Symmetric Distance Research Articles

An adaptive Fuzzy C-means method utilizing neighboring information for breast tumor segmentation in ultrasound images.

Ultrasound (US) imaging has been widely used in breast tumor diagnosis and treatment intervention. Automatic delineation of the tumor is a crucial first step, especially for the computer-aided diagnosis (CAD) and US-guided breast procedure. However, the intrinsic properties of US images such as low contrast and blurry boundaries pose challenges to the automatic segmentation of the breast tumor. Therefore, the purpose of this study is to propose a segmentation algorithm that can contour the breast tumor in US images. To utilize the neighbor information of each pixel, a Hausdorff distance based fuzzy c-means (FCM) method was adopted. The size of the neighbor region was adaptively updated by comparing the mutual information between them. The objective function of the clustering process was updated by a combination of Euclid distance and the adaptively calculated Hausdorff distance. Segmentation results were evaluated by comparing with three experts' manual segmentations. The results were also compared with a kernel-induced distance based FCM with spatial constraints, the method without adaptive region selection, and conventional FCM. Results from segmenting 30 patient images showed the adaptive method had a value of sensitivity, specificity, Jaccard similarity, and Dice coefficient of 93.60 ± 5.33%, 97.83 ± 2.17%, 86.38 ± 5.80%, and 92.58 ± 3.68%, respectively. The region-based metrics of average symmetric surface distance (ASSD), root mean square symmetric distance (RMSD), and maximum symmetric surface distance (MSSD) were 0.03 ± 0.04 mm, 0.04 ± 0.03 mm, and 1.18 ± 1.01 mm, respectively. All the metrics except sensitivity were better than that of the non-adaptive algorithm and the conventional FCM. Only three region-based metrics were better than that of the kernel-induced distance based FCM with spatial constraints. Inclusion of the pixel neighbor information adaptively in segmenting US images improved the segmentation performance. The results demonstrate the potential application of the method in breast tumor CAD and other US-guided procedures.

Inferior vena cava segmentation with parameter propagation and graph cut.

The inferior vena cava (IVC) is one of the vital veins inside the human body. Accurate segmentation of the IVC from contrast-enhanced CT images is of great importance. This extraction not only helps the physician understand its quantitative features such as blood flow and volume, but also it is helpful during the hepatic preoperative planning. However, manual delineation of the IVC is time-consuming and poorly reproducible. In this paper, we propose a novel method to segment the IVC with minimal user interaction. The proposed method performs the segmentation block by block between user-specified beginning and end masks. At each stage, the proposed method builds the segmentation model based on information from image regional appearances, image boundaries, and a prior shape. The intensity range and the prior shape for this segmentation model are estimated based on the segmentation result from the last block, or from user- specified beginning mask if at first stage. Then, the proposed method minimizes the energy function and generates the segmentation result for current block using graph cut. Finally, a backward tracking step from the end of the IVC is performed if necessary. We have tested our method on 20 clinical datasets and compared our method to three other vessel extraction approaches. The evaluation was performed using three quantitative metrics: the Dice coefficient (Dice), the mean symmetric distance (MSD), and the Hausdorff distance (MaxD). The proposed method has achieved a Dice of [Formula: see text], an MSD of [Formula: see text]mm, and a MaxD of [Formula: see text]mm, respectively, in our experiments. The proposed approach can achieve a sound performance with a relatively low computational cost and a minimal user interaction. The proposed algorithm has high potential to be applied for the clinical applications in the future.

Automatic image quality assessment and measurement of fetal head in two-dimensional ultrasound image.

Owing to the inconsistent image quality existing in routine obstetric ultrasound (US) scans that leads to a large intraobserver and interobserver variability, the aim of this study is to develop a quality-assured, fully automated US fetal head measurement system. A texton-based fetal head segmentation is used as a prerequisite step to obtain the head region. Textons are calculated using a filter bank designed specific for US fetal head structure. Both shape- and anatomic-based features calculated from the segmented head region are then fed into a random forest classifier to determine the quality of the image (e.g., whether the image is acquired from a correct imaging plane), from which fetal head measurements [biparietal diameter (BPD), occipital-frontal diameter (OFD), and head circumference (HC)] are derived. The experimental results show a good performance of our method for US quality assessment and fetal head measurements. The overall precision for automatic image quality assessment is 95.24% with 87.5% sensitivity and 100% specificity, while segmentation performance shows 99.27% ([Formula: see text]) of accuracy, 97.07% ([Formula: see text]) of sensitivity, 2.23mm ([Formula: see text]) of the maximum symmetric contour distance, and 0.84mm ([Formula: see text]) of the average symmetric contour distance. The statistical analysis results using paired [Formula: see text]-test and Bland-Altman plots analysis indicate that the 95% limits of agreement for inter observer variability between the automated measurements and the senior expert measurements are 2.7mm of BPD, 5.8mm of OFD, and 10.4mm of HC, whereas the mean differences are [Formula: see text], [Formula: see text], and [Formula: see text], respectively. These narrow 95% limits of agreements indicate a good level of consistency between the automated and the senior expert's measurements.

