Biomechanical risk factors and reduced bone health in lower limb amputees

Bone constantly adapts to its surroundings through the formation and resorption of material, controlled by bone modelling and remodelling. Strains produced by mechanical loading are one factor that drive these processes and thus determine bone health. Lower limb amputees (LLA) adopt an asymmetrical movement pattern to compensate for the loss of a limb, resulting in a change in mechanical loading and subsequently a degradation in bone health. The aetiology of the majority of amputations is vascular diseases, which affect bone health. Therefore, it is not clear whether the asymmetrical loading, or comorbidities cause the degradation in bone health in LLA. Finite element models (FEM) are used to generate strain plots and predict the bone's response to mechanical loading. To understand the relationship between the degradation in bone health and asymmetrical loads in LLAs the asymmetrical loads can be applied to a healthy bone using FEMs, or simulated within a healthy population using restrictive devices. Therefore, the overall aim was to investigate the relationship between asymmetrical loading, as observed in LLA’s, and bone health, through the use of semi-subject specific FEMs and restrictive lower limb devices. Study one established a novel image processing method to convert peripheral quantative computed tomography (pQCT) scan images into binary and segment the tibia. The outer perimeter of the tibia was identified and sectioned to produce landmarks. The outer geometry landmarks were used to morph a base FEM, constructed from open source scan images to create semi-subject tibia FEM. Study two applied subject-specific joint reaction and muscle forces to the semi-subject tibia FEM. The strain plots output from Study two were validated against longitudinal geometrical changes from Study three. Study three, used 3D motion capture, pQCT and dual energy x-ray absorptiometry (DXA) to investigate gait and tibial geometry within a lower limb amputee and able-bodied population across twelve months. The coefficient of variation (CV) for able bodied subjects was less than 10% for ground reaction force (GRF) in level walking and less than 4% for bone total area. Study four, used a rigid foot orthosis and a trans-femoral prosthesis, to restrict able-bodied gait. Results showed participants walked significantly slower (p<0.01) in the restricted conditions, with a longer non-restricted step length (p<0.001). The loading rate and maximum GRF were higher in the non-restricted limb (p<0.05). Larger knee adductor moments were shown in the un-restricted leg in the trans-tibial condition (p<0.05). This thesis presents a novel method of constructing semi-subject specific FEMs from pQCT scans. This can be used to further investigate the link between asymmetrical loading and bone health in LLA's and other populations with asymmetrical gait. The use of restrictive devices allow investigation into LLA's specifically, without the interference of prosthetic variability, or comorbidities

Active modelling of virtual humans

This thesis provides a complete framework that enables the creation of photorealistic 3D human models in real-world environments. The approach allows a non-expert user to use any digital capture device to obtain four images of an individual and create a personalised 3D model, for multimedia applications. To achieve this, it is necessary that the system is automatic and that the reconstruction process is flexible to account for information that is not available or incorrectly captured. In this approach the individual is automatically extracted from the environment using constrained active B-spline templates that are scaled and automatically initialised using only image information. These templates incorporate the energy minimising framework for Active Contour Models, providing a suitable and flexible method to deal with the adjustments in pose an individual can adopt. The final states of the templates describe the individual’s shape. The contours in each view are combined to form a 3D B-spline surface that characterises an individual’s maximal silhouette equivalent. The surface provides a mould that contains sufficient information to allow for the active deformation of an underlying generic human model. This modelling approach is performed using a novel technique that evolves active-meshes to 3D for deforming the underlying human model, while adaptively constraining it to preserve its existing structure. The active-mesh approach incorporates internal constraints that maintain the structural relationship of the vertices of the human model, while external forces deform the model congruous to the 3D surface mould. The strength of the internal constraints can be reduced to allow the model to adopt the exact shape of the bounding volume or strengthened to preserve the internal structure, particularly in areas of high detail. This novel implementation provides a uniform framework that can be simply and automatically applied to the entire human model

