Automatic B-spline image registration using histogram-based landmark extraction

Recognition and correction of inhomogeneous displacement caused by patient's movement has been recently discussed as an interesting topic in medical image processing. Considering consistency in general structure of the image during distortion, histogram could be employed as a fast implementation method in feature domain. Accordingly, attribute vectors could be defined for each pixel based on spatial features to find corresponding points in two images. Consequently a point-based and non-rigid transformation approach will be designed. A B-spline image registration has been applied to match those pairs with a defined smoothness factor. This algorithm is a step-by-step registration process controlled by this factor. The proposed algorithm has been applied to brain MR images. The normal mean square error value has been measured between registered and original images and the result shows a significant improvement in the proposed algorithm.

[1]  Haralampos Karanikas,et al.  A Pattern Similarity Scheme for Medical Image Retrieval , 2009, IEEE Transactions on Information Technology in Biomedicine.

[2]  Albert C. S. Chung,et al.  Multi-dimensional Mutual Information Based Robust Image Registration Using Maximum Distance-Gradient-Magnitude , 2005, IPMI.

[3]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[4]  Roman M. Palenichka,et al.  Automatic Extraction of Control Points for the Registration of Optical Satellite and LiDAR Images , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[5]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Grace Wahba,et al.  Spline Models for Observational Data , 1990 .

[7]  Curt H. Davis,et al.  Pixel-Based Invariant Feature Extraction and its Application to Radiometric Co-Registration for Multi-Temporal High-Resolution Satellite Imagery , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  Emad Fatemizadeh,et al.  MMRO: A Feature Selection Criterion for MR Images Based on Alpha Stable Filter Responses , 2011, 2011 7th Iranian Conference on Machine Vision and Image Processing.

[9]  Robert T. Schultz,et al.  A Unified Feature Registration Method for Brain Mapping , 2001, IPMI.

[10]  Karl Rohr,et al.  Image Registration Based on Thin-Plate Splines and Local Estimates of Anisotropic Landmark Localization Uncertainties , 1998, MICCAI.

[11]  R. Bajcsy,et al.  A computerized system for the elastic matching of deformed radiographic images to idealized atlas images. , 1983, Journal of computer assisted tomography.

[12]  Shu Liao,et al.  Feature Based Nonrigid Brain MR Image Registration With Symmetric Alpha Stable Filters , 2010, IEEE Transactions on Medical Imaging.

[13]  Dinggang Shen,et al.  Image registration by local histogram matching , 2007, Pattern Recognit..

[14]  Christos Davatzikos,et al.  Hierarchical Matching of Cortical Features for Deformable Brain Image Registration , 1999, IPMI.

[15]  Dinggang Shen,et al.  HAMMER: hierarchical attribute matching mechanism for elastic registration , 2002, IEEE Transactions on Medical Imaging.