Efficient Anderson localization bounds for large multi-particle systems

We study multi-particle interactive quantum disordered systems on a polynomially growing countable connected graph ( \mathcal Z, \mathcal E ). The main novelty is to give localization bounds uniform in finite volumes (subgraphs) in \mathcal Z^N as well as for the whole of \mathcal Z^N . Such bounds are proved here by means of a comprehensive fixed-energy multi-particle multiscale analysis. We consider – for the first time in the literature – a discrete N -particle model with an infinite-range, sub-exponentially decaying interaction, and establish (1) exponential spectral localization, and (2) strong dynamical localization with sub-exponential rate of decay of the eigenfunction correlators with respect to the natural symmetrized distance in the multi-particle configuration space.

A Comparative Evaluation of 3 Different Free-Form Deformable Image Registration and Contour Propagation Methods for Head and Neck MRI: The Case of Parotid Changes During Radiotherapy.

Purpose:To validate and compare the deformable image registration and parotid contour propagation process for head and neck magnetic resonance imaging in patients treated with radiotherapy using 3 different approaches—the commercial MIM, the open-source Elastix software, and an optimized version of it.Materials and Methods:Twelve patients with head and neck cancer previously treated with radiotherapy were considered. Deformable image registration and parotid contour propagation were evaluated by considering the magnetic resonance images acquired before and after the end of the treatment. Deformable image registration, based on free-form deformation method, and contour propagation available on MIM were compared to Elastix. Two different contour propagation approaches were implemented for Elastix software, a conventional one (DIR_Trx) and an optimized homemade version, based on mesh deformation (DIR_Mesh). The accuracy of these 3 approaches was estimated by comparing propagated to manual contours in terms of average symmetric distance, maximum symmetric distance, Dice similarity coefficient, sensitivity, and inclusiveness.Results:A good agreement was generally found between the manual contours and the propagated ones, without differences among the 3 methods; in few critical cases with complex deformations, DIR_Mesh proved to be more accurate, having the lowest values of average symmetric distance and maximum symmetric distance and the highest value of Dice similarity coefficient, although nonsignificant. The average propagation errors with respect to the reference contours are lower than the voxel diagonal (2 mm), and Dice similarity coefficient is around 0.8 for all 3 methods.Conclusion:The 3 free-form deformation approaches were not significantly different in terms of deformable image registration accuracy and can be safely adopted for the registration and parotid contour propagation during radiotherapy on magnetic resonance imaging. More optimized approaches (as DIR_Mesh) could be preferable for critical deformations.

Enhancement of “Technique for Order Preference by Similarity to Ideal Solution” Approach for Evaluating the Web Sources to Select as External Source for Web Warehousing

The main concern of this paper is to evaluate the web sources, which are to be selected as external data sources for web warehousing. In order to identify the web sources, they are evaluated on the ground of their multiple features. For it, Multi Criteria Decision Making (MCDM) approach has been used. Here, among all the MCDM approach, the focus is on “Technique for Order Preference by Similarity to Ideal Solution” (TOPSIS) approach and proposing an enhancement in this method. The conventional TOPSIS approach uses Euclidean Distance to measure the similarity. Here, Jeffrey Divergence has been proposed to measure the similarity instead of Euclidean Distance which includes all the symmetric distances during computation. The Euclidean Distance only measures unidirectional distance whereas the Jeffrey Divergence includes multidirectional distances. Unidirectional distance includes only distance in one dimension but multidirectional distances includes differences, so more relevant in web sources evaluation. Experimental analysis for both the variations of TOPSIS approach have been conducted and the result shows the enhancement in the selection of web sources.

