Histogram Distance Research Articles

Multi-site Study of Diffusion Metric Variability: Characterizing the Effects of Site, Vendor, Field Strength, and Echo Time using the Histogram Distance.

MRI-based multi-site trials now routinely include some form of diffusion-weighted imaging (DWI) in their protocol. These studies can include data originating from scanners built by different vendors, each with their own set of unique protocol restrictions, including restrictions on the number of available gradient directions, whether an externally-generated list of gradient directions can be used, and restrictions on the echo time (TE). One challenge of multi-site studies is to create a common imaging protocol that will result in a reliable and accurate set of diffusion metrics. The present study describes the effect of site, scanner vendor, field strength, and TE on two common metrics: the first moment of the diffusion tensor field (mean diffusivity, MD), and the fractional anisotropy (FA). We have shown in earlier work that ROI metrics and the mean of MD and FA histograms are not sufficiently sensitive for use in site characterization. Here we use the distance between whole brain histograms of FA and MD to investigate within- and between-site effects. We concluded that the variability of DTI metrics due to site, vendor, field strength, and echo time could influence the results in multi-center trials and that histogram distance is sensitive metrics for each of these variables.

Structural-Function Study of Membrane Proteins with Restrained-Ensemble and Dummy Spin-Label Molecular Dynamics Simulations

Membrane proteins perform numerous physiological functions that are critical for human health. DEER (Double Electron Electron Resonance) spectroscopy is playing an increasingly important role in characterizing the conformational states of these proteins. DEER is a powerful-pulsed EPR technique allowing the determination of spin-spin distance histograms between site-directed nitroxide label sites on a protein. Combining the DEER structural data with molecular dynamics (MD) simulation methods can be a powerful strategy to study membrane protein structure and function.

Unified framework for human activity recognition: An approach using spatial edge distribution and ℜ-transform

In this paper, a unified approach for the recognition of human activity using the spatial edge distribution of gradients and orientation of the human silhouettes in a video sequence is presented. The spatial edge distribution is computed on still image at different levels of resolution of sub-images to extract out the shape of the activity posture. The fuzzy trapezoidal membership function is used to extract the key frames of the activity, and the single still key image is extracted according to the histogram distance. The temporal content of the activity is extracted by the computation of orientation of the silhouettes using ℜ-transform. The ℜ-transform is applied on the binary human silhouettes, and the extraction of human silhouettes from the video sequence is done using texture based segmentation techniques. The high dimensionality of the ℜ-transform features is handled by applying Local linear embedding (LLE) dimension reduction approach. A unified model is constructed by integrating the spatial edge distribution of gradients and temporal content of the activity. The performance of the developed model is demonstrated on publicly available datasets, and the highest classification accuracy achieved on each datasets is compared with the similar state-of-the-art techniques and shows the superior performance.

A proposed unified framework for the recognition of human activity by exploiting the characteristics of action dynamics

The aim of this paper is to present a novel integrated framework for the recognition of human actions using a spatial distribution of edge gradient (SDEG) of human pose and detailed geometric orientation of a human silhouette in a video sequence. The combined descriptor endows a wealthy feature vector dictionary having both the appearance and angular kinematics information that significantly wraps the local and global information and provides discriminative depiction for the action recognition. The SDEG is computed on a still image at different levels of resolution of sub-images, and still images of the human poses are extracted from the input video sequence using fuzzy trapezoidal membership function based on the normalized histogram distance between the contiguous segment frames. The change of geometric orientation of human silhouette with time is computed using normalized R-Transform. To validate the performance of the proposed approach, extensive experiments are conducted on five publicly available human action datasets i.e. Weizmann, KTH, Ballet Movements, Multi-view i3dPost, and IXMAS. The recognition accuracy achieved on these datasets demonstrates that the proposed approach has an abundant discriminating power of recognizing the variety of actions. Moreover, the proposed approach yields superior results when compared with similar state-of-the-art methods.

