Two-dimensional gel electrophoresis image registration using block-matching techniques and deformation models.

Block-matching techniques have been widely used in the task of estimating displacement in medical images, and they represent the best approach in scenes with deformable structures such as tissues, fluids, and gels. In this article, a new iterative block-matching technique-based on successive deformation, search, fitting, filtering, and interpolation stages-is proposed to measure elastic displacements in two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) images. The proposed technique uses different deformation models in the task of correlating proteins in real 2D electrophoresis gel images, obtaining an accuracy of 96.6% and improving the results obtained with other techniques. This technique represents a general solution, being easy to adapt to different 2D deformable cases and providing an experimental reference for block-matching algorithms.

[1]  Hervé Delingette,et al.  Robust nonrigid registration to capture brain shift from intraoperative MRI , 2005, IEEE Transactions on Medical Imaging.

[2]  Xiaofeng Ren,et al.  Local grouping for optical flow , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Ruigang Yang,et al.  A Performance Study on Different Cost Aggregation Approaches Used in Real-Time Stereo Matching , 2007, International Journal of Computer Vision.

[4]  Babu Raman,et al.  Quantitative comparison and evaluation of two commercially available, two‐dimensional electrophoresis image analysis software packages, Z3 and Melanie , 2002, Electrophoresis.

[5]  Olga Veksler,et al.  Fast variable window for stereo correspondence using integral images , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[6]  Chen Rui,et al.  Hierarchical Support-weight Block Matching Approach in Depth Extraction , 2011 .

[7]  Richard Szeliski,et al.  A Database and Evaluation Methodology for Optical Flow , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[8]  Mohd. Aizaini Maarof,et al.  Efficient image block matching algorithm with two layer feature extraction , 2011, 2011 7th International Conference on Information Technology in Asia.

[9]  Karl Rohr,et al.  Geometric Alignment of 2D Gel Electrophoresis Images , 2008, Bildverarbeitung für die Medizin.

[10]  M. Celenk,et al.  A hybrid block-matching approach to motion estimation with adaptive search area , 2008, 2008 15th International Conference on Systems, Signals and Image Processing.

[11]  Carlos Fernandez-Lozano,et al.  Motion estimation in real deformation processes based on block-matching techniques , 2011, 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[12]  Julie Delon,et al.  Small Baseline Stereovision , 2007, Journal of Mathematical Imaging and Vision.

[13]  Graham R. Martin,et al.  Variable size block matching motion estimation with minimal error , 1996, Electronic Imaging.

[14]  Nira Dyn,et al.  Image Warping by Radial Basis Functions: Application to Facial Expressions , 1994, CVGIP Graph. Model. Image Process..

[15]  Gabriel Kiss,et al.  Performance optimization of block matching in 3D echocardiography , 2009, 2009 IEEE International Ultrasonics Symposium.

[16]  Javier Preciozzi,et al.  Processing of 2D Electrophoresis Gels , 2007, ICCV 2007.

[17]  Nahum Kiryati,et al.  Coarse to over-fine optical flow estimation , 2007, Pattern Recognit..

[18]  S Veeser,et al.  Multiresolution image registration for two‐dimensional gel electrophoresis , 2001, Proteomics.

[19]  Richard Szeliski,et al.  Handling occlusions in dense multi-view stereo , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[20]  Driss Aboutajdine,et al.  Fast block matching algorithms using frequency domain , 2011, 2011 International Conference on Multimedia Computing and Systems.

[21]  Anthony G. Constantinides,et al.  Variable size block matching motion compensation with applications to video coding , 1990 .

[22]  M J Dunn,et al.  Bovine dilated cardiomyopathy: Proteomic analysis of an animal model of human dilated cardiomyopathy , 1999, Electrophoresis.

[23]  Karl Rohr,et al.  Elastic registration of electrophoresis images using intensity information and point landmarks , 2004, Pattern Recognit..

[24]  Jaydev K Dave,et al.  Static and Dynamic Cumulative Maximum Intensity Display Mode for Subharmonic Breast Imaging , 2010, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[25]  Bilge Karaçali.  Information Theoretic Deformable Registration Using Local Image Information , 2006, International Journal of Computer Vision.

[26]  Juan R. Rabuñal,et al.  Optical Analysis of Strength Tests Based on Block‐Matching Techniques , 2012, Comput. Aided Civ. Infrastructure Eng..

[27]  Emanuele Trucco,et al.  Symmetric Stereo with Multiple Windowing , 2000, Int. J. Pattern Recognit. Artif. Intell..

