An innovative multimodal/multispectral image registration method for medical images based on the Expectation-Maximization algorithm

In this paper, we present a methodology for multimodal/ multispectral image registration of medical images. This approach is formulated by using the Expectation-Maximization (EM) methodology, such that we estimate the parameters of a geometric transformation that aligns multimodal/multispectral images. In this framework, the hidden random variables are associated to the intensity relations between the studied images, which allow to compare multispectral intensity values between images of different modalities. The methodology is basically composed by an iterative two-step procedure, where at each step, a new estimation of the joint conditional multispectral intensity distribution and the geometric transformation is computed. The proposed algorithm was tested with different kinds of medical images, and the obtained results show that the proposed methodology can be used to efficiently align multimodal/multispectral medical images.

[1]  Habib Zaidi,et al.  PET versus SPECT: strengths, limitations and challenges , 2008, Nuclear medicine communications.

[2]  Martin Styner,et al.  Parametric estimate of intensity inhomogeneities applied to MRI , 2000, IEEE Transactions on Medical Imaging.

[3]  A. Alavi,et al.  The Clinical Role of Fusion Imaging Using PET, CT, and MR Imaging. , 2008, PET clinics.

[4]  M. D. Di Carli,et al.  Hybrid SPECT/CT and PET/CT imaging: the next step in noninvasive cardiac imaging. , 2009, Seminars in nuclear medicine.

[5]  Qi Zhang,et al.  GPU-Based Visualization and Synchronization of 4-D Cardiac MR and Ultrasound Images , 2012, IEEE Transactions on Information Technology in Biomedicine.

[6]  Daniel U. Campos-Delgado,et al.  Affine image registration guided by particle filter , 2012 .

[7]  Giovanna Rizzo,et al.  Rigid Multimodal/Multispectral Image Registration Based on the Expectation-Maximization Algorithm , 2014, ISVC.

[8]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

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

[10]  Marios S. Pattichis,et al.  Robust Multispectral Image Registration Using Mutual-Information Models , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[11]  D. Louis Collins,et al.  Unbiased average age-appropriate atlases for pediatric studies , 2011, NeuroImage.

[12]  Mahua Bhattacharya,et al.  Affine-based registration of CT and MR modality images of human brain using multiresolution approaches: comparative study on genetic algorithm and particle swarm optimization , 2010, Neural Computing and Applications.

[13]  Daniel Rueckert,et al.  Nonrigid Registration of Medical Images: Theory, Methods, and Applications [Applications Corner] , 2010, IEEE Signal Processing Magazine.