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This paper describes my participation in a MITRE Corporation sponsored computer science clinic project at Harvey Mudd College as my senior project. The goal of the project was to implement a landmark-based pose correction system as a component in a larger, existing face recognition system. The main contribution I made to the project was the implementation of the Active Shape Models (ASM) algorithm; the inner workings of ASM are explained as well as how the pose correction system makes use of it. Included is the most recent draft (as of this writing) of the final report that my teammates and I produced highlighting the year's accomplishments. Even though there are few quantitative results to show because the clinic program is ongoing, our qualitative results are quite promising.

This work deals with methods of segmentation of biomedical image signals. It describes, sums up and compares representative methods of digital image processing. Segmentation based on parametric representation is one of the mentioned methods. So as the basic parameter can be chosen for example luminance and the final binary image is obtained by thresholding. Next described method is segmentation based on edge representation. This method can be divided into edge detection by the help of edge detectors and of Hough transformation. Edge detectors work with the first and second derivation. The following method is region-based segmentation, which can be used for a image with noise. This category can be divided into three parts. The first one is segmentation via splitting and merging regions, when the image is split and the created regions are tested on a defined condition. If the condition is satisfied, the region merges and doesn’t continue splitting. The second one is region growing segmentation, when adjacent pixels with a similar intensity of luminance are grouped together and create a segmentated region. Third one is watershed segmentation algorithm based on the idea of water diffusion on uneven surface. The last group of methods is segmentation via flexible and active contours. Here is described an active shape model proceeding from a possibility to deform models so that they match with sample shapes. Next I also describe method Snakes, where occurs gradual contour shaping up to the edge of the object in the image. For the final editing is used mathematical morphology of segmentated images. I aimed to meet methods of image signals segmentation, to cover the chosen methods as a script in programming language Matlab and to check their properties on images.

Our motivation is to develop an automated technique for the segmentation of sub-cortical human brain structures from MR images. To this purpose, models of shape-and-appearance are constructed and fit to new image data. The statistical models are trained from 317 manually labelled T1-weighted MR images. Shape is modelled using a surface-based point distribution model (PDM) such that the shape space is constrained to the linear combination of the mean shape and eigenvectors of the vertex coordinates. In addition, to model intensity at the structural boundary, intensities are sampled along the surface normal from the underlying image. We propose a novel Bayesian appearance model whereby the relationship between shape and intensity are modelled via the conditional distribution of intensity given shape. Our fully probabilistic approach eliminates the need for arbitrary weightings between shape and intensity as well as for tuning parameters that specify the relative contribution between the use of shape constraints and intensity information. Leave-one-out cross-validation is used to validate the model and fitting for 17 structures. The PDM for shape requires surface parameterizations of the volumetric, manual labels such that vertices retain a one-to-one correspondence across the training subjects. Surface parameterizations with correspondence are generated through the use of deformable models under constraints that embed the correspondence criterion within the deformation process. A novel force that favours equal-area triangles throughout the mesh is introduced. The force adds stability to the mesh such that minimal smoothing or within-surface motion is required. The use of the PDM for segmentation across a series of subjects results in a set surfaces that retain point correspondence. The correspondence facilitates landmark-based shape analysis. Amongst other metrics, vertex-wise multivariate statistics and discriminant analysis are used to investigate local and global size and shape differences between groups. The model is fit, and shape analysis is applied to two clinical datasets.

We investigate the photometric accuracy of the sparse data from astrometric surveys available on AstDyS. We use data from seven surveys with the best accu- racy in combination with relative lightcurves in the lightcurve inversion method to derive ∼300 new asteroid physical models (i.e., convex shapes and rotational states). We introduce several reliability tests that we use on all new asteroid mod- els. We investigate rotational properties of our MBAs sample (∼450 models here or previously derived by the lightcurve inversion), especially the spin vector dis- tribution. It is clear that smaller asteroids (D 30 km) have strongly anisotropic spin vector distribution even when we remove the bias of the lightcurve inversion, the poles are clustered towards ecliptic poles. We explain this anisotropy as a re- sult of non-gravitational torques (YORP effect) acting on these objects, because without accounting these torques, we were not able to create such anisotropic dis- tribution by our model of the spin evolution. We also estimate sizes for 41 and 10 asteroids by scaling their models to fit the adaptive optics profiles and occultation observations, respectively.

