We present ennemi, a Python package for correlation analysis based on mutual information (MI). MI is measure of relationship between variables. Unlike Pearson it valid also non-linear relationships, yet in the linear case two are equivalent. The effect other variables can be removed like with partial correlation, same equivalence. These features make better exploratory many variable pairs. Our ...

Mutual Information (MI) and Normalised Mutual Information (NMI) have enjoyed success as image similarity measures in medical image registration. More recently, they have been used for non-rigid registration, most often evaluated empirically as functions of changing registration parameter. In this paper we present expressions derived from intensity histogram representations of these measures, fo...

Mutual information (MI) based registration methods are susceptible to the variation of the intensity of the image. We present a multi-modality MRI-CT non-rigid registration method by combining Kmeans clustering technique with mutual information. This method makes use of K-means clustering to determine variant bin sizes in CT image. The resulting clustered (labeled) CT image is non-rigidly regis...

This article highlights advantages of entropy-based genetic diversity measures, at levels from gene expression to landscapes. Shannon’s entropy-based diversity is the standard for ecological communities. The exponentials of Shannon’s and the related “mutual information” excel in their ability to express diversity intuitively, and provide a generalised method of considering microscopic behaviour...

This paper presents an information-theoretic analysis of security for data hiding methods based on spread spectrum. The security is quantified by means of the mutual information between the observed watermarked signals and the secret carrier (a.k.a. spreading vector) that conveys the watermark, a measure that can be used to bound the number of observations needed to estimate the carrier up to a...

This paper define a spatiotemporal mutual information on the pixels of a given video image on the basis of information theory (Shannon’s communication theory), which can be interpreted as the theoretical estimation of interested spots for human being. As an application of this spatiotemporal mutual information, we propose a method of producing a vivid video image of the distance learning by usi...

Influenza A virus is characterised by remarkable genome diversity in multiple virus strains. New strains are continuously evolved, occasionally leading to dangerous outbreaks and pandemics. Local RNA secondary structures were predicted in several regions of the influenza A virus genome. The conserved structures may be functional in either genomic negative-sense RNA (e.g. involved in vRNA packag...

Quantifying cooperation or synergy among random variables in predicting a single target random variable is an important problem in many complex systems. We review three prior information-theoretic measures of synergy and introduce a novel synergy measure defined as the difference between the whole and the union of its parts. We apply all four measures against a suite of binary circuits to demon...

The paper examines relationships between the Shannon entropy and the `α-norm for n-ary probability vectors, n ≥ 2. More precisely, we investigate the tight bounds of the `α-norm with a fixed Shannon entropy, and vice versa. As applications of the results, we derive the tight bounds between the Shannon entropy and several information measures which are determined by the `α-norm. Moreover, we app...

We propose a new characterization of inner and outer bounds of some network information theoretic regions in terms of upper concave envelopes of certain functions of mutual information. While this characterization is related to the characterization using auxiliary random variables, it is shown that the new characterization can make computations of boundary points much simpler. Further this repr...