NG-DBSCAN

We present NG-DBSCAN, an approximate density-based clustering algorithm that operates on arbitrary data and any symmetric distance measure. The distributed design of our algorithm makes it scalable to very large datasets; its approximate nature makes it fast, yet capable of producing high quality clustering results. We provide a detailed overview of the steps of NG-DBSCAN, together with their analysis. Our results, obtained through an extensive experimental campaign with real and synthetic data, substantiate our claims about NG-DBSCAN's performance and scalability.

Kullback-Leibler distance-based enhanced detection of incipient anomalies

Accurate and effective anomaly detection and diagnosis of modern engineering systems by monitoring processes ensure reliability and safety of a product while maintaining desired quality. In this paper, an innovative method based on Kullback-Leibler divergence for detecting incipient anomalies in highly correlated multivariate data is presented. We use a partial least square (PLS) method as a modeling framework and a symmetrized Kullback-Leibler distance (KLD) as an anomaly indicator, where it is used to quantify the dissimilarity between current PLS-based residual and reference probability distributions obtained using fault-free data. Furthermore, this paper reports the development of two monitoring charts based on the KLD. The first approach is a KLD-Shewhart chart, where the Shewhart monitoring chart with a three sigma rule is used to monitor the KLD of the response variables residuals from the PLS model. The second approach integrates the KLD statistic into the exponentially weighted moving average monitoring chart. The performance of the PLS-based KLD anomaly-detection methods is illustrated and compared to that of conventional PLS-based anomaly detection methods. Using synthetic data and simulated distillation column data, we demonstrate the greater sensitivity and effectiveness of the developed method over the conventional PLS-based methods, especially when data are highly correlated and small anomalies are of interest. Results indicate that the proposed chart is a very promising KLD-based method because KLD-based charts are, in practice, designed to detect small shifts in process parameters.

The asymmetric relationship between national cultural distance and target premiums in cross-border M&A

Prior literature routinely assumes symmetric cultural distance (CD) in a given country pair, suggesting an identical role for the home and host countries. However, if the absolute CD is perceived differently depending on the acquirer's home base, it may yield disparate effects in cross-border M&A (CB M&A) transactions. Consistently, we find that the relationship between CD and CB M&A premiums is not uniform, but varies by acquirer origin. While we find a strong negative association between CD and premiums when U.S. firms bid for foreign targets, no such negative association is observed when foreign bidders evaluate U.S. targets. Using traveler flows, student exchanges, and previous acquisitions as proxies for directional cross-cultural familiarity, we provide evidence that familiarity with the target's national culture is related to the magnitude of CD discount in a systematic way and thus explains the observed asymmetry. Our findings highlight the existence, as well as the source, of the asymmetric property in the relationship between CD and target premiums in CB M&A.

Hardware Design of Seizure Detection Based on Wavelet Transform and Sample Entropy

Detecting epileptic seizure is a very time consuming and costly task if a support vector machine (SVM) hardware processor is used. In this paper, an automated seizure detection scheme is developed by combining discrete wavelet transform (DWT), sample entropy (SampEn) and a novel classification algorithm based on each wavelet coefficient and voting strategy. In order to save circuit area, a Daubechies order 4 (db4) filter of lattice structure is introduced in DWT, only half elements of the symmetric distance matrix in the SampEn are stored and module reusing strategy is used. To speed up the detection, intermediate results are reused by reasonably organizing the SampEn calculation procedures. The seizure detection scheme is implemented in a field-programmable gate array (FPGA) and its classification performance is tested with publicly available epilepsy dataset.

General construction method for a rotationally symmetric ultrashort distance catadioptric projector.

In this work, the off-axis imaging of a fan of rays with a rotationally symmetric mirror is studied, based on which a general construction method is proposed for the design of a rotationally symmetric ultrashort distance catadioptric projector. With the proposed method, the intermediate images at arbitrary planes of incidence can be obtained, and thus the designer has the freedom to choose suitable intermediate images to simplify the aberration correction of the catadioptric starting point. To prove the effectiveness of the proposed method, the construction process of a convex-type ultrashort distance catadioptric projector is presented. The intermediate image at the meridional plane is discontinuous, and the image distances for some fields are infinite. In contrast, the intermediate image at the sagittal plane is continuous, smooth, and finite-and thus is selected to correct the skew aberrations in the catadioptric starting point. An initial configuration, which has small aberrations, is obtained without increasing the system complexity. Further automatic optimization is applied to the initial configuration to get the optimal solution. The simulated performance of the optimal solution is excellent.

