IEEE Trans Image Process - Simultaneous multiresolution strategies for nonrigid image registration.

Tópicos

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{ perform(1367) use(1326) method(1137) }
{ high(1669) rate(1365) level(1280) }
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Resumo

Multiresolution strategies are commonly used in the nonrigid registration to avoid local minima in the optimization space. Generally, a step-by-step hierarchical approach is adopted, in which the registration starts on a level with reduced complexity (downsampled images, global transformations), then continuing to levels with increased complexity, until the finest level is reached. In this paper, we propose two alternative multiresolution strategies for both the data and transformation models, in which different resolution levels are considered simultaneously instead of subsequently. Through combining the different strategies for data and transformation, we systematically define 3 ? 3 multiresolution schemes, including both existing and novel methods. Experiments on 10 pairs of computed tomography lung data sets showed that the best performing strategy resulted in a reduction of the upper quartile of the mean target registration error from 2 to 1.5 mm, compared with the conventionally hierarchical multiresolution method, while achieving smoother deformations. Experiments with intersubject registration of 18 3D T1-weighted MRI brain scans confirmed that simultaneous multiresolution strategies produce more accurate registration results (median of mean overlap increased from 0.55 to 0.57) and smoother deformation fields than the traditionally hierarchical method. Evaluation of robustness indicated that the largest differences in accuracy between methods are observed for structures with a relatively large initial misalignment.

Resumo Limpo

multiresolut strategi common use nonrigid registr avoid local minima optim space general stepbystep hierarch approach adopt registr start level reduc complex downsampl imag global transform continu level increas complex finest level reach paper propos two altern multiresolut strategi data transform model differ resolut level consid simultan instead subsequ combin differ strategi data transform systemat defin multiresolut scheme includ exist novel method experi pair comput tomographi lung data set show best perform strategi result reduct upper quartil mean target registr error mm compar convent hierarch multiresolut method achiev smoother deform experi intersubject registr d tweight mri brain scan confirm simultan multiresolut strategi produc accur registr result median mean overlap increas smoother deform field tradit hierarch method evalu robust indic largest differ accuraci method observ structur relat larg initi misalign

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