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A novel method for high-dimensional mutual information registration is proposed. This method first calculates high-dimensional mutual information matrix, and then calculates the entropy of that matrix. The maximal entropy corresponds to the optimal registration solution. The method was qualitatively and quantitatively evaluated on simulated and real brain(More)
As a similarity measure of medical image registration, f-information is studied. Mutual information is considered a special type of f-information. In order to reduce sensitivity to changes in overlap, two novel normalized I-alpha-information measures are proposed. The function curves, computational time and convergence are studied by applying these measures(More)
—Image decomposition technology is a very useful tool for image analysis. Images contain structural component and textural component which can be decomposed by variational methods such as VO (Vese-Osher) and OSV (Osher-Sole-Vese) models. OSV model is a powerful tool for image decomposition but the minimization is a hard problem because of solving the 4 th(More)
The mean divergence measures are used as the similarity measure of medical image registration. The square root arithmetic mean divergence (SAM), square root geometric mean divergence (SGM), square root harmonic mean divergence (SHM), arithmetic geometric mean divergence (AGM), and arithmetic harmonic mean divergence (AHM) are applied to rigid registration(More)
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