Automated Pivot Location for the Cartesian-Polar Hybrid Point Distribution Model

The Point Distribution Model (PDM) has already proved useful for many tasks involving the location or tracking of deformable objects. A principal limitation is that non-linear variations must be approximated by combining linear variations, which sometimes results in a non-optimal model producing implausible object shapes. The Cartesian-Polar Hybrid PDM helps to overcome this limitation; selective use of polar geometry allows bending or pivotal deformation to be modelled more accurately; model components which exhibit no such trend remain in the Cartesian domain. Use of the Hybrid PDM currently requires the identification of pivot points by hand. In this paper we present a method for automatically identifying rigid model parts and pivot points from the training data. Experimental results are given for real data from human hands and for synthetic data from a simple jointed object. Keywords: Deformable models, Point Distribution Model, polar coordinates. 1 Introduction Models are used wi..

Statistical shape modelling: automatic shape model building

Statistical Shape Models (SSM) have wide applications in image segmentation, surface registration and morphometry. This thesis deals with an important issue in SSM, which is establishing correspondence between a set of shape surfaces on either 2D or 3D. Current methods involve either manual annotation of the data (current ‘gold standard’); or establishing correspondences by using segmentation or registration algorithms; or using an information technique, Minimum Description Length (MDL), as an objective function that measures the utility of a model (the state-of-the-art). This thesis presents in principle another framework for establishing correspondences completely automatically by treating it as a learning process. Shannon theory is used extensively to develop an objective function, which measures the performance of a model along each eigenvector direction, and a proper weighting is automatically calculated for each energy component. Correspondence finding can then be treated as optimizing the objective function. An efficient optimization method is also incorporated by deriving the gradient of the cost function. Experimental results on various data are presented on both 2D and 3D. In the end, a quantitative evaluation between the proposed algorithm and MDL shows that the proposed model has better Generalization Ability, Specificity and similar Compactness. It also shows a good potential ability to solve the so-called “Pile Up” problem that exists in MDL. In terms of application, I used the proposed algorithm to help build a facial contour classifier. First, correspondence points across facial contours are found automatically and classifiers are trained by using the correspondence points found by the MDL, proposed method and direct human observer. These classification schemes are then used to perform gender prediction on facial contours. The final conclusion for the experiments is that MEM found correspondence points built classification scheme conveys a relatively more accurate gender prediction result. Although, we have explored the potential of our proposed method to some extent, this is not the end of the research for this topic. The future work is also clearly stated which includes more validations on various 3D datasets; discrimination analysis between normal and abnormal subjects could be the direct application for the proposed algorithm, extension to model-building using appearance information, etc

Multi-scale active shape description in medical imaging

Shape description in medical imaging has become an increasingly important research field in recent years. Fast and high-resolution image acquisition methods like Magnetic Resonance (MR) imaging produce very detailed cross-sectional images of the human body - shape description is then a post-processing operation which abstracts quantitative descriptions of anatomically relevant object shapes. This task is usually performed by clinicians and other experts by first segmenting the shapes of interest, and then making volumetric and other quantitative measurements. High demand on expert time and inter- and intra-observer variability impose a clinical need of automating this process. Furthermore, recent studies in clinical neurology on the correspondence between disease status and degree of shape deformations necessitate the use of more sophisticated, higher-level shape description techniques. In this work a new hierarchical tool for shape description has been developed, combining two recently developed and powerful techniques in image processing: differential invariants in scale-space, and active contour models. This tool enables quantitative and qualitative shape studies at multiple levels of image detail, exploring the extra image scale degree of freedom. Using scale-space continuity, the global object shape can be detected at a coarse level of image detail, and finer shape characteristics can be found at higher levels of detail or scales. New methods for active shape evolution and focusing have been developed for the extraction of shapes at a large set of scales using an active contour model whose energy function is regularized with respect to scale and geometric differential image invariants. The resulting set of shapes is formulated as a multiscale shape stack which is analysed and described for each scale level with a large set of shape descriptors to obtain and analyse shape changes across scales. This shape stack leads naturally to several questions in regard to variable sampling and appropriate levels of detail to investigate an image. The relationship between active contour sampling precision and scale-space is addressed. After a thorough review of modem shape description, multi-scale image processing and active contour model techniques, the novel framework for multi-scale active shape description is presented and tested on synthetic images and medical images. An interesting result is the recovery of the fractal dimension of a known fractal boundary using this framework. Medical applications addressed are grey-matter deformations occurring for patients with epilepsy, spinal cord atrophy for patients with Multiple Sclerosis, and cortical impairment for neonates. Extensions to non-linear scale-spaces, comparisons to binary curve and curvature evolution schemes as well as other hierarchical shape descriptors are discussed