for this purpose, which is made publicly available, and considerably
larger than previous state of the art. To validate the applicability of this
data set, and provide and investigation into the state of the art of NRSfM,
including potential directions forward, we here present a benchmark and a
scrupulous evaluation using this data set. This benchmark evaluates 16
different methods with available code, which we argue reasonably spans the
state of the art in NRSfM. We also hope, that the presented and public data set
and evaluation, will provide benchmark tools for further development in this
field
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A novel musculoskeletal joint modelling for orthopaedic applications
This thesis was submitted for the degree of Docter of Philosophy and awarded by Brunel University.The objective of the work carried out in this thesis was to develop analytical and
computational tools to model and investigate musculoskeletal human joints. It was
recognised that the FEA was used by many researchers in modelling human
musculoskeletal motion, loading and stresses. However the continuum mechanics
played only a minor role in determining the articular joint motion, and its value was
questionable. This is firstly due to the computational cost and secondly due to its
impracticality for this application. On the other hand, there isnât any suitable software
for precise articular joint motion analysis to deal with the local joint stresses or non
standard joints. The main requirement in orthopaedics field is to develop a modeller
software (and its associated theories) to model anatomic joint as it is, without any
simplification with respect to joint surface morphology and material properties of
surrounding tissues. So that the proposed modeller can be used for evaluating and
diagnosing different joint abnormalities but furthermore form the basis for performing
implant insertion and analysis of the artificial joints. The work which is presented in this thesis is a new frame work and has been developed for human anatomic joint analysis which describes the joint in terms of its surface geometry and surrounding
musculoskeletal tissues. In achieving such a framework several contributions were
made to the 6DOF linear and nonlinear joint modelling, the mathematical definition of
joint stiffness, tissue path finding and wrapping and the contact with collision analysis. In 6DOF linear joint modelling, the contribution is the development of joint stiffness and damping matrices. This modelling approach is suitable for the linear range of tissue stiffness and damping properties. This is the first of its kind and it gives a firm analytical basis for investigating joints with surrounding tissue and the cartilage. The 6DOF nonlinear joint modelling is a new scheme which is described for modelling the motion of multi bodies joined by non-linear stiffness and contact elements. The proposed method requires no matrix assembly for the stiffness and damping elements or mass elements. The novelty in the nonlinear modelling, relates to the overall algorithmic approach and handling local non-linearity by procedural means. The mathematical definition of joint stiffness is also a new proposal which is based on the mathematical definition of stiffness between two bodies. Based on the joint stiffness matrix properties, number of joint stiffness invariants was obtained analytically such as the centre of stiffness, the principal translational stiffnesses, and the principal rotational stiffnesses. In corresponding to these principal stiffnesses, their principal axes have been also obtained. Altogether, a joint is assessed by six principal axes and six principal stiffnesses and its centre of stiffness. These formulations are new and show that a joint can be described in terms of inherent stiffness properties. It is expected that these will be better in characterising a joint in comparison to laxity based characterisation. The
development of tissue path finding and wrapping algorithms are also introduced as new approaches. The musculoskeletal tissue wrapping involves calculating the shortest
distance between two points on a meshed surface. A new heuristic algorithm was
proposed. The heuristic is based on minimising the accumulative divergence from the straight line between two points on the surface and the direction of travel on the surface (i.e. bone). In contact and collision based development, the novel algorithm has been proposed that detects possible colliding points on the motion trajectory by redefining the distance as a two dimensional measure along the velocity approach vector and perpendicular to this vector. The perpendicular distance determines if there are potentially colliding points, and the distance along the velocity determines how close they are. The closest pair among the potentially colliding points gives the âtime to collisionâ. The algorithm can eliminate the âfly passâ situation where very close points may not collide because of the direction of their relative velocity. All these developed
algorithms and modelling theories, have been encompassed in the developed prototype
software in order to simulate the anatomic joint articulations through modelling
formulations developed. The software platform provides a capability for analysing joints as 6DOF joints based on anatomic joint surfaces. The software is highly interactive and driven by well structured database, designed to be highly flexible for the future developments. Particularly, two case studies are carried out in this thesis in order to generate results relating to all the proposed elements of the study. The results obtained from the case studies show good agreement with previously published results or model based results obtained from Lifemod software, whenever comparison was possible. In some cases the comparison was not possible because there were no equivalent results; the results were supported by other indicators. The modelling based results were also supported by experiments performed in the Brunel Orthopaedic Research and Learning
Centre
Advances in Monocular Exemplar-based Human Body Pose Analysis: Modeling, Detection and Tracking
