Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333645
Title: Computational methods for image rotation and computer aided tomography
Author: Fayed, Karam Ali
ISNI:       0000 0001 3458 5279
Awarding Body: Cranfield University
Current Institution: Cranfield University
Date of Award: 1993
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Abstract:
This thesis, describes work whose principal goal has been to research and iden- tify an optimum algorithm for computing the Radon transform - optimum in the sense of minimum CPU time for maximum image fidelity. This transform is the basis for a wide range of applications which are either directly or indirectly associ- ated with Computer Aided Tomography. It has therefore been important to try and identify an optimum computational technique for the evaluation of this transform. A central theme to the computational techniques reported in this thesis has been the digital image rotation for which a new algorithm has been designed based en- tirely on integer arithmetic. This algorithm has been used for the evaluation of the forward and inverse Radon transforms (in particular, forward and back-projection). The relationship between the interpolation methods used for image rotation and the filtering techniques required to compute the inverse Radon transform has been stud- ied in detail. The result of this study has been to identify a specific data processing path which provides an algorithm whose 'computational energy' is approximately half that of previously published algorithms. With an optimum algorithm identified, research has been undertaken into the use of the Radon transform for two-dimensional array processing. It is shown that the Radon transform can be used to reduce the dimensionality of a problem from two-dimensions to a one-dimensional problem. By applying signal processors to the set of projections obtained by taking the Radon transform of an image, the inverse Radon transform can be used to reconstruct the processed image. The value of this approach lies in the fact that some useful one-dimensional signal processing algorithms do not have a two-dimensional extension or else the implementation of the two-dimensional algorithm is computationally intensive. An example of the latter case is reported.
Supervisor: Blackledge, J. M. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.333645  DOI: Not available
Keywords: Pattern recognition & image processing Pattern recognition systems Pattern perception Image processing
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