[28]  Heung-Kyu Lee,et al.  Block-matching algorithm based on an adaptive reduction of the search area for motion estimation , 2000, Real Time Imaging.

[29]  Michael J. Black,et al.  Skin and bones: multi-layer, locally affine, optical flow and regularization with transparency , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[30]  Thomas Brox,et al.  High Accuracy Optical Flow Estimation Based on a Theory for Warping , 2004, ECCV.

[31]  Til Aach,et al.  Multiple-Motion-Estimation by Block-matching using MRF , 2004 .

[32]  Nico de Jong,et al.  Automated tracking of the mitral valve annulus motion in apical echocardiographic images using multidimensional dynamic programming. , 2007, Ultrasound in medicine & biology.

[33]  Fred L. Bookstein,et al.  Principal Warps: Thin-Plate Splines and the Decomposition of Deformations , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  In-So Kweon,et al.  Adaptive Support-Weight Approach for Correspondence Search , 2006, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  Olivier Langella,et al.  A method based on bead flows for spot detection on 2‐D gel images , 2008, Proteomics.

[36]  K. Mardia,et al.  A review of image-warping methods , 1998 .

[37]  T. Rabilloud,et al.  Two-dimensional gel electrophoresis in proteomics: Past, present and future. , 2010, Journal of proteomics.

[38]  Thomas Brox,et al.  Universität Des Saarlandes Fachrichtung 6.1 – Mathematik Highly Accurate Optic Flow Computation with Theoretically Justified Warping Highly Accurate Optic Flow Computation with Theoretically Justified Warping , 2022 .

[39]  M J Dunn,et al.  Changes in myocardial protein expression in pacing‐induced canine heart failure , 1999, Electrophoresis.

[40]  Walid Aoudi,et al.  Parametric Deformable Block Matching for Ultrasound Imaging , 2007, 2007 IEEE International Conference on Image Processing.

[41]  Guang-Zhong Yang,et al.  Automated image alignment for 2D gel electrophoresis in a high-throughput proteomics pipeline , 2008, Bioinform..

[42]  C Thieke,et al.  An enhanced block matching algorithm for fast elastic registration in adaptive radiotherapy , 2006, Physics in medicine and biology.

[43]  Carsten Rother,et al.  Fast cost-volume filtering for visual correspondence and beyond , 2011, CVPR 2011.

[44]  Markus Raffel,et al.  Particle Image Velocimetry: A Practical Guide , 2002 .

[45]  Cordelia Schmid,et al.  The Geometry and Matching of Lines and Curves Over Multiple Views , 2000, International Journal of Computer Vision.

[46]  Dengsheng Zhang,et al.  An edge and color oriented optical flow estimation using block matching , 2000, WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.

[47]  Xiaojing Shen,et al.  Block Matching Algorithm Based on Particle Swarm Optimization for Motion Estimation , 2008, 2008 International Conference on Embedded Software and Systems.

[48]  Javier Preciozzi,et al.  Processing of 2 D Electrophoresis Gels ? , 2007 .

[49]  T Voss,et al.  Observations on the reproducibility and matching efficiency of two‐dimensional electrophoresis gels: Consequences for comprehensive data analysis , 2000, Electrophoresis.

[50]  Sébastien Ourselin,et al.  Block Matching: A General Framework to Improve Robustness of Rigid Registration of Medical Images , 2000, MICCAI.

[51]  Olivier Commowick,et al.  Block-matching strategies for rigid registration of multimodal medical images , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[52]  Xiong Shi,et al.  Image mosaics algorithm based on feature-block matching , 2011, 2011 International Conference on Multimedia Technology.

[53]  Michael J. Black,et al.  Learning Optical Flow , 2008, ECCV.

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

[55]  Michael J. Black,et al.  Secrets of optical flow estimation and their principles , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[56]  Injong Rhee,et al.  Quadtree-structured variable-size block-matching motion estimation with minimal error , 2000, IEEE Trans. Circuits Syst. Video Technol..

[57]  Takeo Kanade,et al.  A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[58]  Robert D. Howe,et al.  Fast block flow tracking of atrial septal defects in 4D echocardiography , 2008, Medical Image Anal..

[59]  Daniel Schwarz,et al.  Multilevel block matching technique with the use of generalized partial volume interpolation for nonlinear intersubject registration of MRI brain images , 2008, 2008 16th European Signal Processing Conference.