Int J Comput Assist Radiol Surg - Local/non-local regularized image segmentation using graph-cuts: application to dynamic and multispectral MRI.

Tópicos

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Resumo

JECTIVE: Multispectral, multichannel, or time series image segmentation is important for image analysis in a wide range of applications. Regularization of the segmentation is commonly performed using local image information causing the segmented image to be locally smooth or piecewise constant. A new spatial regularization method, incorporating non-local information, was developed and tested.METHODS: Our spatial regularization method applies to feature space classification in multichannel images such as color images and MR image sequences. The spatial regularization involves local edge properties, region boundary minimization, as well as non-local similarities. The method is implemented in a discrete graph-cut setting allowing fast computations.RESULTS: The method was tested on multidimensional MRI recordings from human kidney and brain in addition to simulated MRI volumes.CONCLUSION: The proposed method successfully segment regions with both smooth and complex non-smooth shapes with a minimum of user interaction.

Resumo Limpo

jectiv multispectr multichannel time seri imag segment import imag analysi wide rang applic regular segment common perform use local imag inform caus segment imag local smooth piecewis constant new spatial regular method incorpor nonloc inform develop testedmethod spatial regular method appli featur space classif multichannel imag color imag mr imag sequenc spatial regular involv local edg properti region boundari minim well nonloc similar method implement discret graphcut set allow fast computationsresult method test multidimension mri record human kidney brain addit simul mri volumesconclus propos method success segment region smooth complex nonsmooth shape minimum user interact

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