Cootes, T. F.

Petrovic, V. S.

Taylor, C.

Twining, C. J.

English

We describe an framework that simultaneously segments and registers a set of medical images in an automatic manner, incrementally constructing a model of the structure and shape deformations of the set. The framework extends existing groupwise registration and modeling approaches by explicitly modeling the fraction of each tissue type in each voxel, rather than the expected intensity in each voxel. This decouples the model from the effects of the imaging sequence and thus imaging modality. When estimating the optimal deformation field between examples in the set each image is compared to its reconstruction generated from the model segment fractions and the current estimate of its intensity distributions for each tissue type (i.e. an estimate of how the model would appear given the imaging conditions for that image). We also present a method to determine the optimal number of tissue types and fully automate the approach as well as model construction methods that ensure efficient convergeance. We describe the algorithm in detail and present results of applying it to a set of MR images of the brain

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The University of Manchester ResearchAutomatic Framework for Medical Image Registration,Segmentation and ModelingLink to publication record in Manchester Research ExplorerCitation for published version (APA):Petrovic, V. S., Cootes, T. F., Twining, C. J., & Taylor, C. (2006). Automatic Framework for Medical ImageRegistration, Segmentation and Modeling. In Medical Image Understanding and Analysis (pp. 141-145)Published in:Medical Image Understanding and AnalysisCiting this paperPlease note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscriptor Proof version this may differ from the final Published version. If citing, it is advised that you check and use thepublisher's definitive version.General rightsCopyright and moral rights for the publications made accessible in the Research Explorer are retained by theauthors and/or other copyright owners and it is a condition of accessing publications that users recognise andabide by the legal requirements associated with these rights.Takedown policyIf you believe that this document breaches copyright please refer to the University of Manchester’s TakedownProcedures [http://man.ac.uk/04Y6Bo] or contact uml.scholarlycommunications@manchester.ac.uk providingrelevant details, so we can investigate your claim.Download date:09. Jun. 2022

Automatic Framework for Medical Image Registration, Segmentation and Modeling

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Automatic Framework for Medical Image Registration, Segmentation and Modeling

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