Registration of dynamic renal MR images using neurobiological model of saliency

In this paper we propose the use of a neurobiology-based saliency measure to improve the performance of a quantitative- qualitative measure of mutual information for rigid registration of 4D renal perfusion MR images. Our registration method assigns greater importance to more salient voxels by applying a soft thresholding function to normalized saliency values. The resulting saliency map is a better representation of what is truly visually salient than an entropy-based saliency map. Our tests on real patient datasets show that incorporating this saliency measure produces better registration results than traditional entropy-based approaches.

[1]  A Hasman,et al.  Movement correction of the kidney in dynamic MRI scans using FFT phase difference movement detection , 2001, Journal of magnetic resonance imaging : JMRI.

[2]  Ting Song,et al.  Automatic 4-D Registration in Dynamic MR Renography , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[3]  Dinggang Shen,et al.  Multimodality image registration by maximization of quantitative-qualitative measure of mutual information , 2008, Pattern Recognit..

[4]  Silviu Guiasu,et al.  A quantitative-qualitative measure of information in cybernetic systems (Corresp.) , 1968, IEEE Trans. Inf. Theory.

[5]  José M. F. Moura,et al.  Integrated registration of dynamic renal perfusion MR images , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[6]  C. Koch,et al.  A saliency-based search mechanism for overt and covert shifts of visual attention , 2000, Vision Research.

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

[8]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[9]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.