Significados de mutual information en inglés

Spatial registration was performed in 3D image space using a mutual information-based approach.

We created a slice-to-volume mutual information registration algorithm with special features to improve robustness.

The normalized mutual information (NMI) method was used for image registration.

In the fine registration, a local B-spline FFD model with normalized mutual information gradient is employed.

We introduced mutual information measures which provide access to nonlinear interdependencies as counterpart to the classically linear correlation analysis.

The independent components were estimated by recursively minimizing the mutual information (measure of dependence) between the components.

The rigid body registration method combines the advantages of mutual information (MI) and correlation coefficient at different resolutions.

Measures of signal complexity and interactions were calculated over multiple time scales, including sample entropy, mutual information, and transfer entropy.

Co-registration was performed using mutual information (MI) as a similarity measure of the matching of the acquired images.

Reproducibility was evaluated on an ellipsoidal phantom by calculating the residual sum of squares, zero-mean normalized cross-correlation, and mutual information.

Here we use single cell signaling measures to calculate mutual information as a measure of information transfer via gonadotropin-releasing h …

Information theory, or more specifically mutual information, provides a method to identify those basepairs that are key to the secondary structure.

The automatic slice-to-volume mutual information registration algorithm is robust and probably sufficiently accurate to aid in iMRI-guided treatment of prostate cancer.

Conclusion: We provide a novel algorithm that uses mutual information to identify the key basepairs that lead to a multimodal Boltzmann distribution.

These correlations can be studied using mutual information because it measures the amount of information one random variable contains about the other.

We find that corpus-dependent bag of words approach with mutual information between word and emotion dimensions is by far the best representation.