MIA: Mutual Information Analyzer, a graphic user interface program that calculates entropy, vertical and horizontal mutual information of molecular sequence sets.

BackgroundShort and long range correlations in biological sequences are central in genomic studies of covariation. These correlations can be studied using mutual information because it measures the amount of information one random variable contains about the other. Here we present MIA (Mutual Information Analyzer) a user friendly graphic interface pipeline that calculates spectra of vertical entropy (VH), vertical mutual information (VMI) and horizontal mutual information (HMI), since currently there is no user friendly integrated platform that in a single package perform all these calculations. MIA also calculates Jensen-Shannon Divergence (JSD) between pair of different species spectra, herein called informational distances. Thus, the resulting distance matrices can be presented by distance histograms and informational dendrograms, giving support to discrimination of closely related species.ResultsIn order to test MIA we analyzed sequences from Drosophila Adh locus, because the taxonomy and evolutionary patterns of different Drosophila species are well established and the gene Adh is extensively studied. The search retrieved 959 sequences of 291 species. From the total, 450 sequences of 17 species were selected. With this dataset MIA performed all tasks in less than three hours: gathering, storing and aligning fasta files; calculating VH, VMI and HMI spectra; and calculating JSD between pair of different species spectra. For each task MIA saved tables and graphics in the local disk, easily accessible for future analysis.ConclusionsOur tests revealed that the “informational model free” spectra may represent species signatures. Since JSD applied to Horizontal Mutual Information spectra resulted in statistically significant distances between species, we could calculate respective hierarchical clusters, herein called Informational Dendrograms (ID). When compared to phylogenetic trees all Informational Dendrograms presented similar taxonomy and species clusterization.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0837-0) contains supplementary material, which is available to authorized users.

Multiclass Coarse Analysis for UAV Imagery

This paper presents a novel method to “coarsely” describe extremely high-resolution (EHR) images acquired by means of unmanned aerial vehicles (UAVs) over urban areas. Standard image analysis approaches cannot be directly exploited for the automatic description of UAV images due to their EHR. For this reason, we propose an alternative approach that consists first in the subdivision of the original UAV image in a grid of tiles. Then, each tile is compared with a library of training tiles to inherit the binary multilabel vector of the most similar training tile. This vector conveys a list of classes likely present in the considered tile. Our multiclass tile-based approach needs the definition of two main ingredients: 1) a suitable tile-representation strategy; and 2) a tile-to-tile matching operation. Various tile-representation and matching strategies are investigated. In particular, we present three global representation strategies, which process each tile as a whole and two point-based strategies that exploit points of interest within the considered tile. Regarding the matching strategies, two simple measures of distance, namely, the Euclidean and the chi-squared histogram distances, are explored. Interesting experimental results conducted on a rich set of real UAV images acquired over an urban area are reported and discussed.

Structural Refinement of Proteins by Restrained Molecular Dynamics Simulations with Non-interacting Molecular Fragments

The knowledge of multiple conformational states is a prerequisite to understand the function of membrane transport proteins. Unfortunately, the determination of detailed atomic structures for all these functionally important conformational states with conventional high-resolution approaches is often difficult and unsuccessful. In some cases, biophysical and biochemical approaches can provide important complementary structural information that can be exploited with the help of advanced computational methods to derive structural models of specific conformational states. In particular, functional and spectroscopic measurements in combination with site-directed mutations constitute one important source of information to obtain these mixed-resolution structural models. A very common problem with this strategy, however, is the difficulty to simultaneously integrate all the information from multiple independent experiments involving different mutations or chemical labels to derive a unique structural model consistent with the data. To resolve this issue, a novel restrained molecular dynamics structural refinement method is developed to simultaneously incorporate multiple experimentally determined constraints (e.g., engineered metal bridges or spin-labels), each treated as an individual molecular fragment with all atomic details. The internal structure of each of the molecular fragments is treated realistically, while there is no interaction between different molecular fragments to avoid unphysical steric clashes. The information from all the molecular fragments is exploited simultaneously to constrain the backbone to refine a three-dimensional model of the conformational state of the protein. The method is illustrated by refining the structure of the voltage-sensing domain (VSD) of the Kv1.2 potassium channel in the resting state and by exploring the distance histograms between spin-labels attached to T4 lysozyme. The resulting VSD structures are in good agreement with the consensus model of the resting state VSD and the spin-spin distance histograms from ESR/DEER experiments on T4 lysozyme are accurately reproduced.

