title: Volume image registration by cross-entropy optimization.
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.
authors.affiliation: Nuclear Medicine Division, Philips Medical Systems (formerly Marconi Medical Systems), Cleveland, OH 44143, USA. yzhu@computer.org
authors.forename: Yang-Ming
authors.initials: YM
authors.lastname: Zhu
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D003952:Diagnostic Imaging / Q000379:methods; D005069:Evaluation Studies as Topic; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D009333:Neck / Q000033:anatomy & histology / Q000000981:diagnostic imaging; D010363:Pattern Recognition, Automated; D011336:Probability; D011877:Radionuclide Imaging; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D013909:Thorax / Q000033:anatomy & histology / Q000000981:diagnostic imaging