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Registration is an important component of medical image analysis and for analysing large amounts of data it is desirable to have fully automatic registration methods. Many different automatic registration methods have been proposed to date, and almost all share a common mathematical framework - one of optimising a cost function. To date little attention has been focused on the optimisation method itself, even though the success of most registration methods hinges on the quality of this optimisation. This paper examines the assumptions underlying the problem of registration for brain images using inter-modal voxel similarity measures. It is demonstrated that the use of local optimisation methods together with the standard multi-resolution approach is not sufficient to reliably find the global minimum. To address this problem, a global optimisation method is proposed that is specifically tailored to this form of registration. A full discussion of all the necessary implementation details is included as this is an important part of any practical method. Furthermore, results are presented for inter-modal, inter-subject registration experiments that show that the proposed method is more reliable at finding the global minimum than several of the currently available registration packages in common usage.

Original publication

DOI

10.1016/s1361-8415(01)00036-6

Type

Journal article

Journal

Medical image analysis

Publication Date

06/2001

Volume

5

Pages

143 - 156

Addresses

University of Oxford, John Radcliffe Hospital, FMRIB Centre, Oxford OX3 9DU, UK. mark@fmrib.ox.ac.uk

Keywords

Humans, Magnetic Resonance Imaging, Brain Mapping, Mathematics, Image Processing, Computer-Assisted, Costs and Cost Analysis