Entropy-based Similarity Measure Research Articles

ENT3C: an entropy-based similarity measure for Hi-C and micro-C derived contact matrices.

Hi-C and micro-C sequencing have shed light on the profound importance of 3D genome organization in cellular function by probing 3D contact frequencies across the linear genome. The resulting contact matrices are extremely sparse and susceptible to technical- and sequence-based biases, making their comparison challenging. The development of reliable, robust and efficient methods for quantifying similarity between contact matrices is crucial for investigating variations in the 3D genome organization in different cell types or under different conditions, as well as evaluating experimental reproducibility. We present a novel method, ENT3C, which measures the change in pattern complexity in the vicinity of contact matrix diagonals to quantify their similarity. ENT3C provides a robust, user-friendly Hi-C or micro-C contact matrix similarity metric and a characteristic entropy signal that can be used to gain detailed biological insights into 3D genome organization.

Modeling a domain wall network in BiFeO3 with stochastic geometry and entropy-based similarity measure

A compact and tractable two-dimensional model to generate the topological network structure of domain walls in BiFeO3 thin films is presented in this study. Our method combines the stochastic geometry parametric model of the centroidal Voronoi tessellation optimized using the von Neumann entropy, a novel information-theoretic tool for networks. The former permits the generation of image-based stochastic artificial samples of domain wall networks, from which the network structure is subsequently extracted and converted to the graph-based representation. The von Neumann entropy, which reflects information diffusion across multiple spatiotemporal scales in heterogeneous networks, plays a central role in defining a fitness function. It allows the use of the network as a whole rather than using a subset of network descriptors to search for optimal model parameters. The optimization of the parameters is carried out by a genetic algorithm through the maximization of the fitness function and results in the desired graph-based network connectivity structure. Ground truth empirical networks are defined, and a dataset of network connectivity structures of domain walls in BiFeO3 thin films is undertaken through manual annotation. Both a versatile tool for manual network annotation of noisy images and a new automatic network extraction method for high-quality images are developed.

Managing consistency and consensus measures and adjustment strategies in group decision making with probabilistic linguistic preference relations

The use of probabilistic linguistic preference relations (PLPRs) in pairwise comparisons enhances the flexibility of quantitative decision making. To promote the application of probabilistic linguistic term sets (PLTSs) and PLPRs, this paper introduces the consistency and consensus measures and adjustment strategies to guarantee the rationality of preference information utilized in the group decision making process. First of all, a novel entropy-based similarity measure is developed with PLTSs. Hereafter an improved consistency measure is defined on the basis of the proposed similarity measure, and a convergent algorithm is constructed to deal with the consistency improving process. Furthermore, a similarity-based consensus measure is developed in a given PLPR, and the consensus reaching process is presented to deal with the unacceptable consensus degree. The proposed consistency improving and consensus reaching processes follow a principle of minimum information loss, called a local adjustment strategy. In particular, the presented methods not only overcome the deficiencies in existing studies but also enhance the interpretation and reduce the complexity of the group decision making process. Finally, the proposed consistency measure and improving process, as well as consensus measure and reaching process are verified through a numerical example for the medical plan selection issue. The result and in-depth comparison analysis validate the feasibility and effectiveness of the proposed methods.

New Entropy-Based Similarity Measure between Interval-Valued Intuitionstic Fuzzy Sets

In this paper, we propose a new approach to constructing similarity measures using the entropy measure for Interval-Valued Intuitionistic Fuzzy Sets. In addition, we provide several illustrative examples to demonstrate the practicality and effectiveness of the proposed formula. Finally, we use the new proposed similarity measure to develop a new approach for solving problems of pattern recognition and multi-criteria fuzzy decision-making.

