A Statistical Shape Model (SSM) is a statistical representation of a shape obtained\ud from data to study variation in shapes. Work on shape modelling is constrained by\ud many unsolved problems, for instance, difficulties in modelling local versus global\ud variation. SSM have been successfully applied in medical image applications such\ud as the analysis of brain anatomy. Since brain structure is so complex and varies\ud across subjects, methods to identify morphological variability can be useful for\ud diagnosis and treatment.\ud The main objective of this research is to generate and develop a statistical shape\ud model to analyse local variation in shapes. Within this particular context, this\ud work addresses the question of what are the local elements that need to be identified for effective shape analysis. Here, the proposed method is based on a Point\ud Distribution Model and uses a combination of other well known techniques: Fractal\ud analysis; Markov Chain Monte Carlo methods; and the Curvature Scale Space\ud representation for the problem of contour localisation. Similarly, Diffusion Maps\ud are employed as a spectral shape clustering tool to identify sets of local partitions\ud useful in the shape analysis. Additionally, a novel Hierarchical Shape Analysis\ud method based on the Gaussian and Laplacian pyramids is explained and used to\ud compare the featured Local Shape Model.\ud Experimental results on a number of real contours such as animal, leaf and brain\ud white matter outlines have been shown to demonstrate the effectiveness of the\ud proposed model. These results show that local shape models are efficient in modelling\ud the statistical variation of shape of biological structures. Particularly, the\ud development of this model provides an approach to the analysis of brain images\ud and brain morphometrics. Likewise, the model can be adapted to the problem of\ud content based image retrieval, where global and local shape similarity needs to be\ud measured
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