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Abstract:
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In this dissertation a new approach for non -rigid image registration using mutual information is introduced . A fast method for non -rigid registration is developed by adjusting divergence and curl of an intermediate vector field from which the deformation field is computed using finite difference method . The similarity measure mutual information is employed in the gradient -based cost minimization (or mutual information maximization ) of the registration . The huge amount of data associated with MRI is handled by fully automated algorithm optimized with a multi -resolution topology preserving regridding scheme . The adaptive grid system naturally distributes more grids to deprived areas . The positive monitor function disallows grid folding and provides a mean to control the ratio of the areas between the original and transformed domain . The flexibility of the adaptive grid allocation could dramatically reduce processing time with quality preserved . Mutual information facilitates robust registration between different image modalities . Different types of joint histogram estimation are compared and integrated with the system . The whole system is also implemented on GPU which allows efficient parallel computation of vast v amount of 3D data in SIMD manner during different procedures . The GPU implementation offers up to 221 times speed up in the gradient normalization routine and around 40 times speed up in the overall calculation . This scheme is applied on 3D dynamic contrast -enhanced breast MRI , which requires the registration algorithm to be non -rigid , contrast -enhanced features preserving and within clinical visit time limit . Experiments show promising results and great potential for future extension . |