A parametric intensity-based 3D image registration method for magnetic resonance imaging

Image registration (IR) aims to map one image to another of a same scene. With rapid progress in image acquisition technologies, 3D IR becomes an important problem in magnetic resonance imaging (MRI) and other applications. In the literature, however, most IR methods are for 2D images and there are only a limited number of 3D methods available. Because 3D images have much complicated structure than their 2D counterparts, 3D IR is not just a simple generalization of the 2D IR problem. In this paper, we develop a 3D IR method that can handle cases with affine geometric transformations well. By its definition, an affine transformation maps a line to a line, and it includes rotation, translation, and scaling as special cases. In practice, most geometric transformations involved in IR problems are affine transformations. Therefore, our proposed method can find many IR applications. It is shown that this method works well in various cases, including cases when the data size of a 3D image is reduced for different reasons. This latter property makes it attractive for many 3D IR applications, since 3D images are often big in data size and it is natural to reduce their size for fast computation.

[1]  Michal Irani,et al.  Motion Analysis for Image Enhancement: Resolution, Occlusion, and Transparency , 1993, J. Vis. Commun. Image Represent..

[2]  Mohammad Reza Daliri,et al.  Predicting brain states associated with object categories from fMRI data. , 2014, Journal of integrative neuroscience.

[3]  D. Wang,et al.  Image and Texture Segmentation Using Local Spectral Histograms , 2006, IEEE Transactions on Image Processing.

[4]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Peihua Qiu,et al.  On Image Registration in Magnetic Resonance Imaging , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[6]  Brian B. Avants,et al.  Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..

[7]  Peihua Qiu,et al.  Intensity-Based Image Registration by Nonparametric Local Smoothing , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Frédéric Dufaux,et al.  Efficient, robust, and fast global motion estimation for video coding , 2000, IEEE Trans. Image Process..

[9]  Robert B. Fisher,et al.  Estimating 3-D rigid body transformations: a comparison of four major algorithms , 1997, Machine Vision and Applications.

[10]  Tianzi Jiang,et al.  Fingerprint registration by maximization of mutual information , 2006, IEEE Transactions on Image Processing.

[11]  Jiaolong Yang,et al.  Go-ICP: Solving 3D Registration Efficiently and Globally Optimally , 2013, 2013 IEEE International Conference on Computer Vision.

[12]  R. J. Althof,et al.  A rapid and automatic image registration algorithm with subpixel accuracy , 1997, IEEE Transactions on Medical Imaging.

[13]  D. Rueckert,et al.  Comparison and evaluation of rigid, affine, and nonrigid registration of breast MR images. , 1999, Journal of computer assisted tomography.

[14]  Brian B. Avants,et al.  Directly Manipulated Free-Form Deformation Image Registration , 2009, IEEE Transactions on Image Processing.

[15]  Kuo-Chin Fan,et al.  Image Registration Using a New Edge-Based Approach , 1997, Comput. Vis. Image Underst..

[16]  Peihua Qiu,et al.  Feature based image registration using non-degenerate pixels , 2013, Signal Process..

[17]  A. Khotanzad,et al.  A physics-based coordinate transformation for 3-D image matching , 1997, IEEE Transactions on Medical Imaging.

[18]  Wei Pan,et al.  An Adaptable-Multilayer Fractional Fourier Transform Approach for Image Registration , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  B. S. Manjunath,et al.  A contour-based approach to multisensor image registration , 1995, IEEE Trans. Image Process..

[20]  N Saeed,et al.  Magnetic resonance image segmentation using pattern recognition, and applied to image registration and quantitation , 1998, NMR in biomedicine.

[21]  H. Boyaci,et al.  Statistical Analysis Methods for the fMRI Data , 2011 .

[22]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[23]  M. Daliri,et al.  RDLPFC area of the brain encodes sentence polarity: a study using fMRI , 2015, Brain Imaging and Behavior.

[24]  P. Qiu.  Image processing and jump regression analysis , 2005 .

[25]  Anand Rangarajan,et al.  Probability Density Estimation Using Isocontours and Isosurfaces: Applications to Information-Theoretic Image Registration , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Dinggang Shen,et al.  Learning-based deformable registration of MR brain images , 2006, IEEE Transactions on Medical Imaging.

[27]  Mohamed M. Fouad,et al.  Geometric image registration under arbitrarily-shaped locally variant illuminations , 2012, Signal Image Video Process..

[28]  Mohammad Reza Daliri,et al.  Software Tools for the Analysis of Functional Magnetic Resonance Imaging , 2012 .

[29]  Peihua Qiu,et al.  On Nonparametric Image Registration , 2013, Technometrics.

[30]  Bhu Dev Sharma,et al.  Advanced free-form deformation and Kullback–Lieblier divergence measure for digital elevation model registration , 2014, Signal, Image and Video Processing.

[31]  Jan Modersitzky,et al.  FAIR - Flexible Algorithms for Image Registration , 2009, Fundamentals of algorithms.

[32]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.