Information-Theoretic Remodularization of Object-Oriented Software Systems

Software remodularization consists in reorganizing software entities into modules such that pairs of entities belonging to the same modules are more similar than those belonging to different modules. In recent years, Search-Based Software Engineering (SBSE) approach has gained unprecedented growth for solving software remodularization problem. Most of the previous studies remodularize the software system by optimizing the structural coupling and cohesion metrics as objective functions. These metrics are defined in terms of the number of structural relationships counts, rather than taking patterns of relationships. It has been observed that the computation of coupling and cohesion based on patterns of relationships (i.e., information-theory based) are more accurate than the number of relationships. This paper proposes an information-theoretic software remodularization where an entropy-based similarity measure is introduced as an objective function along with other objective functions i.e., inter-module class change coupling, intra-module class change coupling, module size index (MSI), and module count index (MCI) and is further optimized using many-objective meta-heuristic algorithm. To evaluate the effectiveness of the proposed approach, seven object-oriented software systems have been remodularized using NSGA-III, MOEA/D, IBEA, and TAA algorithms. The results are compared with existing multi-objective formulation of remodularization problem in terms of authoritative software remodularization, non-extreme distribution, and stability. The experimentation results suggest that the proposed approach can be a good alternative to improve the quality of software systems. The findings suggest that the approach is more suitable for generating remodularization solution good from both quality metrics and developers perspective.

RMI based non-rigid image registration using BF-QPSO optimization and P-spline

Registration is an image processing technique which aligns two or more images spatially and produces an informative image. Regional mutual information is an entropy based similarity measure that is invariant to the overlapped regions of the two images to be registered. But, it is difficult for images having deformation due to organ motion with a non-convex and irregular shape. In this paper, nonrigid image registration problem is addressed for multimodal medical images where P-spline interpolation method is adopted for modeling the transformation parameters. Intensity based registration requires optimization of similarity metric. As function of these parameters is complex, local optimization techniques frequently fail, which necessitates global optimization methods. Quantum-behaved particle swarm optimization (QPSO) requires fewer parameters to control and outperforms particle swarm optimization (PSO) in search ability. Bacterial Foraging Algorithm (BFA) is a non gradient global optimization technique where E. coli bacteria depends on random search direction during chemotaxis, which delays the rate of convergence. Here, a new algorithm is proposed hybridizing the notion of QPSO and BFA for tradeoff between local and global search. P-spline interpolated image registration is tested with 10 sets of MR images and some other medical images using RMI similarity index optimized by BF-QPSO. The proposed technique is compared with BFA, QPSO and hybrid BF-PSO algorithms.

Evaluation of information‐theoretic similarity measures for content‐based retrieval and detection of masses in mammograms

The purpose of this study was to evaluate image similarity measures employed in an information-theoretic computer-assisted detection (IT-CAD) scheme. The scheme was developed for content-based retrieval and detection of masses in screening mammograms. The study is aimed toward an interactive clinical paradigm where physicians query the proposed IT-CAD scheme on mammographic locations that are either visually suspicious or indicated as suspicious by other cuing CAD systems. The IT-CAD scheme provides an evidence-based, second opinion for query mammographic locations using a knowledge database of mass and normal cases. In this study, eight entropy-based similarity measures were compared with respect to retrieval precision and detection accuracy using a database of 1820 mammographic regions of interest. The IT-CAD scheme was then validated on a separate database for false positive reduction of progressively more challenging visual cues generated by an existing, in-house mass detection system. The study showed that the image similarity measures fall into one of two categories; one category is better suited to the retrieval of semantically similar cases while the second is more effective with knowledge-based decisions regarding the presence of a true mass in the query location. In addition, the IT-CAD scheme yielded a substantial reduction in false-positive detections while maintaining high detection rate for malignant masses.

Fast BlockMatching Registration with Entropy-based Similarity

This paper describes the implementation of a multidimensional block-matching nonrigid registration algorithm. The main features of the algorithm are its simplicity, its free form nature, the modularity of the similarity measure, which makes it possible using local entropy-based similarity measures and the avoidance of the optimization module. The algorithm implementation described in this paper is based on the method by Suarez et al. This paper, which has already been submitted to the Insight Journal, is accompanied with the source code, input data, parameters and output data used for validating the algorithm described in it.

RIGID MEDICAL IMAGE REGISTRATION AND ITS ASSOCIATION WITH MUTUAL INFORMATION

Image registration plays a crucial role in the computer vision and medical imaging field where it is used to develop a spatial mapping between different sets of data. These transformations can range from simple rigid registrations to complex nonrigid deformations. Mutual information (MI) is a popular entropy-based similarity measure which has recently experienced a prolific expansion in a number of image registration applications. Stemming from information theory, this measure generally outperforms most other intensity-based measures in multimodal applications as it only assumes a statistical dependence between images. This paper provides a thorough introduction to the MI measure and its use in rigid medical image registration. A look at the extensions proposed to the original measure will also be provided. These were developed to improve the robustness of the measure and to avoid certain cases when maximizing MI does not lead to the correct spatial alignment.