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Statistical shape analysis for bio-structures : local shape modelling, techniques and applications
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Valdés Amaro, Daniel Alejandro (2009) Statistical shape analysis for bio-structures : local shape modelling, techniques and applications. PhD thesis, University of Warwick.
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Official URL: http://webcat.warwick.ac.uk/record=b2339988~S15
Abstract
A Statistical Shape Model (SSM) is a statistical representation of a shape obtained
from data to study variation in shapes. Work on shape modelling is constrained by
many unsolved problems, for instance, difficulties in modelling local versus global
variation. SSM have been successfully applied in medical image applications such
as the analysis of brain anatomy. Since brain structure is so complex and varies
across subjects, methods to identify morphological variability can be useful for
diagnosis and treatment.
The main objective of this research is to generate and develop a statistical shape
model to analyse local variation in shapes. Within this particular context, this
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
Distribution Model and uses a combination of other well known techniques: Fractal
analysis; Markov Chain Monte Carlo methods; and the Curvature Scale Space
representation for the problem of contour localisation. Similarly, Diffusion Maps
are employed as a spectral shape clustering tool to identify sets of local partitions
useful in the shape analysis. Additionally, a novel Hierarchical Shape Analysis
method based on the Gaussian and Laplacian pyramids is explained and used to
compare the featured Local Shape Model.
Experimental results on a number of real contours such as animal, leaf and brain
white matter outlines have been shown to demonstrate the effectiveness of the
proposed model. These results show that local shape models are efficient in modelling
the statistical variation of shape of biological structures. Particularly, the
development of this model provides an approach to the analysis of brain images
and brain morphometrics. Likewise, the model can be adapted to the problem of
content based image retrieval, where global and local shape similarity needs to be
measured.
Item Type: | Thesis (PhD) | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Library of Congress Subject Headings (LCSH): | Shapes -- Statistics, Image processing, Imaging systems in medicine | ||||
Official Date: | October 2009 | ||||
Dates: |
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Institution: | University of Warwick | ||||
Theses Department: | Department of Computer Science | ||||
Thesis Type: | PhD | ||||
Publication Status: | Unpublished | ||||
Supervisor(s)/Advisor: | Bhalerao, Abhir H. ; Rajpoot, Nasir M. (Nasir Mahmood) | ||||
Sponsors: | Consejo Nacional de Ciencia y Tecnología (Mexico) (CONACYT) | ||||
Extent: | xxv, 189 leaves : ill., charts | ||||
Language: | eng |
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