This paper presents a novel measure of image similarity, called quantitative-qualitative measure of mutual information (Q-MI), for multi-modal image registration. Conventional information measure, i.e., Shannon’s entropy, is a quantitative measure of information, since it only considers probabilities, not utilities of events. Actually, each event has its own utility to the fulfillment of the un...

Introduction: The purpose of multimodal and co-registration In MR Neuroimaging is to fuse two or more sets images (T1, T2, fMRI, DTI, pMRI, …) for combining the different information into a composite correlated data set in order to visualization, re-alignment and generating transform to functional Matrix. Multimodal registration and motion correction in spinal cord MR Neuroimag...

Mutual Information (MI) is a long studied measure of coding efficiency, and many attempts to apply it to population coding have been made. However, this is a computationally intractable task, and most previous studies redefine the criterion in forms of approximations. Recently we described properties of a simple lower bound on MI [2]. Here we describe the bound optimization procedure for learni...

Accurate detection of liver lesions is of great importance in hepatic surgery planning. Recent studies have shown that the detection rate of liver lesions is significantly higher in gadoxetic acid-enhanced magnetic resonance imaging (Gd-EOB-DTPA-enhanced MRI) than in contrast-enhanced portal-phase computed tomography (CT); however, the latter remains essential because of its high specificity, g...

mutual information (mi) is a widely used similarity metric for multimodality image registration. however, it involves an extremely high computational time especially when it is applied to volume images. moreover, its robustness is affected by existence of local maxima. the multi-resolution pyramid approaches have been proposed to speed up the registration process and increase the accuracy of th...

Because of the difficulty of obtaining an analytic expression for Bayes error, a wide variety of separability measures has been proposed for feature selection. In this paper, we show that there is a general framework based on the criterion of mutual information (MI) that can provide a realistic solution to the problem of feature selection for high-dimensional data. We give a theoretical argumen...

The module temperature is the most important parameter influencing the output power of solar photovoltaic (PV) systems, aside from solar irradiance. In this paper, we focus on the interdisciplinary research that combines the correlation analysis, mutual information (MI) and heat transfer theory, which aims to figure out the correlative relations between different meteorological impact factors (...

Abstract—I present several new relations between mutual information (MI) and statistical estimation error for a system that can be regarded simultaneously as a communication channel and as an estimator of an input parameter. I first derive a secondorder result between MI and Fisher information (FI) that is valid for sufficiently narrow priors, but arbitrary channels. A second relation furnishes...

Model-reality comparison can be viewed in a communications context, with the observed data the “sent message,” the model output “received message,” and the model the noisy channel over which the message is transmitted (Figure 1). Figure 1.Communications systemwith source producing H(X) and receiver seeing H(Y ). Amount of data communicated correctly from source to receiver is the mutual informa...