Accurate inverse-consistent symmetric optical flow for 4D CT lung registration

Abstract Deformable image registration remains a challenging research area due to difficulties associated with local intensity variation and large motion. In this paper, an Accurate Inverse-consistent Symmetric Optical Flow (AISOF) method is proposed to overcome these difficulties. The two main contributions of AISOF include the following: (1) a coarse-to-fine strategy for an inverse-consistent symmetric method and (2) a novel Hybrid Local Binary Pattern (HLBP) to the classical Lucas–Kanade optical flow method. The HLBP consists of a median binary pattern and a generalised centre-symmetric local binary pattern. The generalised centre-symmetric local binary pattern has two thresholds, and this pattern can capture more information than the classical centre-symmetric local binary pattern, which has one threshold. The proposed HLBP can cope well with high contrast intensity and local intensity variation. Because the inverse-consistent symmetric method can reduce inverse consistency errors in Markov random fields based registration methods, we adopted this method to improve the accuracy of registration. In addition, a coarse-to-fine strategy was adopted to handle large motion. The proposed AISOF method was evaluated for 10 publicly available 4D CT lung datasets from the DIR-Lab. The mean target registration error of the AISOF method is 1.16 mm, which is significantly superior to the error of the classical Lucas–Kanade optical flow method, 2.83 mm. Moreover, this error is also the smallest of all unmasked registration methods using these datasets.

[1]  Michael Brady,et al.  MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration , 2012, Medical Image Anal..

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

[3]  David Dagan Feng,et al.  Non-Iterative Hierarchical Registration for Medical Images , 2009, J. Signal Process. Syst..

[4]  Laurent Risser,et al.  An implicit sliding-motion preserving regularisation via bilateral filtering for deformable image registration , 2014, Medical Image Anal..

[5]  Michael Brady,et al.  Towards Realtime Multimodal Fusion for Image-Guided Interventions Using Self-similarities , 2013, MICCAI.

[7]  Jan Rühaak,et al.  Combining Automatic Landmark Detection and Variational Methods for Lung CT Registration , 2013 .

[8]  Gösta H. Granlund,et al.  Optical Flow Based on the Inertia Matrix of the Frequency Domain , 1988 .

[9]  Heinz Handels,et al.  Non-parametric Discrete Registration with Convex Optimisation , 2014, WBIR.

[10]  Ramin Zabih,et al.  Non-parametric Local Transforms for Computing Visual Correspondence , 1994, ECCV.

[11]  H. Hirschmüller.  Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information , 2005, CVPR.

[12]  Jan Rühaak,et al.  A fast and accurate parallel algorithm for non-linear image registration using Normalized Gradient fields , 2014, ISBI.

[13]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[14]  Michael J. Black,et al.  The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields , 1996, Comput. Vis. Image Underst..

[15]  René Werner,et al.  TV-L1-Based 3D Medical Image Registration with the Census Cost Function , 2013, PSIVT.

[16]  Mingqiang Yang,et al.  Non-rigid registration of medical images with scale-space corner detection and thin-plate spline , 2012, Biomed. Signal Process. Control..

[17]  Liang Hu,et al.  Manifold‐based feature point matching for multi‐modal image registration , 2013, The international journal of medical robotics + computer assisted surgery : MRCAS.

[18]  Paul J Keall,et al.  Tumor and normal tissue motion in the thorax during respiration: Analysis of volumetric and positional variations using 4D CT. , 2007, International journal of radiation oncology, biology, physics.

[19]  Thomas Guerrero,et al.  Ventilation from four-dimensional computed tomography: density versus Jacobian methods , 2010, Physics in medicine and biology.

[20]  Simon Hermann.  Evaluation of Scan-Line Optimization for 3D Medical Image Registration , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Patrick Pérez,et al.  Hierarchical Estimation and Segmentation of Dense Motion Fields , 2002, International Journal of Computer Vision.

[22]  L. Xing,et al.  Overview of image-guided radiation therapy. , 2006, Medical dosimetry : official journal of the American Association of Medical Dosimetrists.

[23]  Dagan Feng,et al.  The adaptive FEM elastic model for medical image registration , 2014, Physics in medicine and biology.

[24]  Qi Li,et al.  Multimodality image registration using local linear embedding and hybrid entropy , 2013, Neurocomputing.

[25]  Marleen de Bruijne,et al.  Mass preserving image registration for lung CT , 2012, Medical Image Anal..

[26]  Rudolf Mester,et al.  Illumination-Robust Dense Optical Flow Using Census Signatures , 2011, DAGM-Symposium.

[27]  Jitendra Malik,et al.  Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Yaoqin Xie,et al.  Nonrigid Registration of Lung CT Images Based on Tissue Features , 2013, Comput. Math. Methods Medicine.

[29]  P. Anandan,et al.  A computational framework and an algorithm for the measurement of visual motion , 1987, International Journal of Computer Vision.

[30]  Nigel H. Lovell,et al.  Erratum to “Optimisation of a Generic Ionic Model of Cardiac Myocyte Electrical Activity” , 2013, Comput. Math. Methods Medicine.

[31]  D. Feng,et al.  Objective assessment of image registration results using statistical confidence intervals , 1999, 1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019).

[32]  Michael Brady,et al.  Phase mutual information as a similarity measure for registration , 2005, Medical Image Anal..

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

[34]  David Dagan Feng,et al.  Elastic Medical Image Registration Based on Image Intensity , 2001, VIP.

[35]  S. Vaughndill.  TUMOR AND NORMAL TISSUE MOTION IN THE THORAX DURING RESPIRATION: ANALYSIS OF VOLUMETRIC AND POSITIONAL VARIATIONS USING 4D CT , 2007 .

[36]  J. Rojo,et al.  Least median of squares filtering of locally optimal point matches for compressible flow image registration , 2012, Physics in medicine and biology.

[37]  Puneet Singla,et al.  Evaluation of advanced Lukas–Kanade optical flow on thoracic 4D-CT , 2013, Journal of Clinical Monitoring and Computing.

[38]  Marko Heikkilä,et al.  Description of interest regions with local binary patterns , 2009, Pattern Recognit..

[39]  René Werner,et al.  High Accuracy Optical Flow for 3D Medical Image Registration Using the Census Cost Function , 2013, PSIVT.

[40]  R. Castillo,et al.  A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets , 2009, Physics in medicine and biology.

[41]  Nassir Navab,et al.  Dense image registration through MRFs and efficient linear programming , 2008, Medical Image Anal..

[42]  Michael Brady,et al.  MRF-Based Deformable Registration and Ventilation Estimation of Lung CT , 2013, IEEE Transactions on Medical Imaging.

[43]  E. Hoffman,et al.  Mass preserving nonrigid registration of CT lung images using cubic B-spline. , 2009, Medical physics.

[44]  Yin Zhang,et al.  Compressible Image Registration for Thoracic Computed Tomography Images , 2009 .

[45]  Nassir Navab,et al.  Entropy and Laplacian images: Structural representations for multi-modal registration , 2012, Medical Image Anal..

[46]  Jan Rühaak,et al.  Highly accurate fast lung CT registration , 2013, Medical Imaging.

[47]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.