Vessel identification in diabetic retinopathy

Diabetic retinopathy is the single largest cause of sight loss and blindness in 18 to 65 year olds. Screening programs for the estimated one to six per- cent of the diabetic population have been demonstrated to be cost and sight saving, howeverthere are insufficient screening resources. Automatic screen-ing systems may help solve this resource short fall. This thesis reports on research into an aspect of automatic grading of diabetic retinopathy; namely the identification of the retinal blood vessels in fundus photographs. It de-velops two vessels segmentation strategies and assess their accuracies. A literature review of retinal vascular segmentation found few results, and indicated a need for further development. The two methods for vessel segmentation were investigated in this thesis are based on mathematical morphology and neural networks. Both methodologies are verified on independently labeled data from two institutions and results are presented that characterisethe trade off betweenthe ability to identify vesseland non-vessels data. These results are based on thirty five images with their retinal vessels labeled. Of these images over half had significant pathology and or image acquisition artifacts. The morphological segmentation used ten images from one dataset for development. The remaining images of this dataset and the entire set of 20 images from the seconddataset were then used to prospectively verify generaliastion. For the neural approach, the imageswere pooled and 26 randomly chosenimageswere usedin training whilst 9 were reserved for prospective validation. Assuming equal importance, or cost, for vessel and non-vessel classifications, the following results were obtained; using mathematical morphology 84% correct classification of vascular and non-vascular pixels was obtained in the first dataset. This increased to 89% correct for the second dataset. Using the pooled data the neural approach achieved 88% correct identification accuracy. The spread of accuracies observed varied. It was highest in the small initial dataset with 16 and 10 percent standard deviation in vascular and non-vascular cases respectively. The lowest variability was observed in the neural classification, with a standard deviation of 5% for both accuracies. The less tangible outcomes of the research raises the issueof the selection and subsequent distribution of the patterns for neural network training. Unfortunately this indication would require further labeling of precisely those cases that were felt to be the most difficult. I.e. the small vessels and border conditions between pathology and the retina. The more concrete, evidence based conclusions,characterise both the neural and the morphological methods over a range of operating points. Many of these operating points are comparable to the few results presented in the literature. The advantage of the author's approach lies in the neural method's consistent as well as accurate vascular classification

Digital Image Processing via Combination of Low-Level and High-Level Approaches.

With the growth of computer power, Digital Image Processing plays a more and more important role in the modern world, including the field of industry, medical, communications, spaceflight technology etc. There is no clear definition how to divide the digital image processing, but normally, digital image processing includes three main steps: low-level, mid-level and highlevel processing. Low-level processing involves primitive operations, such as: image preprocessing to reduce the noise, contrast enhancement, and image sharpening. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. Finally, higher-level processing involves "making sense" of an ensemble of recognised objects, as in image analysis. Based on the theory just described in the last paragraph, this thesis is organised in three parts: Colour Edge and Face Detection; Hand motion detection; Hand Gesture Detection and Medical Image Processing. II In Colour Edge Detection, two new images G-image and R-image are built through colour space transform, after that, the two edges extracted from G-image and R-image respectively are combined to obtain the final new edge. In Face Detection, a skin model is built first, then the boundary condition of this skin model can be extracted to cover almost all of the skin pixels. After skin detection, the knowledge about size, size ratio, locations of ears and mouth is used to recognise the face in the skin regions. In Hand Motion Detection, frame differe is compared with an automatically chosen threshold in order to identify the moving object. For some special situations, with slow or smooth object motion, the background modelling and frame differencing are combined in order to improve the performance. In Hand Gesture Recognition, 3 features of every testing image are input to Gaussian Mixture Model (GMM), and then the Expectation Maximization algorithm (EM)is used to compare the GMM from testing images and GMM from training images in order to classify the results. In Medical Image Processing (mammograms), the Artificial Neural Network (ANN) and clustering rule are applied to choose the feature. Two classifier, ANN and Support Vector Machine (SVM), have been applied to classify the results, in this processing, the balance learning theory and optimized decision has been developed are applied to improve the performance

Construction of a duck whole genome radiation hybrid panel : an aid for NGS whole genome assembly and a contribution to avian comparative maps

Le canard est une espèce d'importance agronomique en France, principalement à travers l'industrie de foie gras, qui représente plus de 75% de la production mondiale. De plus, c'est aussi un modèle important pour l'étude de l'infection par le virus influenza, pour lequel les oiseaux aquatiques sont un réservoir naturel, car porteurs asymptomatiques. Les travaux réalisés lors de la thèse se situent dans le contexte international de l'étude du génome du canard, comportant la séquence du génome, le séquençage d'EST et l'identification et la cartographie de SNP. Le but à terme pour l'INRA étant de disposer des connaissances sur le génome nécessaires pour la cartographie fine de QTL et l'identification de gènes impliqués dans l'expression de caractères agronomiques. Un panel de 90 d'hybrides irradiés (panel RH) a été réalisé par fusion de cellules donneuses de canard irradiées avec des cellules receveuses de hamster. Afin d'éviter la culture à grande échelle des clones cellulaires, des méthodes de génotypage par PCR utilisant l'amplification complète du génome (WGA) et/ou la réduction des volumes réactionnels ont été testées et deux premières cartes de chromosomes ont ainsi été réalisées. Nous avons également utilisé le génotypage par PCR pour vérifier la qualité de l'assemblage des scaffolds du génome du canard, réalisés par séquençage nouvelle génération Illumina au Beijing Genome Institute (BGI, Chine). Finalement, afin de couvrir le génome complet, nous avons entrepris un séquençage léger (0,1X de profondeur) d'hybrides, permettant une réalisation de cartes plus rapides que par PCR. Ces cartes permettent la détection des réarrangements chromosomiques existant entre les génomes de la poule et du canard, qui sont distants de 80 millions d'années.Duck is a very important agronomic species in France, especially for fatty liver industry which presents 75% worldwide production. Moreover, duck is also a scientific model for avian influenza research as it is a natural reservoir for avian influenza viruses. The work presented here is part of the international collaboration on duck genome sequencing, including SNP detection and mapping, EST sequencing. Our goal is to provide a genome map allowing for fine mapping QTL and identifying candidate genes involved in expression of agronomic traits. A panel composed of 90 radiation hybrids was produced by fusing irradiated duck donor cells with hamster cells. To avoid large-scale culture of the clones, PCR genotyping involving Whole Genome Amplification (WGA) and/or reduction of reaction volumes were tested and two first maps for duck chromosomes were made. We also used the PCR genotyping method to test for the quality of duck sequence scaffold assemblies, which had been produced by the Beijing Genome Institute (BGI, China). Finally, to cover the whole genome, we performed a low-pass sequencing (0.1X depth) of hybrids, allowing for rapid map development. These maps allow the detection of chromosomal rearrangements that have taken place between the duck and chicken genomes, which have diverged 80 million years ago

