When it comes to multimodal image registration, mutual information (MI) seems to hold the biggest market share. However, MI has a number of serious flaws, not the least of which is the quantization effect caused by histogram binning. We propose a novel registration method that, like MI, is motivated by the distribution of points in the joint intensity scatter plot (JISP). However, we avoid the ...

In this paper, we study an information theoretic approach to image similarity measurement for content-base image retrieval. In this novel scheme, similarities are measured by the amount of information the images contained about one another – mutual information (MI). The given approach is based on the premise that two similar images should have high mutual information, or equivalently, the query...

We explore mutual information (MI) as a means of characterizing linguistic statistical structure. The MI between two linguistic tokens x and y is the degree to which seeing x helps us anticipate the occurrence of y. We computed MI between words in 595 samples of written text in 25 languages. Our analyses indicate that MI dependencies do not extend beyond a range of five words. Moreover, the sim...

Maximising mutual information (MI) under various constraints has been suggested as a goal for neural networks in a perceptual system. Networks using Hebbian algorithms have been found to be suitable for optimising MI with either input or output noise. In this paper we show that a double feedback loop network, using local Hebbian algorithms, can approximate the characteristics required for optim...

BACKGROUND A new non-linear approach was applied to calculate the left ventricular ejection fraction (LVEF) using multigated acquisition (MUGA) images. METHODS In this study, 50 patients originally for the estimation of the percentage of LVEF to monitor the effects of various cardiotoxic drugs in chemotherapy were retrospectively selected. All patients had both MUGA and echocardiography exami...

This paper represents a further steps in combining Information Theory tools with relational learning. We show how mutual information can be used to find relevant relational features.

In this paper we study the distribution of words across the different parts of a book using tools from information theory. In particular, the mutual information between words in the text and parts of the text is compared with the mutual information of a shuffled version of the book. This analysis allows us to extract not only relevant words of the text but also relationships between the differe...

The mutual information (MI) of Gaussian multi-input multi-output (MIMO) channels has been evaluated by utilizing random matrix theory (RMT) and shown to asymptotically follow distribution, where the ergodic (EMI) converges a deterministic quantity. However, with non-Gaussian channels, there is bias between EMI its equivalent (DE), whose evaluation not available in literature. This related for t...

A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images, which is...