Esta tesis contribuye en el anĂĄlisis de la postura del cuerpo humano a partir de secuencias de imĂĄgenes adquiridas con una sola cĂĄmara. Esta temĂĄtica presenta un amplio rango de potenciales aplicaciones en video-vigilancia, video-juegos o aplicaciones biomĂŠdicas. Las tĂŠcnicas basadas en patrones han tenido ĂŠxito, sin embargo, su precisiĂłn depende de la similitud del punto de vista de la cĂĄmara y de las propiedades de la escena entre las imĂĄgenes de entrenamiento y las de prueba. Teniendo en cuenta un conjunto de datos de entrenamiento capturado mediante un nĂşmero reducido de cĂĄmaras fijas, paralelas al suelo, se han identificado y analizado tres escenarios posibles con creciente nivel de dificultad: 1) una cĂĄmara estĂĄtica paralela al suelo, 2) una cĂĄmara de vigilancia fija con un ĂĄngulo de visiĂłn considerablemente diferente, y 3) una secuencia de video capturada con una cĂĄmara en movimiento o simplemente una sola imagen estĂĄtica
Towards a framework for multi class statistical modelling of shape, intensity, and kinematics in medical images
Statistical modelling has become a ubiquitous tool for analysing of morphological variation of bone structures in medical images. For radiological images, the shape, relative pose between the bone structures and the intensity distribution are key features often modelled separately. A wide range of research has reported methods that incorporate these features as priors for machine learning purposes. Statistical shape, appearance (intensity profile in images) and pose models are popular priors to explain variability across a sample population of rigid structures. However, a principled and robust way to combine shape, pose and intensity features has been elusive for four main reasons: 1) heterogeneity of the data (data with linear and non-linear natural variation across features); 2) sub-optimal representation of three-dimensional Euclidean motion; 3) artificial discretization of the models; and 4) lack of an efficient transfer learning process to project observations into the latent space. This work proposes a novel statistical modelling framework for multiple bone structures. The framework provides a latent space embedding shape, pose and intensity in a continuous domain allowing for new approaches to skeletal joint analysis from medical images. First, a robust registration method for multi-volumetric shapes is described. Both sampling and parametric based registration algorithms are proposed, which allow the establishment of dense correspondence across volumetric shapes (such as tetrahedral meshes) while preserving the spatial relationship between them. Next, the framework for developing statistical shape-kinematics models from in-correspondence multi-volumetric shapes embedding image intensity distribution, is presented. The framework incorporates principal geodesic analysis and a non-linear metric for modelling the spatial orientation of the structures. More importantly, as all the features are in a joint statistical space and in a continuous domain; this permits on-demand marginalisation to a region or feature of interest without training separate models. Thereafter, an automated prediction of the structures in images is facilitated by a model-fitting method leveraging the models as priors in a Markov chain Monte Carlo approach. The framework is validated using controlled experimental data and the results demonstrate superior performance in comparison with state-of-the-art methods. Finally, the application of the framework for analysing computed tomography images is presented. The analyses include estimation of shape, kinematic and intensity profiles of bone structures in the shoulder and hip joints. For both these datasets, the framework is demonstrated for segmentation, registration and reconstruction, including the recovery of patient-specific intensity profile. The presented framework realises a new paradigm in modelling multi-object shape structures, allowing for probabilistic modelling of not only shape, but also relative pose and intensity as well as the correlations that exist between them. Future work will aim to optimise the framework for clinical use in medical image analysis
Social-ecological analysis of climate induced changes in biodiversity â outline of a research concept
The interactions of changes in climate and biodiversity with societal actions, structures and processes are a priority topic within the international scientific debate â and thus, a relevant subject matter for BiKFâs work. This paper outlines a concept for transdisciplinary research within BiKF. It focuses on the analysis of social-ecological systems supporting society with biodiversity driven ecosystem services. Such research is considering different issues: defining sustainable societal adaptations to climate induced biodiversity changes; permitting adequate understanding of the social-ecological reproduction of ecosystem functions, including their conservation and restoration; analysing the societal values and socio-economic utilisation of ecosystem services. Gaining knowledge in these areas provides an improved basis for decision-making in biodiversity and resource management
Deformable and articulated 3D reconstruction from monocular video sequences
PhDThis thesis addresses the problem of deformable and articulated structure from motion from
monocular uncalibrated video sequences. Structure from motion is defined as the problem of
recovering information about the 3D structure of scenes imaged by a camera in a video sequence.
Our study aims at the challenging problem of non-rigid shapes (e.g. a beating heart or a smiling
face). Non-rigid structures appear constantly in our everyday life, think of a bicep curling, a
torso twisting or a smiling face. Our research seeks a general method to perform 3D shape
recovery purely from data, without having to rely on a pre-computed model or training data.
Open problems in the field are the difficulty of the non-linear estimation, the lack of a real-time
system, large amounts of missing data in real-world video sequences, measurement noise and
strong deformations. Solving these problems would take us far beyond the current state of the
art in non-rigid structure from motion. This dissertation presents our contributions in the field
of non-rigid structure from motion, detailing a novel algorithm that enforces the exact metric
structure of the problem at each step of the minimisation by projecting the motion matrices
onto the correct deformable or articulated metric motion manifolds respectively. An important
advantage of this new algorithm is its ability to handle missing data which becomes crucial
when dealing with real video sequences. We present a generic bilinear estimation framework,
which improves convergence and makes use of the manifold constraints. Finally, we demonstrate
a sequential, frame-by-frame estimation algorithm, which provides a 3D model and camera
parameters for each video frame, while simultaneously building a model of object deformation
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