A METHOD FOR DETECTING THE CHANGE OF AURORAL ACTIVITIES BASED ON THE ALL‐SKY IMAGE SEQUENCE

AbstractFaced with the annually increasing amount of auroral data, we need to quickly browse the auroral activities and exactly find similar auroral events as more as possible. Traditional method of browsing auroral activities is Keogram. The Keogram loses two‐dimensional information about appearance and motion, which result in missing several auroral events, such as aurora images including curl structure or luminance structure not appearing on the magnetic meridian. In this paper, considering the fact that the solar winds have magnetic fluid nature, we model the auroral motion using the continuous equation. And thereby relative motion in neighborhood is represented by the local vector difference (LVD) based on motion field. Spatiotemporally statistics of the LVDs is used as the representation of auroral sequences. By computing the difference between two successive auroral sequences using metric‐square histogram distance, the change of auroral activities can be quantitatively measured. The local peaks of the curve indicate the changes of auroral activities. Because motion field provides the information about two‐dimensional appearance and motion, the proposed method can detect auroral change and thereby the change curve makes it feasible to fast browse auroral activities and to study auroral activity period.

Person Search System Using Clothing Features

SUMMARYThis paper proposes a person search system that uses clothing features. The system uses a camera to obtain the appearance of a person and a range sensor for person detection and direction estimation. The system detects a moving person based on background subtraction using distance information and histogram of oriented gradient (HOG) features. The clothing area is extracted according to the positions of the person's shoulders and waist. The clothes are characterized by four features that are calculated from the color histogram, color differences, frequency, and co‐occurrence matrix. The system recognizes the same clothing based on the degree of similarity, which is obtained by comparing clothing features. However, two problems can be assumed: (1) the appearance of clothing differs depending on the body direction and (2) the color of clothing changes depending on the illumination. For the former problem, we prepared three databases (front/side/rear views) corresponding to the direction of the person. The system estimates the direction of the person by using information on the body arc based on ellipse fitting. The direction is determined as perpendicular to the long axis of the ellipse fitted to the cross‐section of the body. Concerning the latter problem, we calibrated the color information to correspond to the environment. The method recognizes clothes by selecting the database provided for the direction of the person. The results showed that it was robust in identifying specific persons.

SU‐E‐J‐275: Review ‐ Computerized PET/CT Image Analysis in the Evaluation of Tumor Response to Therapy

Purpose:To review the literature in using computerized PET/CT image analysis for the evaluation of tumor response to therapy.Methods:We reviewed and summarized more than 100 papers that used computerized image analysis techniques for the evaluation of tumor response with PET/CT. This review mainly covered four aspects: image registration, tumor segmentation, image feature extraction, and response evaluation.Results:Although rigid image registration is straightforward, it has been shown to achieve good alignment between baseline and evaluation scans. Deformable image registration has been shown to improve the alignment when complex deformable distortions occur due to tumor shrinkage, weight loss or gain, and motion. Many semi‐automatic tumor segmentation methods have been developed on PET. A comparative study revealed benefits of high levels of user interaction with simultaneous visualization of CT images and PET gradients. On CT, semi‐automatic methods have been developed for only tumors that show marked difference in CT attenuation between the tumor and the surrounding normal tissues. Quite a few multi‐modality segmentation methods have been shown to improve accuracy compared to single‐modality algorithms. Advanced PET image features considering spatial information, such as tumor volume, tumor shape, total glycolytic volume, histogram distance, and texture features have been found more informative than the traditional SUVmax for the prediction of tumor response. Advanced CT features, including volumetric, attenuation, morphologic, structure, and texture descriptors, have also been found advantage over the traditional RECIST and WHO criteria in certain tumor types. Predictive models based on machine learning technique have been constructed for correlating selected image features to response. These models showed improved performance compared to current methods using cutoff value of a single measurement for tumor response.Conclusion:This review showed that computerized PET/CT image analysis holds great potential to improve the accuracy in evaluation of tumor response.This work was supported in part by the National Cancer Institute Grant R01CA172638.