Review and Perspective for Distance-Based Clustering of Vehicle Trajectories

In this paper, we tackle the issue of clustering trajectories of geolocalized observations based on the distance between trajectories. We first provide a comprehensive review of the different distances used in the literature to compare trajectories. Then, based on the limitations of these methods, we introduce a new distance: symmetrized segment-path distance (SSPD). We compare this new distance to the others according to their corresponding clustering results obtained using both the hierarchical clustering and affinity propagation methods. We finally present a python package: trajectory distance, which contains the methods for calculating the SSPD distance, and the other distances reviewed in this paper.

An integer programming approach for solving the p-dispersion problem

Given a collection of n items (elements) and an associated symmetric distance dij between each pair of items i and j, we seek a subset P of these items (with a given cardinality p) so that the minimum pairwise distance among the selected items is maximized. This problem is known as the max–min diversity problem or the p-dispersion problem, and it is shown to be np-hard. We define a collection of node packing problems associated with each instance of this problem and employ a binary search among these node packing problems to devise an effective procedure for solving the original problem. We employ existing integer programming techniques, i.e., branch-and-bound and strong valid inequalities, to solve these node packing problems. Through a computational experiment we show that this approach can be used to solve relatively large instances of the p-dispersion problem, i.e., instances with more than 1000 items. We also discuss an application of this problem in the context of locating traffic sensors in a highway network.

Omokage Search and Gmfit: Shape Similarity Search and Superposition among Models and Maps

Single particle CryoEM typically produces 3D density maps of the macromolecular complex with low resolution. For understanding these maps, it is essential to fit atomic models of individual subunits into the maps of the complex. We developed a program gmfit employing Gaussian mixture model (GMM) to rapidly superimpose subunits into the map (Kawabata, 2008, Biophys. J. 95, 4643-4658). The web server (“pairwise gmfit”) for pairwise superposition of atomic models and density maps is now available. We also developed the web server for Omokage search, which is a global shape similarity search in both the PDB and EMDB (Suzuki et al., Bioinformatics, submitted). The Omokage search is rapidly performed using 1D distance rank profiles, found similar models or maps are superimposed onto the query model or map using the pairwise gmfit program.Fitting multiple subunits into the map is more difficult than fitting one subunit. We try to include several additional experimental restraints to obtain more realistic configurations for low-resolution maps. (1) Overlaps with density map and (2) repulsions are two basic restraints. In addition, we introduce additional restraints: (3) symmetries of the subunits and (4) proximities between subunits. We introduce three methods to generate subunit initial configurations: symmetric operation, segmentation fitting, and distance geometry. The segmentation fitting method is repeating the “segmentation” of the map for each subunit, and the “fitting” of each subunit to the corresponding segmented region. The distance geometry method is employed for generating subunit configurations satisfying the given proximities. The subunit configurations are refined using the gradient-based optimization with the restraint energy. These methods have been tested for several simulated maps and real maps registered in EMDB. The source code of gmfit is freely available.

A supervised texton based approach for automatic segmentation and measurement of the fetal head and femur in 2D ultrasound images

This paper presents a supervised texton based approach for the accurate segmentation and measurement of ultrasound fetal head (BPD, OFD, HC) and femur (FL). The method consists of several steps. First, a non-linear diffusion technique is utilized to reduce the speckle noise. Then, based on the assumption that cross sectional intensity profiles of skull and femur can be approximated by Gaussian-like curves, a multi-scale and multi-orientation filter bank is designed to extract texton features specific to ultrasound fetal anatomic structure. The extracted texton cues, together with multi-scale local brightness, are then built into a unified framework for boundary detection of ultrasound fetal head and femur. Finally, for fetal head, a direct least square ellipse fitting method is used to construct a closed head contour, whilst, for fetal femur a closed contour is produced by connecting the detected femur boundaries. The presented method is demonstrated to be promising for clinical applications. Overall the evaluation results of fetal head segmentation and measurement from our method are comparable with the inter-observer difference of experts, with the best average precision of 96.85%, the maximum symmetric contour distance (MSD) of 1.46 mm, average symmetric contour distance (ASD) of 0.53 mm; while for fetal femur, the overall performance of our method is better than the inter-observer difference of experts, with the average precision of 84.37%, MSD of 2.72 mm and ASD of 0.31 mm.

Hemispatial Neglect Shows That "Before" Is "Left".

