Information Similarity Measure Research Articles

Neutrosophic Vague Incidence Graph

Vague sets gives more intuitive graphical notation of vague data, that devotes better analysis in information relationships, incompleteness and similarity measures. Neutrosophic graphs are used as a mathematical tool to kept an imprecise and unspecified information. In this paper, the neutrosophic vague incidence graphs are introduced. The edge-connectivity, vertex-connectivity and pair-connectivity in neutrosophic vague incidence graphs are established. The given results are illustrated with suitable example.

Asymptotic distribution-free change-point detection for multivariate and non-Euclidean data

We consider the testing and estimation of change-points, locations where the distribution abruptly changes, in a sequence of multivariate or non-Euclidean observations. We study a nonparametric framework that utilizes similarity information among observations, which can be applied to various data types as long as an informative similarity measure on the sample space can be defined. The existing approach along this line has low power and/or biased estimates for change-points under some common scenarios. We address these problems by considering new tests based on similarity information. Simulation studies show that the new approaches exhibit substantial improvements in detecting and estimating change-points. In addition, under some mild conditions, the new test statistics are asymptotically distribution-free under the null hypothesis of no change. Analytic $p$-value approximations to the significance of the new test statistics for the single change-point alternative and changed interval alternative are derived, making the new approaches easy off-the-shelf tools for large datasets. The new approaches are illustrated in an analysis of New York taxi data.

Efficient Relational Interest Feature Selection for Improving the Quality of M-distance Education Using Content-Based Information Similarity Measure

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

A Weighted Edge-Count Two-Sample Test for Multivariate and Object Data

ABSTRACTTwo-sample tests for multivariate data and non-Euclidean data are widely used in many fields. Parametric tests are mostly restrained to certain types of data that meets the assumptions of the parametric models. In this article, we study a nonparametric testing procedure that uses graphs representing the similarity among observations. It can be applied to any data types as long as an informative similarity measure on the sample space can be defined. The classic test based on a similarity graph has a problem when the two sample sizes are different. We solve the problem by applying appropriate weights to different components of the classic test statistic. The new test exhibits substantial power gains in simulation studies. Its asymptotic permutation null distribution is derived and shown to work well under finite samples, facilitating its application to large datasets. The new test is illustrated through an analysis on a real dataset of network data.

Fat-based registration of breast dynamic contrast enhanced water images.

In this study, a 3D fat-based deformable registration algorithm was developed for registering dynamic contrast-enhanced breast images. The mutual information similarity measure with free-form deformation motion correction in rapidly enhancing lesions can introduce motion. However, in Dixon-based fat-water separated acquisitions, the nonenhancing fat signal can directly be used to estimate deformable motion, which can be later used to deform the water images. Qualitative comparison of the fat-based registration method to a water-based registration method, and to the unregistered images, was performed by two experienced readers. Quantitative analysis of the registration was evaluated by estimating the mean-squared signal difference on the fat images. Using a scale of 0 (no motion) to 2 ( > 4 voxels of motion), the average image quality score of the fat-based registered images was 0.5 ± 0.6, water-based registration was 0.8 ± 0.8, and the unregistered dataset was 1.6 ± 0.6. The mean-squared-signal-difference metric on the fat images was significantly lower for fat-based registered images compared with both water-based registered and unregistered images. Fat-based registration of breast dynamic contrast-enhanced images is a promising technique for performing deformable motion correction of breast without introducing new motion. Magn Reson Med 79:2408-2414, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Secure Multiset Intersection Cardinality and its Application to Jaccard Coefficient

The Jaccard Coefficient, as an information similarity measure, has wide variety of applications, such as cluster analysis and image segmentation. Due to the concerns of personal privacy, the Jaccard Coefficient cannot be computed directly between two independently owned datasets. The problem, secure computation of the Jaccard Coefficient for multisets (SJCM), considers the situation where two parties want to securely compute the random shares of the Jaccard Coefficient between their multisets. During the process, the content of each party's multiset is not disclosed to the other party and also the value of Jaccard Coefficient should be hidden from both parties. Secure computation of multiset intersection cardinality is an important sub-problem of SJCM. Existing methods when applied to solve such a problem can lead to either insecure or inefficient solutions. Our work addresses this gap. We first present a basic SJCM protocol constructed using the existing secure dot product method as a sub-routine. Then, as a major contribution, we propose an approximated version of our basic protocol to improve efficiency without compromising accuracy much. We provide various experimental results to show that the proposed protocols are significantly more efficient than the existing techniques when the domain size is small using both simulated and real datasets.