A Split-and-Merge-Based Uterine Fibroid Ultrasound Image Segmentation Method in HIFU Therapy.

High-intensity focused ultrasound (HIFU) therapy has been used to treat uterine fibroids widely and successfully. Uterine fibroid segmentation plays an important role in positioning the target region for HIFU therapy. Presently, it is completed by physicians manually, reducing the efficiency of therapy. Thus, computer-aided segmentation of uterine fibroids benefits the improvement of therapy efficiency. Recently, most computer-aided ultrasound segmentation methods have been based on the framework of contour evolution, such as snakes and level sets. These methods can achieve good performance, although they need an initial contour that influences segmentation results. It is difficult to obtain the initial contour automatically; thus, the initial contour is always obtained manually in many segmentation methods. A split-and-merge-based uterine fibroid segmentation method, which needs no initial contour to ensure less manual intervention, is proposed in this paper. The method first splits the image into many small homogeneous regions called superpixels. A new feature representation method based on texture histogram is employed to characterize each superpixel. Next, the superpixels are merged according to their similarities, which are measured by integrating their Quadratic-Chi texture histogram distances with their space adjacency. Multi-way Ncut is used as the merging criterion, and an adaptive scheme is incorporated to decrease manual intervention further. The method is implemented using Matlab on a personal computer (PC) platform with Intel Pentium Dual-Core CPU E5700. The method is validated on forty-two ultrasound images acquired from HIFU therapy. The average running time is 9.54 s. Statistical results showed that SI reaches a value as high as 87.58%, and normHD is 5.18% on average. It has been demonstrated that the proposed method is appropriate for segmentation of uterine fibroids in HIFU pre-treatment imaging and planning.

Mesoscale analysis of failure in quasi-brittle materials: comparison between lattice model and acoustic emission data.

SummaryThe purpose of this paper is to analyse the development and the evolution of the fracture process zone during fracture and damage in quasi‐brittle materials. A model taking into account the material details at the mesoscale is used to describe the failure process at the scale of the heterogeneities. This model is used to compute histograms of the relative distances between damaged points. These numerical results are compared with experimental data, where the damage evolution is monitored using acoustic emissions. Histograms of the relative distances between damage events in the numerical calculations and acoustic events in the experiments exhibit good agreement. It is shown that the mesoscale model provides relevant information from the point of view of both global responses and the local failure process. © 2015 The Authors. International Journal for Numerical and Analytical Methods in Geomechanics published by John Wiley & Sons Ltd.

Consecutive Mechanism in the Diffusion of D2O on a NaCl(100) Bilayer.

The motion of D2O monomers is investigated on a NaCl(100) bilayer on Ag(111) between 42.3 and 52.3 K by scanning tunneling microscopy. The diffusion distance histogram reveals a squared diffusion lattice that agrees with the primitive unit cell of the (100) surface. From the Arrhenius dependence, we derive the diffusion energy, the pre-exponential factor, and the attempt frequency. The mechanism of the motion is identified by comparison of the experimental results to theoretical calculations. Via low temperature adsorption site determination in connection with density functional theory, we reveal an influence of the metallic support onto the intermediate state of the diffusive motion.

