Attribute vector guided groupwise registration.

NeuroImage
Qian WangDinggang Shen

Abstract

Groupwise registration has been recently introduced to simultaneously register a group of images by avoiding the selection of a particular template. To achieve this, several methods have been proposed to take advantage of information-theoretic entropy measures based on image intensity. However, simplistic utilization of voxelwise image intensity is not sufficient to establish reliable correspondences, since it lacks important contextual information. Therefore, we explore the notion of attribute vector as the voxel signature, instead of image intensity, to guide the correspondence detection in groupwise registration. In particular, for each voxel, the attribute vector is computed from its multi-scale neighborhoods, in order to capture the geometric information at different scales. The probability density function (PDF) of each element in the attribute vector is then estimated from the local neighborhood, providing a statistical summary of the underlying anatomical structure in that local pattern. Eventually, with the help of Jensen-Shannon (JS) divergence, a group of subjects can be aligned simultaneously by minimizing the sum of JS divergences across the image domain and all attributes. We have employed our groupwise registrati...Continue Reading

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Citations

Jul 5, 2012·Medical Physics·Liang GaoWufan Chen
Dec 3, 2014·NeuroImage·Minjeong KimDinggang Shen
Dec 3, 2013·Human Brain Mapping·Qian WangDinggang Shen
May 17, 2011·IEEE Transactions on Medical Imaging·Pew-Thian YapDinggang Shen
Feb 2, 2012·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Ziga SpiclinFranjo Pernus
Apr 6, 2013·Journal of Neuroscience Methods·William R CrumSteven C R Williams
Jan 29, 2011·Neurobiology of Aging·Yang LiUNKNOWN Alzheimer's Disease Neuroimaging Initiative
Nov 10, 2015·IEEE Transactions on Bio-medical Engineering·Guorong WuDinggang Shen
Jul 11, 2020·Medical & Biological Engineering & Computing·Yuanjun WangYu Liu

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