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We present a statistical image-based shape + structure model for Bayesian visual hull reconstruction and 3D structure inference. The 3D shape of a class of objects is represented by sets of contours from silhouette views simultaneously observed from multiple calibrated cameras. Bayesian reconstructions of new shapes are then estimated using a prior density constructed with a mixture model and probabilistic principal components analysis. We show how the use of a class-specific prior in a visual hull reconstruction can reduce the effect of segmentation errors from the silhouette extraction process. The proposed method is applied to a data set of pedestrian images, and improvements in the approximate 3D models under various noise conditions are shown. We further augment the shape model to incorporate structural features of interest; unknown structural parameters for a novel set of contours are then inferred via the Bayesian reconstruction process. Model matching and parameter inference are done entirely in the image domain and require no explicit 3D construction. Our shape model enables accurate estimation of structure despite segmentation errors or missing views in the input silhouettes, and works even with only a single input view. Using a data set of thousands of pedestrian images generated from a synthetic model, we can accurately infer the 3D locations of 19 joints on the body based on observed silhouette contours from real images.

Total Hip Replacement (THR) has become a common surgical procedure in recent years, due to the increase in the aging population with hip osteoarthritis. Identifying the proper orientation of the pelvis is a critical step in accurate placement of the femur prosthesis in the acetabulum in THR. The general approach to localize the orientation of the pelvic anatomical coordinate system (PaCS) is to use intra-operative X-ray fluoroscopy in a specialized interventional radiology facility to guide the procedure. Employing intra-operative ultrasound (US) imaging fused with pre-operative CT scan or fluoroscopy imaging was proposed to eliminate the ionizing radiation of intra-operative X-ray to the patient and the need for radiology facilities in the OR. However, the use of pre-operative imaging exposes patients to accumulative ionizing radiation which is desirable to be eliminated. In this thesis, I propose to replace pre-operative imaging with a statistical shape model (SSM) of the pelvis which is constructed from CT images of patients. An automatic deformable registration of a pelvis anatomical shape model to a sparse set of 2D ultrasound images of the pelvis is presented in order to localize the PaCS. In this registration technique, a set of 2D slices are extracted from the pelvic shape model, based on the approximate location and orientation of a corresponding 2D ultrasound image. The comparison of the shape model slices and ultrasound images is made possible by using an ultrasound simulation technique and a correlation-based similarity metric. During the registration, an instance of the shape model is generated that best matches the ultrasound data. I demonstrate the feasibility of our proposed approach in localizing the PaCS on four patient phantoms and on data from two male human cadavers. None of the test data sets were included in the SSM generation. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2010-08-23 17:57:24.708