Osteoarthritis (OA) is the most common form of human joint disease causing significant pain and disability. Current treatment for hip OA is limited to pain management and joint replacement for end-stage disease. The development of methods for early diagnosis and new treatment options are urgently needed to minimise the impact of the disease. Studies of hip OA have shown that hip joint morphology correlates with susceptibility to hip OA and disease progression. Bone shape analyses play an important role in disease diagnosis, pre-operative planning, and treatment analysis as well as in epidemiological studies aimed at identifying risk factors for hip OA. Statistical Shape Models (SSMs) are being increasingly applied to imaging-based bone shape analyses as they provide a means of quantitatively describing the global shape of the bone. This is in contrast to conventional clinical and research practice where the analysis of bone shape is reduced to a series of measurements of lengths and angles. This thesis describes the development of a novel fully automatic software system that segments the proximal femur from anteroposterior (AP) pelvic radiographs by densely placing 65 points along its contour. These annotations can then be used for the detailed morphometric analysis of proximal femur shape. The performance of the system was evaluated on a large dataset of 839 radiographs of mixed quality. Achieving a mean point-to-curve error of less than 0.9mm for 99% of all 839 AP pelvic radiographs, this is the most accurate and robust automatic method for segmenting the proximal femur in two-dimensional radiographs yet published. The system was also applied to a number of morphometric analyses of the proximal femur, showing that SSM-based radiographic proximal femur shape significantly differs between males and females, and is highly symmetric between the left and right hip joint of an individual. In addition, the research described in this thesis demonstrates how the point annotations resulting from the system can be used for univariate and multivariate genetic association analyses, identifying three novel genetic variants that contribute to radiographic proximal femur shape while also showing an association with hip OA.The developed system will facilitate complex morphometric and genetic analyses of shape variation of the proximal femur across large datasets, paving the way for the development of new options to diagnose, treat and prevent hip OA.

As imagens são sinais que possuem muita informação. Os objetos representados em ima- gens podem sofrer deformações fazendo com que suas características mudem, o que difi- culta o reconhecimento do objeto. A chave para o sucesso de um sistema de reconheci- mento de padrões em imagens é escolher adequadamente a sua abordagem e um modelo para as feições presentes nas imagens. Uma das dificuldades é extrair e selecionar as feições que são mais discriminantes entre as diferentes classes usadas para modelar um objeto. Os modelos ativos de formas (Active Shape Models ASM) adaptam-se às defor- mações de um objeto. O objeto a ser modelado, pode ser representado com um modelo de pontos distribuídos (Point Distribution Models PDM). O PDM consiste de pontos de interesse ou marcadores, que permitem a extração de feições em localizações específicas do objeto. Após tratar a incerteza da localização e oclusão dos marcadores é possível ex- trair as feições mais representativas, obtendo-se um desempenho alto em termos da taxa de reconhecimento. Nesta tese são introduzidas novas formas para extrair e selecionar feições com modelos ativos de formas, que melhoram a taxa de classificação onde há objetos deformáveis. Esta tese é inovadora no sentido de aperfeiçoar o uso de ASMs na classificação de faces humanas, e na sua aplicação no monitoramento visual de outros tipos de objetos deformáveis. / Images are signals that have a lot of information. The objects depicted in images may suf- fer deformations causing changes in their characteristics, which hinders the recognition of the object. The key to the success of a system of pattern recognition in images is to choose properly your approach and a model for the features in the images. One difficulty is to extract and select the features that are most discriminating between different classes used to model an object. The Active Shape Models (ASM) adapt to deformations of an object. The object to be modeled can be represented with a Points Distribution Model (PDM). The PDM consists of points of interest or landmarks that allow the extraction of features in specific locations of the object. After treating the uncertainty of the location and occlu- sion of the landmarks it is possible to extract the most representative features, obtaining a high performance in terms of recognition rate. This thesis introduces new ways to extract and select features with ASMs, which improve the classification rate where deformable objects are present. This thesis is innovative in the sense that improves the use of ASMs in the classification of human faces, and that can be applied in visual monitoring of other types of deformable objects.