Vessel identification in diabetic retinopathy

Diabetic retinopathy is the single largest cause of sight loss and blindness in 18 to 65 year olds. Screening programs for the estimated one to six per- cent of the diabetic population have been demonstrated to be cost and sight saving, howeverthere are insufficient screening resources. Automatic screen-ing systems may help solve this resource short fall. This thesis reports on research into an aspect of automatic grading of diabetic retinopathy; namely the identification of the retinal blood vessels in fundus photographs. It de-velops two vessels segmentation strategies and assess their accuracies. A literature review of retinal vascular segmentation found few results, and indicated a need for further development. The two methods for vessel segmentation were investigated in this thesis are based on mathematical morphology and neural networks. Both methodologies are verified on independently labeled data from two institutions and results are presented that characterisethe trade off betweenthe ability to identify vesseland non-vessels data. These results are based on thirty five images with their retinal vessels labeled. Of these images over half had significant pathology and or image acquisition artifacts. The morphological segmentation used ten images from one dataset for development. The remaining images of this dataset and the entire set of 20 images from the seconddataset were then used to prospectively verify generaliastion. For the neural approach, the imageswere pooled and 26 randomly chosenimageswere usedin training whilst 9 were reserved for prospective validation. Assuming equal importance, or cost, for vessel and non-vessel classifications, the following results were obtained; using mathematical morphology 84% correct classification of vascular and non-vascular pixels was obtained in the first dataset. This increased to 89% correct for the second dataset. Using the pooled data the neural approach achieved 88% correct identification accuracy. The spread of accuracies observed varied. It was highest in the small initial dataset with 16 and 10 percent standard deviation in vascular and non-vascular cases respectively. The lowest variability was observed in the neural classification, with a standard deviation of 5% for both accuracies. The less tangible outcomes of the research raises the issueof the selection and subsequent distribution of the patterns for neural network training. Unfortunately this indication would require further labeling of precisely those cases that were felt to be the most difficult. I.e. the small vessels and border conditions between pathology and the retina. The more concrete, evidence based conclusions,characterise both the neural and the morphological methods over a range of operating points. Many of these operating points are comparable to the few results presented in the literature. The advantage of the author's approach lies in the neural method's consistent as well as accurate vascular classification.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

MPEG-4 content creation: integration of MPEG-4 content creation tools into an existing animation tool

This thesis provides a complete framework that enables the creation of photorealistic 3D human models in real-world environments. The approach allows a non-expert user to use any digital capture device to obtain four images of an individual and create a personalised 3D model, for multimedia applications. To achieve this, it is necessary that the system is automatic and that the reconstruction process is flexible to account for information that is not available or incorrectly captured. In this approach the individual is automatically extracted from the environment using constrained active B-spline templates that are scaled and automatically initialised using only image information. These templates incorporate the energy minimising framework for Active Contour Models, providing a suitable and flexible method to deal with the adjustments in pose an individual can adopt. The final states o f the templates describe the individual’s shape. The contours in each view are combined to form a 3D B-spline surface that characterises an individual’s maximal silhouette equivalent. The surface provides a mould that contains sufficient information to allow for the active deformation of an underlying generic human model. This modelling approach is performed using a novel technique that evolves active-meshes to 3D for deforming the underlying human model, while adaptively constraining it to preserve its existing structure. The active-mesh approach incorporates internal constraints that maintain the structural relationship of the vertices of the human model, while external forces deform the model congruous to the 3D surface mould. The strength of the internal constraints can be reduced to allow the model to adopt the exact shape o f the bounding volume or strengthened to preserve the internal structure, particularly in areas of high detail. This novel implementation provides a uniform framework that can be simply and automatically applied to the entire human model