My recent paper on the subject defined above is pressed in Elsevier Pattern Recognition Letters. Earlier version of this paper was published in IEEE SIBGRAPI conference in Belo Horizonte, Brazil in 2007. SIBGRAPI is the most important Brazilian conference on Computer Graphics, Image Processing and Computer Vision. Fourteen best evaluated papers were invited for the submission to the journal of pattern recognition letters. Seven of them are finally published in the special issue of Pattern Recognition Letters, Volume 31, Issue 4, March 2010. My paper is one of those seven. I strictly recommend it to be read and used in registration process of medical data by experts.
Acquisition-to-acquisition signal intensity variations (non-standardness) are inherent in MR images. Standardization is a post processing method for correcting inter-subject intensity variations through transforming all images from the given image gray scale into a standard gray scale wherein similar intensities achieve similar tissue meanings. The lack of a standard image intensity scale in MRI leads to many difficulties in tissue characterizability, image display, and analysis, including image segmentation. The influence of standardization on these tasks has been documented well; however, effects of standardization on medical image registration have not been studied yet. In this paper, we investigate the role of intensity standardization in registration tasks with systematic and analytic evaluations involving clinical MR images. We conducted nearly 20,000 clinical MR image registration experiments and evaluated the quality of registrations both quantitatively and qualitatively. The evaluations show that intensity variations between images degrades the accuracy of registration performance. The results imply that the accuracy of image registration not only depends on spatial and geometric similarity but also on the similarity of the intensity values for the same tissues in different images.
Bagci, U., Udupa, J.K., Bai, L. ” The Role of Intensity Standardization in Medical Image Registration.” Pattern Recognition Letters, Vol. 31 (4), pp.315-323, March 2010.