Dr. Neil A. Thacker Profile

Dr. Neil A. Thacker

Sr. Lecturer at Univ of Manchester

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Proceedings Article | 15 May 2003 Paper

Biomechanical simulation of atrophy in MR images

Andrew Castellano Smith, William Crum, Derek Hill, Neil Thacker, Paul Bromiley

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.480412

KEYWORDS: Brain, Tissues, Neuroimaging, Dementia, Finite element methods, Magnetic resonance imaging, Image registration, Modeling, Natural surfaces, Signal attenuation

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Progressive cerebral atrophy is a physical component of the most common forms of dementia - Alzheimer's disease, vascular dementia, Lewy-Body disease and fronto-temporal dementia. We propose a phenomenological simulation of atrophy in MR images that provides gold-standard data; the origin and rate of progression of atrophy can be controlled and the resultant remodelling of brain structures is known. We simulate diffuse global atrophic change by generating global volumetric change in a physically realistic biomechanical model of the human brain. Thermal loads are applied to either single, or multiple, tissue types within the brain to drive tissue expansion or contraction. Mechanical readjustment is modelled using finite element methods (FEM). In this preliminary work we apply these techniques to the MNI brainweb phantom to produce new images exhibiting global diffuse atrophy. We compare the applied atrophy with that measured from the images using an established quantitative technique. Early results are encouraging and suggest that the model can be extended and used for validation of atrophy measurement techniques and non-rigid image registration, and for understanding the effect of atrophy on brain shape.

Proceedings Article | 19 March 2003 Paper

Using quantitative statistics for the construction of machine vision systems

Neil Thacker

Proceedings Volume 4877, (2003) https://doi.org/10.1117/12.468491

KEYWORDS: Data modeling, Machine vision, 3D modeling, Error analysis, Statistical analysis, Statistical modeling, Image processing, Visual process modeling, Data processing, Binary data

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This paper describes a design methodology for constructing machine vision systems. Central to this is the use of empirical design techniques and in particular quantitative statistics. The approach views both the construction and evaluation of systems as one and is based upon what could be regarded as a set of self-evident propositions; (1) Vision algorithms must deliver information allowing practical decisions regarding interpretation of an image. (2) Probability is the only self-consistent computational framework for data analysis, and so must form the basis of all algorithmic analysis processes. (3) The most effective and robust algorithms will be those that match most closely the statistical properties of the data. (4) A statistically based algorithm which takes correct account of all available data will yield an optimal result. Where the definition of optimal can be unambiguously defined by the statistical specification of the problem. Machine vision research has not emphasized the need for (or necessary methods of) algorithm characterization, which is unfortunate, as the subject cannot advance without a sound empirical base. In general this problem can be attributed to one of two factors; a poor understanding of the role of assumptions and statistics, and a lack of appreciation of what is to be done with the generated data. The methodology described here focuses on identifying the statistical characteristics of the data and matching these to the assumptions of the underlying techniques. The methodology has been developed from more than a decade of vision design and testing, which has culminated in the construction of the TINA open source image analysis/machine vision system [htt://www.tina-vision.net].

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