Mutual information extremal optimization for multimodal medical image registration

In this paper it is considered the image registration (IR) between medical images of computed tomography and magnetic resonance. Our approach formulates the IR as an optimization problem where mutual information cost function is used as a similarity metric (cost function). The Extremal Optimization algorithm is implemented as the optimizer. The numerical results are contrasted against two state of the art optimization algorithms for this kind of problems (being one deterministic and another evolutionary). Our approach is competitive with the deterministic algorithm in accuracy and with the evolutionary algorithms in computational cost. The qualitative results are quite satisfactory with a 83 % of success, whilst the quantitative results present an average error of 0.36mm with registrations of CT with proton density MR. The results show that the proposal is useful for multimodal registrations.

[1]  A. Percus,et al.  Nature's Way of Optimizing , 1999, Artif. Intell..

[2]  V.R.S Mani,et al.  Survey of Medical Image Registration , 2013 .

[3]  Manuel Desco,et al.  PET, CT, and MR image registration of the rat brain and skull , 2001 .

[4]  Max A. Viergever,et al.  Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.

[5]  Stefan Boettcher,et al.  Extremal Optimization: an Evolutionary Local-Search Algorithm , 2002, ArXiv.

[6]  D. Hill,et al.  Registration of MR and CT images for skull base surgery using point-like anatomical features. , 1991, The British journal of radiology.

[7]  Alan C. Evans,et al.  MRI-PET Correlation in Three Dimensions Using a Volume-of-Interest (VOI) Atlas , 1991, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[8]  Paul A. Viola,et al.  Multi-modal volume registration by maximization of mutual information , 1996, Medical Image Anal..

[9]  C. Pelizzari,et al.  Accurate Three‐Dimensional Registration of CT, PET, and/or MR Images of the Brain , 1989, Journal of computer assisted tomography.

[10]  David R. Haynor,et al.  PET-CT image registration in the chest using free-form deformations , 2003, IEEE Transactions on Medical Imaging.

[11]  D. Hill,et al.  Medical image registration , 2001, Physics in medicine and biology.

[12]  C. Pelizzari,et al.  Clinical validation of SPECT and CT/MRI image registration in radiolabeled monoclonal antibody studies of colorectal carcinoma. , 1994, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[13]  Terry M. Peters,et al.  Three-dimensional multimodal image-guidance for neurosurgery , 1996, IEEE Trans. Medical Imaging.

[14]  Max A. Viergever,et al.  Image registration by maximization of combined mutual information and gradient information , 2000, IEEE Transactions on Medical Imaging.

[15]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[16]  David R. Haynor,et al.  Nonrigid multimodality image registration , 2001, SPIE Medical Imaging.

[17]  James C. Gee.  Performance evaluation of medical image processing algorithms , 2000, Medical Imaging: Image Processing.

[18]  Colin Studholme,et al.  An overlap invariant entropy measure of 3D medical image alignment , 1999, Pattern Recognit..

[19]  R. Mccoll,et al.  Spatial and temporal registration of cardiac SPECT and MR images: methods and evaluation. , 1991, Radiology.

[20]  Nikos Paragios,et al.  Deformable Medical Image Registration: A Survey , 2013, IEEE Transactions on Medical Imaging.

[21]  David J. Hawkes,et al.  Voxel similarity measures for 3-D serial MR brain image registration , 1999, IEEE Transactions on Medical Imaging.

[22]  Ramin Shahidi,et al.  Validation of medical image processing in image-guided therapy , 2002, IEEE Transactions on Medical Imaging.

[23]  Mohamed Batouche,et al.  Ant colony system with extremal dynamics for point matching and pose estimation , 2002, Object recognition supported by user interaction for service robots.

[24]  David M. Raup,et al.  How Nature Works: The Science of Self-Organized Criticality , 1997 .

[25]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[26]  Mohamed Batouche,et al.  Robust Point Correspondence for Image Registration Using Optimization with Extremal Dynamics , 2002, DAGM-Symposium.

[27]  Stefan Boettcher,et al.  Optimization with extremal dynamics , 2003, Complex..

[28]  Frank Y. Shih,et al.  Image Segmentation , 2007, Encyclopedia of Biometrics.

[29]  Bak,et al.  Punctuated equilibrium and criticality in a simple model of evolution. , 1993, Physical review letters.

[30]  H. K. Abhyankar,et al.  Image Registration Techniques: An overview , 2009 .

[31]  T. Greitz,et al.  Head fixation system for integration of radiodiagnostic and therapeutic procedures , 2004, Neuroradiology.

[32]  Jason Brownlee,et al.  Clever Algorithms: Nature-Inspired Programming Recipes , 2012 .

[33]  A. El-Baz.  Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies: Volume 1 , 2016 .

[34]  A. Ardeshir Goshtasby,et al.  2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications , 2005 .