Spatial Mutual Information as Similarity Measure for 3D Brain Image Registration
Information theoretic-based similarity measures, in particular mutual information, are widely used for intermodal/intersubject 3D brain image registration. However, conventional mutual information does not take into account spatial dependency between adjacent voxels in images thus reducing its efficacy as a similarity measure in image registration. This paper first presents a review of the existing attempts to incorporate spatial dependency into the computation of mutual information. Then, a recently introduced spatially dependent similarity measure, named spatial mutual information, is extended to 3D brain image registration. This extension also eliminates its artifact for translational misregistration. Finally, the effectiveness of the proposed 3D spatial mutual information as a similarity measure is compared with three existing mutual information measures by applying controlled levels of noise degradation to 3D simulated brain images.
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See complete bios of the authors in the full version of this article.
Dr. Razlighi is currently Assistant Professor in the Department of Neurology and Adjunct Assistance Professor in the Department of Biomedical Engineering at Columbia University. He was a Senior System Engineer at Alcatel Lucent, Plano TX, USA, before joining Columbia University. His research interests lie in the broad areas of signal and image processing with application in neuroimaging.
Dr. Kehtarnavaz is a Professor of electrical engineering and the Director of the Signal and Image Processing Laboratory at the University of Texas at Dallas. His research interests include digital signal and image processing, real-time signal and image processing, pattern recognition, and biomedical image analysis. He has authored or co-authored eight books and more than 250 papers related to these areas.