Iconic feature based nonrigid registration: the PASHA algorithm

By Pascal Cathier, É. Bardinet, Didier Dormont, Xavier Pennec, Nicholas Ayache. Computer Vision and Image Understanding, 2003

298 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/0f21565dfcc858a122ca587bcd5071175fd91952

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In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Dense image registration through MRFs and efficient linear programming (2008, background): “In general, these images are re-lated with a non-linear transformation as well as a non-linear relation between intensities, that is where T ( x ) is the transformation and h is a non-linear operator explaining the changes of appearance between them.”
Spherical Demons: Fast Diffeomorphic Landmark-Free Surface Registration (2009, methodology): “We choose to work with the modified Demons objective function [14], [66].”
A framework to study the cortical folding patterns. (2004, background): “Most are driven by the maximization of an intensity-based similarity measure computed between the processed image and a template image (Cachier et al., 2003; Guimond et al., 2001; Hellier et al., 2001; Shen and Davatzikos, 2002).”
Survey of Medical Image Registration (2013, methodology): “A more sophisticated approach built on the same principles is used in PASHA (Pair And Smooth Hybrid Algorithm) [41] where the similarity measure is the weighted sum of an intensity similarity, a term expressing the difference between the landmark correspondence and the volumetric deformation ﬁeld,…”
Non-rigid image registration: theory and practice. (2004, methodology): “A more sophisticated approach built on the same principles is used in PASHA (Pair And Smooth Hybrid Algorithm) [55] where the similarity measure is the weighted sum of an intensity similarity, a term expressing the difference between the landmark correspondence and the volumetric deformation field, ”
Non-parametric Diffeomorphic Image Registration with the Demons Algorithm (2007, methodology): “We classically have dist (s, c) = ‖c− s‖ and Reg (s) = ‖∇s‖ 2 but the regularization can also be modified to handle fluid-like constraints [7].”
Deformable Medical Image Registration: A Survey (2013, background): “[42] proposed, to relate symmetric Demons forces with the efficient second-order minimization (ESM) [43].”
Brain Functional Localization: A Survey of Image Registration Techniques (2007, methodology): “Several voxel intensity similarity measures have been tested in combination with feature correspondences in the OSN and PASHA algorithms.”
A three-dimensional, histological and deformable atlas of the human basal ganglia. I. Atlas construction based on immunohistochemical and MRI data (2007, methodology): “In order to compensate for these distortions, various non-linear regularization schemes combining elastic and fluid terms (Cachier et al., 2003; Stefanescu et al., 2004) were tested.”
Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies (2014, methodology): “As in [28], the means and standard deviations at voxel were calculated using a Gaussian kernel with standard deviation”