Volume image registration by cross-entropy optimization

IEEE Transactions on Medical Imaging
Yang-Ming Zhu

Abstract

Cross-entropy (CE), an information-theoretic measure, quantifies the difference between two probability density functions. This measure is applied to volume image registration. When a good prior estimation of the joint distribution of the voxel values of two images in registration is available, the CE can be minimized to find an optimal registration. If such a prior estimation is not available, one seeks the registration which gives a joint distribution different from unlikely ones as much as possible, i.e., the CE is maximized to find an optimal registration. When the unlikely distribution is a uniform one, CE maximization reduces to joint entropy minimization; when the unlikely distribution is proportional to one of the marginal distributions, it reduces to conditional entropy minimization; when the unlikely distribution is the product of two marginal distributions, it degenerates to mutual-information maximization. These different CEs are added together and are used as criteria for image registration. The accuracy and robustness of this new approach are tested and compared using a likely joint distribution and various unlikely joint distributions and their combinations.

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Citations

Jun 18, 2003·Academic Radiology·Zhenghong LeeDavid L Wilson
Feb 6, 2008·The Journal of the Acoustical Society of America·Jonathan MamouJames F Zachary
Apr 17, 2007·Computer Methods and Programs in Biomedicine·Yang-Ming Zhu
Aug 9, 2003·IEEE Transactions on Medical Imaging·Josien P W PluimMax A Viergever
May 27, 2010·IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society·Kumaradevan PunithakumarShuo Li
Dec 4, 2004·IEEE Transactions on Medical Imaging·Josien P W PluimMax A Viergever
Apr 25, 2006·Computers in Biology and Medicine·Peng Xu, DeZhong Yao

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