A Benchmark Evaluation of Similarity Measures for Multitemporal SAR Image Change Detection

Synthetic aperture radar (SAR) image change detection is playing an important role in various Earth Observation (EO) applications. There exists a large number of different methods that have been proposed to address this issue. However, due to the fact that several kinds of changes with diverse characteristics can arise in SAR images, there is no consensus on their performances because most methods have been evaluated using different data sets, probably facing several kinds of changes, but without an in-depth analysis of the characteristics of SAR image changes. Therefore, two important problems arise. The first is what kind of change each approach can detect. The second is how much they can detect a kind of change. Although the importance to model any kind of changes has been realized, there is no principled methodology to carry out the analysis due to the difficulty in modeling various kinds of changes. In this paper, we propose a benchmark methodology to reach this goal by simulating selected kinds of changes in addition to using real data with changes. Six kinds of SAR changes for eight typical image categories are simulated, i.e., reflectivity changes, first-order, second-order, and higher order statistical changes, linear and nonlinear changes. Based on this methodology for change simulation, a comprehensive evaluation of information similarity measures is carried out. An explicit conclusion we have drawn from the evaluation is that the various methods behave very differently for all kinds of changes. We hope that this study will promote the advancement of this topic.

Graph-based change-point detection

We consider the testing and estimation of change-points—locations where the distribution abruptly changes—in a data sequence. A new approach, based on scan statistics utilizing graphs representing the similarity between observations, is proposed. The graph-based approach is nonparametric, and can be applied to any data set as long as an informative similarity measure on the sample space can be defined. Accurate analytic approximations to the significance of graph-based scan statistics for both the single change-point and the changed interval alternatives are provided. Simulations reveal that the new approach has better power than existing approaches when the dimension of the data is moderate to high. The new approach is illustrated on two applications: The determination of authorship of a classic novel, and the detection of change in a network over time.

Regularization in Deformable Registration of Biomedical Images Based on Divergence and Curl Operators

Similarity measures in medical images do not uniquely determine the correspondence between two voxels in deformable image registration. Uncertainties in the final computed deformation exist, questioning the actual physiological consistency of the deformation between the two images. We developed a deformable image registration method that regularizes the deformation field in order to model a deformation with physiological properties, relying on vector calculus based operators as a regularization function. We implemented a 3D multi-resolution parametric deformable image registration, containing divergence and curl of the deformation field as regularization terms. Exploiting a BSpline model, we fit the transformation to optimize histogram-based mutual information similarity measure. In order to account for compression/expansion, we extract sink/source/circulation components as irregularities in the warped image and compensate them. The registration performance was evaluated using Jacobian determinant of the deformation field, inverse-consistency, landmark errors and residual image difference along with displacement field errors. Finally, we compare our results to a robust combination of second derivative regularization, as well as to non-regularized methods. The implementation was tested on synthetic phantoms and clinical data, leading to increased image similarity and reduced inverse-consistency errors. The statistical analysis on clinical cases showed that regularized methods are able to achieve better image similarity than non regularized methods. Also, divergence/curl regularization improves anatomical landmark errors compared to second derivative regularization. The implemented divergence/curl regularization was successfully tested, leading to promising results in comparison with competitive regularization methods. Future work is required to establish parameter tuning and reduce the computational cost.

A hybrid framework for registration of carotid ultrasound images combining iconic and geometric features

Stroke is the third major cause of death worldwide behind heart disease and cancer. Carotid atherosclerosis is the most frequent cause of ischemic stroke. Early diagnosis of carotid plaque and serial monitoring of its size with the help of imaging modalities can help to prevent the atherosclerotic complications. The main difficulty is inevitable variability of patient's head positions during image acquisitions. The time series registration of carotid images helps to improve the monitoring, characterization, and quantification of the disease by suppressing the global movements of the patient. In this work, a novel hybrid registration technique has been proposed and evaluated for registration of carotid ultrasound images taken at different times. The proposed hybrid method bridges the gap between the feature-based and intensity-based registration methods. The feature-based iterative closest point algorithm is used to provide a coarse registration which is subsequently refined by the intensity-based algorithm. The proposed framework uses rigid transformation model coupled with mutual information (MI) similarity measure and Powell optimizer. For quantitative evaluation, different registration approaches have been compared using four error metrics: visual information fidelity, structural similarity index, correlation coefficient, and MI. Qualitative evaluation has also been done using the visual examination of the registered image pairs.