Evaluation of Geo-based Image Fusion on Mobile Cloud Environment using Histogram Similarity Analysis

Mobility and cloud platform have become the dominant paradigm to develop web services dealing with huge and diverse digital contents for scientific solution or engineering application. These two trends are technically combined into mobile cloud computing environment taking beneficial points from each. The intention of this study is to design and implement a mobile cloud application for remotely sensed image fusion for the further practical geo-based mobile services. In this implementation, the system architecture consists of two parts: mobile web client and cloud application server. Mobile web client is for user interface regarding image fusion application processing and image visualization and for mobile web service of data listing and browsing. Cloud application server works on OpenStack, open source cloud platform. In this part, three server instances are generated as web server instance, tiling server instance, and fusion server instance. With metadata browsing of the processing data, image fusion by Bayesian approach is performed using functions within Orfeo Toolbox (OTB), open source remote sensing library. In addition, similarity of fused images with respect to input image set is estimated by histogram distance metrics. This result can be used as the reference criterion for user parameter choice on Bayesian image fusion. It is thought that the implementation strategy for mobile cloud application based on full open sources provides good points for a mobile service supporting specific remote sensing functions, besides image fusion schemes, by user demands to expand remote sensing application fields.

Near neighbor searching with K nearest references

Proximity searching is the problem of retrieving, from a given database, those objects closest to a query. To avoid exhaustive searching, data structures called indexes are built on the database prior to serving queries. The curse of dimensionality is a well-known problem for indexes: in spaces with sufficiently concentrated distance histograms, no index outperforms an exhaustive scan of the database.In recent years, a number of indexes for approximate proximity searching have been proposed. These are able to cope with the curse of dimensionality in exchange for returning an answer that might be slightly different from the correct one.In this paper we show that many of those recent indexes can be understood as variants of a simple general model based on K-nearest reference signatures. A set of references is chosen from the database, and the signature of each object consists of the K references nearest to the object. At query time, the signature of the query is computed and the search examines only the objects whose signature is close enough to that of the query.Many known and novel indexes are obtained by considering different ways to determine how much detail the signature records (e.g., just the set of nearest references, or also their proximity order to the object, or also their distances to the object, and so on), how the similarity between signatures is defined, and how the parameters are tuned. In addition, we introduce a space-efficient representation for those families of indexes, making it possible to search very large databases in main memory. Small indexes are cache friendly, inducing faster queries.We perform exhaustive experiments comparing several known and new indexes that derive from our framework, evaluating their time performance, memory usage, and quality of approximation. The best indexes outperform the state of the art, offering an attractive balance between all these aspects, and turn out to be excellent choices in many scenarios. Our framework gives high flexibility to design new indexes.

基于梯度的区域生长和距离直方图的快速圆检测方法

基于梯度的区域生长和距离直方图的快速圆检测方法

Ligand-Dependent Conformational Cycle of the Na+/Hydantoin Transporter Mhp1

The LeuT Fold is characterized by a five transmembrane helix, inverted repeat structure that has been observed in an increasing number of physiologically important transporter families. The recurrence of this structural scaffold among the LeuT Fold transporters has stimulated conjectures of a unified mechanism of alternating access despite significant diversity in sequence, function, and ion coupling stoichiometries. Mhp1, a Na+-coupled symporter of the Nucleobase:Cation Symporter 1 (NCS1) family, was the first LeuT Fold member to be structurally characterized by a full complement of canonical states including outward-facing, inward-facing, and occluded conformations, which implied a rocking bundle mechanism. Here we report an investigation of Mhp1 dynamics by electron paramagnetic resonance (EPR) spectroscopy. In this analysis, double electron-electron resonance (DEER) distance distributions between pairs of spin labels monitored conformational changes in response to ligand-binding. These experimental distance distributions were directly compared with the Mhp1 crystal structures by applying MD simulations of spin label rotamers at the double mutant sites and simulating distance histograms. The results of this investigation support the assertion that the crystallographically-captured conformations of Mhp1 are reflected in solution as major conformations and that Mhp1 operates primarily through a rocking bundle-like mechanism. However, unlike LeuT, Na+ binding does not induce transitions between inward- and outward-facing conformations, rather it appears that Na+ powers transport exclusively through modulation of substrate binding affinity. The model of Mhp1 transport proposed here depends critically on the description of conformational equilibria, as the Mhp1 transport cycle progresses through states using low probability transitions. A comparative analysis of the LeuT and Mhp1 mechanisms highlight significant mechanistic divergence that we speculate may in part be explained by the differential mechanisms of coupling to the Na+ gradient.