Submitted by JÚLIO HEBER SILVA (julioheber@yahoo.com.br) on 2017-05-11T19:49:56Z No. of bitstreams: 2 Dissertação - Paulo Freire Sobrinho - 2017.pdf: 10088390 bytes, checksum: 93cbaefd8f5c2dc974201729c2e859f7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2017-05-15T11:30:14Z (GMT) No. of bitstreams: 2 Dissertação - Paulo Freire Sobrinho - 2017.pdf: 10088390 bytes, checksum: 93cbaefd8f5c2dc974201729c2e859f7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-05-15T11:30:14Z (GMT). No. of bitstreams: 2 Dissertação - Paulo Freire Sobrinho - 2017.pdf: 10088390 bytes, checksum: 93cbaefd8f5c2dc974201729c2e859f7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-04-13 / Contextualization: Technologies like CAD systems, become ideal as a second opinion, that is, to work in partnership with the doctor. For example, PneumoCAD can be used to perform a diagnosis of absence or absence of pneumonia in children 1 to <5 years of age using X-ray radiographs. Problems: However, the use of PneumoCAD requires a region of interest, referring to the lungs, manually. Based on difficulty and difficulty, we sought a solution that was not found in any research, applied to radiographs, involving PneumoCAD, through the active model, having as a patient children between the ages of 1 and <5 years. Proposal: It is then proposed the use of the active model, associated with the technique developed and called nsAlterar in improvement to segmentation based on ns . Materials and Methods: Fifty-six “padrão ouro” radiographs were submitted to MATLAB, in 8 steps, through modified and improved algorithms, as well as implemented support tools, such as: As well as measures of similarity to investigate quantitatively , On an efficiency of all resources employed for the same purpose. Results: With this question, we obtained, after an analysis of the experiments, a taxon of hits for the right of spraying 75.61% and for the left one in 63.41%, in which nsAlterar promoted the improvement in the distributions, even if They were not segmented correctly, through approximations properly. Conclusions: Based on the active model associated with nsAlterar and other resources, it was possible to complement a functionality of the PneumoCAD system, through the use of segmentation in reais, thus contributing to a higher efficiency and better results. / Contextualização: As tecnologias como sistemas CAD, tornam-se ideais como segunda opinião, ou seja, para trabalhar em parceria com o médico. Por exemplo, o PneumoCAD pode ser utilizado para realização do diagnóstico de ausência ou não de pneumonia em crianças de 1 e < 5 anos de idade, através das radiografias de raios-X. Problemática: Entretanto, a utilização do PneumoCAD exige que uma região de interesse, referente aos pulmões, sejam determinadas manualmente. Baseado nesta exigência e dificuldade buscou-se alguma solução que não foi encontrada em nenhuma pesquisa, aplicada a radiografias, envolvendo o PneumoCAD, através do Active Shape Model, tendo como paciente crianças com idade entre 1 e < 5 anos. Proposta: É, então, proposto o uso do Active Shape Model, associado à técnica desenvolvida e denominada nsAlterar em melhora à segmentação baseada no ns . Materiais e Métodos: Foram submetidas, no MATLAB, 56 amostras de radiografias do “padrão-ouro”, em 8 etapas, através de algoritmos modificados e aperfei- çoados, além de ferramentas implementadas de apoio, como: para o treinamento a partir de exemplos; assim como as medidas de similaridades para buscar investigar, de maneira quantitativa, sobre a eficiência de todos os recursos empregados para o mesmo propósito. Resultados: Com isto, foi obtida, após a análise dos experimentos, a taxa de acertos para o pulmão direito em 75,61% e para o esquerdo em 63,41%, em que o nsAlterar promoveu o melhoramento nas distribuições, mesmo as que não foram segmentadas corretamente, através de aproximações de maneira adequada. Conclusões: A partir do Active Shape Model associado ao nsAlterar e demais recursos, foi possível complementar a funcionalidade do sistema PneumoCAD, através do uso da segmentação em situações reais, contribuindo, assim, para a obtenção de maior eficiência e de melhores resultados.

This thesis presents an investigation into Gyrodactylus species recognition, making use of machine learning classification and feature selection techniques, and explores image feature extraction to demonstrate proof of concept for an envisaged rapid, consistent and secure initial identification of pathogens by field workers and non-expert users. The design of the proposed cognitively inspired framework is able to provide confident discrimination recognition from its non-pathogenic congeners, which is sought in order to assist diagnostics during periods of a suspected outbreak. Accurate identification of pathogens is a key to their control in an aquaculture context and the monogenean worm genus Gyrodactylus provides an ideal test-bed for the selected techniques. In the proposed algorithm, the concept of classification using a single model is extended to include more than one model. In classifying multiple species of Gyrodactylus, experiments using 557 specimens of nine different species, two classifiers and three feature sets were performed. To combine these models, an ensemble based majority voting approach has been adopted. Experimental results with a database of Gyrodactylus species show the superior performance of the ensemble system. Comparison with single classification approaches indicates that the proposed framework produces a marked improvement in classification performance. The second contribution of this thesis is the exploration of image processing techniques. Active Shape Model (ASM) and Complex Network methods are applied to images of the attachment hooks of several species of Gyrodactylus to classify each species according to their true species type. ASM is used to provide landmark points to segment the contour of the image, while the Complex Network model is used to extract the information from the contour of an image. The current system aims to confidently classify species, which is notifiable pathogen of Atlantic salmon, to their true class with high degree of accuracy. Finally, some concluding remarks are made along with proposal for future work.

Traditionally, uncertainties are handled by expanding the irradiated volume to ensure target dose coverage to a certain probability. The uncertainties arise from e.g. the uncertainty in positioning of the patient at every fraction, organ motion and in defining the region of interests on the acquired images. The applied margins are inherently population based and do not exploit the geometry of the individual patient. Probabilistic planning on the other hand incorporates the uncertainties directly into the treatment optimization and therefore has more degrees of freedom to tailor the dose distribution to the individual patient. The aim of this thesis is to create a framework for probabilistic evaluation and optimization based on the concept of dose coverage probabilities. Several computational challenges for this purpose are addressed in this thesis. The accuracy of the fraction by fraction accumulated dose depends directly on the accuracy of the deformable image registration (DIR). Using the simulation framework, we could quantify the requirements on the DIR to 2 mm or less for a 3% uncertainty in the target dose coverage. Probabilistic planning is computationally intensive since many hundred treatments must be simulated for sufficient statistical accuracy in the calculated treatment outcome. A fast dose calculation algorithm was developed based on the perturbation of a pre-calculated dose distribution with the local ratio of the simulated treatment’s fluence and the fluence of the pre-calculated dose. A speedup factor of ~1000 compared to full dose calculation was achieved with near identical dose coverage probabilities for a prostate treatment. For some body sites, such as the cervix dataset in this work, organ motion must be included for realistic treatment simulation. A statistical shape model (SSM) based on principal component analysis (PCA) provided the samples of deformation. Seven eigenmodes from the PCA was sufficient to model the dosimetric impact of the interfraction deformation. A probabilistic optimization method was developed using constructs from risk management of stock portfolios that enabled the dose planner to request a target dose coverage probability. Probabilistic optimization was for the first time applied to dataset from cervical cancer patients where the SSM provided samples of deformation. The average dose coverage probability of all patients in the dataset was within 1% of the requested.

