3D image registration using a fast noniterative algorithm

Zhilkin, Peter; Alexander, Murray E.

doi:10.1016/s0730-725x(00)00209-5

Cited by 14 publications

(3 citation statements)

References 8 publications

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“…Much can be done with these quantitative descriptions of local structure. They can be used for efficient image coding (Lawson and Zhu, 2000), for the calculation of similarity measures to drive image registration processes (Cen et al, 2004;Zhilkin and Alexander, 2000), or for figure/ground segmentation (Martin Figure 1. Much of natural images seems to be composed of the same qualitative structures repeated again and again.…”

Section: Qualitative Description Of Local Image Structurementioning

confidence: 99%

Hypotheses for Image Features, Icons and Textons

Griffin

Lillholm

2006

Int J Comput Vision

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We review ideas about the relationship between qualitative description of local image structure and quantitative description based on responses to a family of linear filters. We propose a sequence of three linking hypotheses. The first, the Feature Hypothesis, is that qualitative descriptions arise from a category system on filterresponse space. The second, the Icon Hypothesis, is that the partitioning into categories of filter response space is determined by a system of iconic images, one associated with each point of the space. The third, the Texton Hypothesis, is that the correct images to play the role of icons are those that are the most likely explanations of a vector of filter responses. We present results in support of these three hypotheses, including new results on 2-D 1st order structure.

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Section: Qualitative Description Of Local Image Structurementioning

confidence: 99%

Hypotheses for Image Features, Icons and Textons

Griffin

Lillholm

2006

Int J Comput Vision

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“…Image registration was performed across 4 levels, each down-sampled by a factor of 2. Down-sampling is a useful and more computationally-efficient method for preliminary registration with a large, mismatched dataset, 21 as evidenced by the changing morphology of our consecutive brain sections. Following initial registration, the next level-up images ( i.e.…”

Section: Resultsmentioning

confidence: 99%

Visualising mouse neuroanatomy and function by metal distribution using laser ablation-inductively coupled plasma-mass spectrometry imaging

et al. 2015

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“…Determined by Equation (8), the corresponding L c s are 31, 40, and 50 mm. The appropriate range of L c is discussed to balance the runtime and accuracy of spatial normalization, where the accuracy is described using the mean squared error (MSE) [37], [38].…”

Section: Methodsmentioning

confidence: 99%

Online Spatial Normalization for Real-Time fMRI

Yao²,

Ye³

et al. 2014

PLoS ONE

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Real-time functional magnetic resonance imaging (rtfMRI) is a recently emerged technique that demands fast data processing within a single repetition time (TR), such as a TR of 2 seconds. Data preprocessing in rtfMRI has rarely involved spatial normalization, which can not be accomplished in a short time period. However, spatial normalization may be critical for accurate functional localization in a stereotactic space and is an essential procedure for some emerging applications of rtfMRI. In this study, we introduced an online spatial normalization method that adopts a novel affine registration (AFR) procedure based on principal axes registration (PA) and Gauss-Newton optimization (GN) using the self-adaptive β parameter, termed PA-GN(β) AFR and nonlinear registration (NLR) based on discrete cosine transform (DCT). In AFR, PA provides an appropriate initial estimate of GN to induce the rapid convergence of GN. In addition, the β parameter, which relies on the change rate of cost function, is employed to self-adaptively adjust the iteration step of GN. The accuracy and performance of PA-GN(β) AFR were confirmed using both simulation and real data and compared with the traditional AFR. The appropriate cutoff frequency of the DCT basis function in NLR was determined to balance the accuracy and calculation load of the online spatial normalization. Finally, the validity of the online spatial normalization method was further demonstrated by brain activation in the rtfMRI data.

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3D image registration using a fast noniterative algorithm

Cited by 14 publications

References 8 publications

Hypotheses for Image Features, Icons and Textons

Hypotheses for Image Features, Icons and Textons

Visualising mouse neuroanatomy and function by metal distribution using laser ablation-inductively coupled plasma-mass spectrometry imaging

Online Spatial Normalization for Real-Time fMRI

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