As imagens são sinais que possuem muita informação. Os objetos representados em ima- gens podem sofrer deformações fazendo com que suas características mudem, o que difi- culta o reconhecimento do objeto. A chave para o sucesso de um sistema de reconheci- mento de padrões em imagens é escolher adequadamente a sua abordagem e um modelo para as feições presentes nas imagens. Uma das dificuldades é extrair e selecionar as feições que são mais discriminantes entre as diferentes classes usadas para modelar um objeto. Os modelos ativos de formas (Active Shape Models ASM) adaptam-se às defor- mações de um objeto. O objeto a ser modelado, pode ser representado com um modelo de pontos distribuídos (Point Distribution Models PDM). O PDM consiste de pontos de interesse ou marcadores, que permitem a extração de feições em localizações específicas do objeto. Após tratar a incerteza da localização e oclusão dos marcadores é possível ex- trair as feições mais representativas, obtendo-se um desempenho alto em termos da taxa de reconhecimento. Nesta tese são introduzidas novas formas para extrair e selecionar feições com modelos ativos de formas, que melhoram a taxa de classificação onde há objetos deformáveis. Esta tese é inovadora no sentido de aperfeiçoar o uso de ASMs na classificação de faces humanas, e na sua aplicação no monitoramento visual de outros tipos de objetos deformáveis. / Images are signals that have a lot of information. The objects depicted in images may suf- fer deformations causing changes in their characteristics, which hinders the recognition of the object. The key to the success of a system of pattern recognition in images is to choose properly your approach and a model for the features in the images. One difficulty is to extract and select the features that are most discriminating between different classes used to model an object. The Active Shape Models (ASM) adapt to deformations of an object. The object to be modeled can be represented with a Points Distribution Model (PDM). The PDM consists of points of interest or landmarks that allow the extraction of features in specific locations of the object. After treating the uncertainty of the location and occlu- sion of the landmarks it is possible to extract the most representative features, obtaining a high performance in terms of recognition rate. This thesis introduces new ways to extract and select features with ASMs, which improve the classification rate where deformable objects are present. This thesis is innovative in the sense that improves the use of ASMs in the classification of human faces, and that can be applied in visual monitoring of other types of deformable objects.

As imagens são sinais que possuem muita informação. Os objetos representados em ima- gens podem sofrer deformações fazendo com que suas características mudem, o que difi- culta o reconhecimento do objeto. A chave para o sucesso de um sistema de reconheci- mento de padrões em imagens é escolher adequadamente a sua abordagem e um modelo para as feições presentes nas imagens. Uma das dificuldades é extrair e selecionar as feições que são mais discriminantes entre as diferentes classes usadas para modelar um objeto. Os modelos ativos de formas (Active Shape Models ASM) adaptam-se às defor- mações de um objeto. O objeto a ser modelado, pode ser representado com um modelo de pontos distribuídos (Point Distribution Models PDM). O PDM consiste de pontos de interesse ou marcadores, que permitem a extração de feições em localizações específicas do objeto. Após tratar a incerteza da localização e oclusão dos marcadores é possível ex- trair as feições mais representativas, obtendo-se um desempenho alto em termos da taxa de reconhecimento. Nesta tese são introduzidas novas formas para extrair e selecionar feições com modelos ativos de formas, que melhoram a taxa de classificação onde há objetos deformáveis. Esta tese é inovadora no sentido de aperfeiçoar o uso de ASMs na classificação de faces humanas, e na sua aplicação no monitoramento visual de outros tipos de objetos deformáveis. / Images are signals that have a lot of information. The objects depicted in images may suf- fer deformations causing changes in their characteristics, which hinders the recognition of the object. The key to the success of a system of pattern recognition in images is to choose properly your approach and a model for the features in the images. One difficulty is to extract and select the features that are most discriminating between different classes used to model an object. The Active Shape Models (ASM) adapt to deformations of an object. The object to be modeled can be represented with a Points Distribution Model (PDM). The PDM consists of points of interest or landmarks that allow the extraction of features in specific locations of the object. After treating the uncertainty of the location and occlu- sion of the landmarks it is possible to extract the most representative features, obtaining a high performance in terms of recognition rate. This thesis introduces new ways to extract and select features with ASMs, which improve the classification rate where deformable objects are present. This thesis is innovative in the sense that improves the use of ASMs in the classification of human faces, and that can be applied in visual monitoring of other types of deformable objects.

The thesis is focussed on the reconstruction of a damaged skull represented by a polygonal model. The reconstruction is based on a statistical shape model of the skull. The thesis covers the registration of skulls by using a thin-plate spline method, aligning polygonal models by generalized procrustes analysis, the identification of missing parts of a skull by means of statistical shape models outliers analysis. Finally, missing parts of the skull are reconstructed and the accuracy of the reconstruction is estimated.