Deformable Breast MRI and CT Registration for Radiation Treatment Planning Using B-spline With Rigidity and Feature Point Distance Regularization

To develop a software tool for accurate and automatic deformable registration between MRI and CT breast images, as particularly preferred in preoperative breast radiation therapy planning. A novel B-Spline registration approach of using feature point distance and local rigidity transformation measure as regularization is adopted to correct fine structure inaccuracy and bone area warping artifact due to B-Spline method's lack of physical property modeling and global intensity based similarity metric. The software tool uses Insight Toolkit (ITK) for image input/output, filtering, feature points identification and auto-segmentation. A B-Spline registration toolkit with invertibility regularization was adopted for mutual information similarity measure and feature point distance penalty. It includes the following steps and functionalities: (1) accepting input of images of different modalities and performing initial rigid registration. (2) Finding feature point pairs, and filtering them by bifurcation identification and distance histogram. (3) Segmenting bone, air, and soft tissue in three levels, in CT by Hounsfield value, or in MR by distinguishing piecewise zero-bound intensity areas' volume and location. Three levels of stiffness are assigned to the segmentation result for rigidity regularization. (4) Using multi-scale approach to update B-Spline coefficients and using conjugate gradient for the solver iteration. (5) Within every step, updating feature point's new position from the deformation field for distance penalty, updating mutual information and transformation Jacobian for regularization. MRI and CT data acquired for three representative breast cancer patients were used to test the software tool. The MRI T1 images were registered upon CT images. Feature point pair distances were evaluated among rigid registration, simple B-Spline registration and our proposed registration. The newly developed deformable multimodality image registration tool can accurately register MRI to CT for breast cancer patients without user interaction. The average feature point pair distance was reduced from 6.7 mm to 3.2 mm by simple B-Spline registration, and was reduced to 2.1 mm by the proposed registration method. The bone area warping artifact in deformed MRI also disappeared in the proposed method. The resulting feature point pair distance is satisfactory considering the resolution of MR images is 1.3 mm. Multimodal images (MRI and CT) for breast cancer patients, which often have large soft tissue deformation, were successfully registered to an acceptable accuracy by using the newly developed software tool, enabling RT planning with MRI-based targeting.

Motion Correction of Multi-b-value Diffusion-weighted Imaging in the Liver

Motion artifacts are a significant source of error in the acquisition and quantification of parameters from multi-b-value diffusion-weighted imaging (DWI). The objective of this article is to present a reliable method to reduce motion-related artifacts during free-breathing at higher b-values when signal levels are low. Twelve patients referred for magnetic resonance imaging of the liver underwent a clinical magnetic resonance imaging examination of the abdominal region that included DWI. Conventional single-shot spin-echo echo planar imaging acquisitions of the liver during free breathing were repeated in a "time-resolved" manner during a single acquisition to obtain data for multi-b-value analysis, alternating between low and high b-values. Image registration using a normalized mutual information similarity measure was used to correct for spatial misalignment of diffusion-weighted volumes caused by motion. Registration error was estimated indirectly by comparing the normalized root-mean-square error (NRMSE) values of data fitted to the biexponential intra-voxel incoherent motion model before and after motion correction. Regions of interest (ROIs) were selected in the liver close to the surface of the liver and close to internal structures such as large bile ducts and blood vessels. For the 12 patient datasets, the mean NRMSE value for the motion-corrected ROIs (0.38 ± 0.16) was significantly lower than the mean NRMSE values for the non-motion-corrected ROIs (0.41 ± 0.13) (P < .05). In cases where there was substantial respiratory motion during the acquisition, visual inspection verified that the algorithm markedly improved alignment of the liver contours between frames. The proposed method addresses motion-related artifacts to increase robustness in multi-b-value acquisitions.

MULTI-FEATURE MUTUAL INFORMATION IMAGE REGISTRATION

Nowadays, information-theoretic similarity measures, especially the mutual information and its derivatives, are one of the most frequently used measures of global intensity feature correspondence in image registration. Because the traditional mutual information similarity measure ignores the dependency of intensity values of neighboring image elements, registration based on mutual information is not robust in cases of low global intensity correspondence. Robustness can be improved by adding spatial information in the form of local intensity changes to the global intensity correspondence. This paper presents a novel method, by which intensities, together with spatial information, i.e., relations between neighboring image elements in the form of intensity gradients, are included in information-theoretic similarity measures. In contrast to a number of heuristic methods that include additional features into the generic mutual information measure, the proposed method strictly follows information theory under certain assumptions on feature probability distribution. The novel approach solves the problem of efficient estimation of multifeature mutual information from sparse high-dimensional feature space. The proposed measure was tested on magnetic resonance (MR) and computed tomography (CT) images. In addition, the measure was tested on positron emission tomography (PET) and MR images from the widely used Retrospective Image Registration Evaluation project image database. The results indicate that multi-feature mutual information, which combines image intensities and intensity gradients, is more robust than the standard single-feature intensity based mutual information, especially in cases of low global intensity correspondences, such as in PET/MR images or significant intensity inhomogeneity.