Large-scale mining for similar protein binding pockets: with RAPMAD retrieval on the fly becomes real.

Determination of structural similarities between protein binding pockets is an important challenge in in silico drug design. It can help to understand selectivity considerations, predict unexpected ligand cross-reactivity, and support the putative annotation of function to orphan proteins. To this end, Cavbase was developed as a tool for the automated detection, storage, and classification of putative protein binding sites. In this context, binding sites are characterized as sets of pseudocenters, which denote surface-exposed physicochemical properties, and can be used to enable mutual binding site comparisons. However, these comparisons tend to be computationally very demanding and often lead to very slow computations of the similarity measures. In this study, we propose RAPMAD (RApid Pocket MAtching using Distances), a new evaluation formalism for Cavbase entries that allows for ultrafast similarity comparisons. Protein binding sites are represented by sets of distance histograms that are both generated and compared with linear complexity. Attaining a speed of more than 20 000 comparisons per second, screenings across large data sets and even entire databases become easily feasible. We demonstrate the discriminative power and the short runtime by performing several classification and retrieval experiments. RAPMAD attains better success rates than the comparison formalism originally implemented into Cavbase or several alternative approaches developed in recent time, while requiring only a fraction of their runtime. The pratical use of our method is finally proven by a successful prospective virtual screening study that aims for the identification of novel inhibitors of the NMDA receptor.

Bin Ratio-Based Histogram Distances and Their Application to Image Classification.

Large variations in image background may cause partial matching and normalization problems for histogram-based representations, i.e., the histograms of the same category may have bins which are significantly different, and normalization may produce large changes in the differences between corresponding bins. In this paper, we deal with this problem by using the ratios between bin values of histograms, rather than bin values' differences which are used in the traditional histogram distances. We propose a bin ratio-based histogram distance (BRD), which is an intra-cross-bin distance, in contrast with previous bin-to-bin distances and cross-bin distances. The BRD is robust to partial matching and histogram normalization, and captures correlations between bins with only a linear computational complexity. We combine the BRD with the ℓ1 histogram distance and the χ(2) histogram distance to generate the ℓ1 BRD and the χ(2) BRD, respectively. These combinations exploit and benefit from the robustness of the BRD under partial matching and the robustness of the ℓ1 and χ(2) distances to small noise. We propose a method for assessing the robustness of histogram distances to partial matching. The BRDs and logistic regression-based histogram fusion are applied to image classification. The experimental results on synthetic data sets show the robustness of the BRDs to partial matching, and the experiments on seven benchmark data sets demonstrate promising results of the BRDs for image classification.

Failure Analysis of MIM and MIS Structures Using Point-to-Event Distance and Angular Probability Distributions

Multiple breakdown (BD) spots are generated in large $(>\hbox{10}^{-4} \hbox{cm}^{2})$ circular and square area metal–insulator–metal and metal–insulator–semiconductor devices using ramped and constant-voltage electrical stresses. Due to the important local thermal effects that take place at the very moment of the formation of the conductive paths spanning the insulating layer, the failure events become visible on the top metal electrode of the structures as a point pattern. The resulting point-to-event distance and angular histograms are compared with the theoretical distributions corresponding to a complete spatial randomness $(CSR)$ process. The location of the voltage probe tip over the top electrode is considered here as the singular point from which the positions of the BD spots are referred to. In this way, we are able to assess the influence of the probe point on the final BD spot distribution. In most of the cases, this distribution is consistent with $CSR$ , but after prolonged electrical stress, a deviation is detected. This departure from $CSR$ is ascribed to the concentration of the current lines in the top electrode toward the center of the structure. The methods reported here are general and can be used for analyzing the generation of similar point patterns occurring in other structures or material systems.