The goal of this thesis is to use machine learning methods for datasets of scanned faces and to create a program that allows to explore and edit faces represented as triangle meshes with a number of controls. Firstly we had to reduce dimension of triangle meshes by PCA and then we tried to predict shape of meshes according to physical properties like weight, height, age and BMI. The modeled faces can be used in animation or games.

Osteoporosis, associated with reduced bone mineral density and structural degeneration, greatly increases the risk of fragility fracture. Magnetic resonance imaging (MRI) has been applied to central skeletal sites including the proximal femur due to its non-ionizing radiation. A major challenge of volumetric bone imaging of the hip is the selection of regions of interest (ROIs) for computation of regional bone measurements. To address this issue, an MRI-based active shape model (ASM) of the human proximal femur is applied to automatically generate ROIs. The challenge in developing the ASM for a complex three-dimensional (3-D) shape lies in determining a large number of anatomically consistent landmarks for a set of training shapes. This thesis proposes a new method of generating the proximal femur ASM, where two types of landmarks, namely fiducial and secondary landmarks, are used. The method consists of—(1) segmentation of the proximal femur bone volume, (2) smoothing the bone surface, (3) drawing fiducial landmark lines on training shapes, (4) drawing secondary landmarks on a reference shape, (5) landmark mesh generation on the reference shape using both fiducial and secondary landmarks, (6) generation of secondary landmarks on other training shapes using the correspondence of fiducial landmarks and an elastic deformation of the landmark mesh, (7) computation of the active shape model. A proximal femur ASM has been developed using hip MR scans of 45 post-menopausal women. The results of secondary landmark generation were visually satisfactory, and no topology violation or notable geometric distortion artifacts were observed. Performance of the method was examined in terms of shape representation errors in a leave-one-out test. The mean and standard deviation of leave-one-out shape representation errors were 0.34mm and 0.09mm respectively. The experimental results suggest that the framework of fiducial and secondary landmarks allows reliable computation of statistical shape models for complex 3-D anatomic structures.

We present an image-based approach to infer 3D structure parameters using a probabilistic "shape+structure'' model. The 3D shape of a class of objects may be represented by sets of contours from silhouette views simultaneously observed from multiple calibrated cameras. Bayesian reconstructions of new shapes can then be estimated using a prior density constructed with a mixture model and probabilistic principal components analysis. We augment the shape model to incorporate structural features of interest; novel examples with missing structure parameters may then be reconstructed to obtain estimates of these parameters. Model matching and parameter inference are done entirely in the image domain and require no explicit 3D construction. Our shape model enables accurate estimation of structure despite segmentation errors or missing views in the input silhouettes, and works even with only a single input view. Using a dataset of thousands of pedestrian images generated from a synthetic model, we can perform accurate inference of the 3D locations of 19 joints on the body based on observed silhouette contours from real images.

With continually growing availability of high performance computing resources, the finite element methods (FEM) are becoming increasingly more efficient and practical research tools. In the domain of computational biomechanics, FEMs have been successfully applied in investigation of biomedical problems that include impact and fracture mechanics of bone, load transmission through the joints, feasibility of joint replacements, and many others. The present research study was concerned with the development of a detailed, anatomically accurate, finite element model of the human hand and wrist. As a first step in this direction, we used a publically available database of wrist bone anatomy and carpal kinematics to construct a multi-body statistical shape model (SSM) of the wrist. The resulting model provides an efficient parameterization of anatomical variations of the entire training set and can thus overcome the major shortcoming of conventional biomechanical models associated with limited generalization ability. The main contributions of this work are: 1) A robust method for constructing multi-body SSM of the wrist from surface meshes. 2) A novel technique for resampling closed genus-0 meshes to produce high quality triangulations suitable for finite element simulations. Additionally, all techniques developed in the course of this study could be directly applied to create an equivalent model of the tarsus.