Automated registration of sequential breath-hold dynamic contrast-enhanced MR images: a comparison of three techniques

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly in use as an investigational biomarker of response in cancer clinical studies. Proper registration of images acquired at different time points is essential for deriving diagnostic information from quantitative pharmacokinetic analysis of these data. Motion artifacts in the presence of time-varying intensity due to contrast enhancement make this registration problem challenging. DCE-MRI of chest and abdominal lesions is typically performed during sequential breath-holds, which introduces misregistration due to inconsistent diaphragm positions and also places constraints on temporal resolution vis-à-vis free-breathing. In this work, we have employed a computer-generated DCE-MRI phantom to compare the performance of two published methods, Progressive Principal Component Registration and Pharmacokinetic Model-Driven Registration, with Sequential Elastic Registration (SER) to register adjacent time-sample images using a published general-purpose elastic registration algorithm. In all three methods, a 3D rigid-body registration scheme with a mutual information similarity measure was used as a preprocessing step. The DCE-MRI phantom images were mathematically deformed to simulate misregistration, which was corrected using the three schemes. All three schemes were comparably successful in registering large regions of interest (ROIs) such as muscle, liver, and spleen. SER was superior in retaining tumor volume and shape, and in registering smaller but important ROIs such as tumor core and tumor rim. The performance of SER on clinical DCE-MRI data sets is also presented.

A Modified PSO Algorithm for Remote Sensing Image Template Matching

Image template matching is essential in image analysis and computer vision tasks. Cross-correlation algorithms are often used in practice, but they are sensitive to nonlinear changes in image intensity and random noise, and are computationally expensive. In this paper, we propose a template-matching algorithm based on a modified particle swarm optimization (PSO) procedure with a mutual information (MI) similarity measure. The influence of MI on the performance of template matching, calculated by different histogram bins, is analyzed first. A modified PSO method (CRI-PSO) is then presented. The proposed algorithm is tested with remote sensing imagery from different sensors and for different seasons. Our experimental results indicate that the proposed approach is robust in practical scenarios and outperforms the standard PSO , multi-start PSO , and cross-correlation algorithms in accuracy and efficiency with our test data. The proposed method can be used for position estimation of aircraft, object recognition, and image retrieval.

Knowledge-based Discrimination In Alzheimer'S Disease

Our goal is to propose a methodology to integrate clinical, cognitive, genetic and neuroimaging results, a disparate assembly of categorical, ordinal, and numerical data, within the context of Alzheimer's disease (AD) research. We describe a knowledge-based, explicit decision model for the discrimination of AD subjects from normal controls (CTRL) based on a recent approach for diagnostic criteria for AD. We proceed by (a) creating a set of rules for each datum source; (b) integrating these disparate data into an information feature in the form of a binary string; and (c) using a machine learning approach based on the Hamming distance as an information similarity measure for the discrimination of probable AD from CTRL. When tested on 249 subjects from the multi-centric Alzheimer's Disease Neuroimaging Initiative study, we reach up to 99.8% discriminative accuracy using a 9-bit information string in a series of 10-fold validation experiments. We expect this versatile approach to become useful in the identification of specific and sensitive phenotypes from large amounts of disparate data.

Cross-Validation of Deformable Registration With Field Maps in Functional Magnetic Resonance Brain Imaging

The localization of brain functional activity with respect to brain anatomy requires registration between a functional image and a reference high-resolution anatomical image. The fast functional magnetic resonance brain images acquired via echo planar imaging (EPI) in functional magnetic resonance imaging (fMRI) suffer from local geometric distortions. After performing standard rigid or affine registration, local nonlinear distortions of up to 10 mm still appear in prefrontal and orbitofrontal cortex, as well as in temporal and cerebellar regions. To reduce such distortions, an enhanced version of a previously developed deformable registration technique is introduced in this paper. The enhanced deformable registration technique involves a multistage mutual information-based free-form deformable framework guided by a field map template. This template is utilized as prior knowledge for performing an initial warp as well as a guiding mechanism to constrain the initial stage of the deformable registration. The enhanced technique is cross-validated with the high-resolution field map distortion correction technique using real datasets. The results from consistency tests using a normalized mutual information similarity measure as well as the statistics of quantitative cross-validation show the degree of enhancement achieved and the impact of the developed deformable registration in applications requiring accurate local registration.

Validation of Non-Rigid Registration Between Functional and Anatomical Magnetic Resonance Brain Images

This paper presents a set of validation procedures for nonrigid registration of functional EPI to anatomical MRI brain images. Although various registration techniques have been developed and validated for high-resolution anatomical MRI images, due to a lack of quantitative and qualitative validation procedures, the use of nonrigid registration between functional EPI and anatomical MRI images has not yet been deployed in neuroimaging studies. In this paper, the performance of a robust formulation of a nonrigid registration technique is evaluated in a quantitative manner based on simulated data and is further evaluated in a quantitative and qualitative manner based on in vivo data as compared to the commonly used rigid and affine registration techniques in the neuroimaging software packages. The nonrigid registration technique is formulated as a second-order constrained optimization problem using a free-form deformation model and mutual information similarity measure. Bound constraints, resolution level and cross-validation issues have been discussed to show the degree of accuracy and effectiveness of the nonrigid registration technique. The analyses performed reveal that the nonrigid approach provides a more accurate registration, in particular when the functional regions of interest lie in regions distorted by susceptibility artifacts.

Accurate and precise 2D–3D registration based on X-ray intensity

This paper addresses the problem of estimating the 3D rigid pose of an object from its digitized X-ray projection. We considered the cases of homogeneous (CAD models) and inhomogeneous (attenuation map obtained from computed tomography) X-ray attenuation in an optimization framework based on a mutual information similarity measure. Convergence of object pose recovery is highly precise and obtained with sub-millimeter accuracy for both screen-film and digital radiographs by three major enhancements: (i) special care is given to the model of Parzen distribution used in the mutual information estimator (data pre-sphering in the bivariate case and bandwidth estimation in the univariate case); (ii) a quasi-global optimization scheme based on a modified version of stochastic clustering is used in conjunction with an object mesh resampling stage to reduce variance of the final pose estimator; (iii) nonlinear response to the radiograph is also estimated for screen-film radiographs.

MO‐E‐330A‐06: Cone Beam CT (CBCT) Image Fusion for Prostate Localization: Interobserver Variability Study Using Manual and Automatic Registration

Purpose: Applying manual registration of CBCT to a reference image for prostate localization increases treatment time affecting prostate motion and patient setup. A reliable automatic registration can be more time efficient and accurate. This preliminary study demonstrates variability of manual fusion by multiple observers comparing manual to an in‐house automatic fusion. Methods and Materials: 7 patients with 7 CBCT scans each were studied by 4 observers. Off‐line manual fusion between CBCT and Simulation CT was performed using On‐Board Imaging (OBI) software by Varian version 1.2. We pre‐defined our Region of Interest (ROI) and Organ of Interest (OOI) to be the pelvic bones and the prostate respectively. Offsets were recorded in 4 degrees of freedom (3 translations and couch rotation). To increase precision, one outlier observation was excluded. Interobserver variation in manual registration was studied by determining percentage of observations that fall within 1 mm and 1 degree standard deviation (SD). Automatic registration, utilizing the uphill simplex, gradient correlation and mutual information similarity measures, was compared to the manual matched results using frequency histograms of the differences between the two methods. System robustness is checked using prostate calcifications. Results: Comparing different observers, 69% cranial‐caudal (C‐C), 83% anterior‐posterior (A‐P), 100% right‐left (R‐L), and 100% (couch rotation) of the observations fall within 1 mm,1 degree SD for ROI. 69% C‐C, 75% A‐P, 90% R‐L, and 100% couch rotation were observed for OOI. ROI/OOI automatic fusion showed 92%/85% overall agreement with manual fusion within 2 mm respectively. Conclusion: Manual registration is time consuming and demonstrates observers' variations. The automatic method localizes the prostate within 15 seconds. More observers and measurements, methods utilizing drawn contours and implanted fiducial markers, will be required for system accuracy and precision. Our automatic registration with feed back from observers' experiences would enable on‐line correction for prostate motion.