Recent research has led to the hypothesis that events which unfold in time might be spatially represented in a left-to-right fashion, resembling writing direction. Here we studied fourteen right-hemisphere damaged patients, with or without neglect, a disorder of spatial awareness affecting contralesional (here left) space processing and representation. We reasoned that if the processing of time-ordered events is spatial in nature, it should be impaired in the presence of neglect and spared in its absence. Patients categorized events of a story as occurring before or after a central event, which acted as a temporal reference. An asymmetric distance effect emerged in neglect patients, with slower responses to events that took place before the temporal reference. The event occurring immediately before the reference elicited particularly slow responses, closely mirroring the pattern found in neglect patients performing numerical comparison tasks. Moreover, the first item elicited significantly slower responses than the last one, suggesting a preference for a left-to-right scanning/representation of events in time. Patients without neglect showed a regular and symmetric distance effect. These findings further suggest that the representation of events order is spatial in nature and provide compelling evidence that ordinality is similarly represented within temporal and numerical domains.

An Adaptive Phasor Estimation Algorithm for Numerical Protective Relays Based on the Generalized Delayed Signal Cancelation Method

In this paper the generalized delayed signal cancelation (GDSC) method is used for estimating the fundamental-frequency positive-sequence, negative-sequence and zero-sequence vector components of the voltages and currents to be processed by a distance relay. The GDSC filtering characteristics allow fast frequency-adaptive extraction of the sequence components of distorted three-phase signals, avoiding the use of the Fortescue transform. The proposed algorithm is incorporated into a symmetrical component distance relay, and the code is implemented in a low-cost digital signal processor. Real fault data obtained from digital fault recorders are used for evaluating the performance of the proposed relay algorithm.

Automatic extraction of three-dimensional thoracic aorta geometric model from phase contrast MRI for morphometric and hemodynamic characterization.

To propose and assess a new method that automatically extracts a three-dimensional (3D) geometric model of the thoracic aorta (TA) from 3D cine phase contrast MRI (PCMRI) acquisitions. The proposed method is composed of two steps: segmentation of the TA and creation of the 3D geometric model. The segmentation algorithm, based on Level Set, was set and applied to healthy subjects acquired in three different modalities (with and without SENSE reduction factors). Accuracy was evaluated using standard quality indices. The 3D model is characterized by the vessel surface mesh and its centerline; the comparison of models obtained from the three different datasets was also carried out in terms of radius of curvature (RC) and average tortuosity (AT). In all datasets, the segmentation quality indices confirmed very good agreement between manual and automatic contours (average symmetric distance < 1.44 mm, DICE Similarity Coefficient > 0.88). The 3D models extracted from the three datasets were found to be comparable, with differences of less than 10% for RC and 11% for AT. Our method was found effective on PCMRI data to provide a 3D geometric model of the TA, to support morphometric and hemodynamic characterization of the aorta.

New estimation method of the contrast parameter for the Perona–Malik diffusion equation

The aim of this contribution is to make an efficient smoothing algorithm that preserves edges and provides valuable information for any segmentation process. The non-linear anisotropic diffusion (AD) model of Perona–Malik is considered to enhance the edges in the process of diffusion through a variable diffusion coefficient. However, the diffusion coefficient is very sensitive to the so-called contrast or gradient threshold parameter. This article proposes a novel methodology for the estimation of this contrast parameter based on a partition of the image using the K-means algorithm and a least-square fit to approximate the diffusion coefficient (KMLS). The experimental results show that the quality of the edge detection process improves when the proposed algorithm in the smoothing AD is used instead of the traditional techniques. The comparison is performed using two objective edge detection performance measures, the so-called Pratt's figure of merit and the symmetric average distance. Both measures show great improvements if we use the Perona–Malik equation with the KMLS estimator.

The Role of Environmental and Spatial Processes in Structuring Stream Macroinvertebrates Communities in a Large River Basin

Understanding spatial patterns of stream biological communities is important for stream ecosystem management. The questions which variables (spatial variables vs. environmental variables) are driving stream community structure, and whether stream network configuration could influence spatial patterns of the structure, are less investigated. We used stream macroinvertebrates community and environmental data collected from 142 sites in August 2010 across the Huai River basin, China, to answer these questions. Spatial variables were constructed based on symmetrical Euclidean distance and stream distance by using Moran's eigenvector maps. We evaluated the relative impacts of local environmental (physicochemical variables) and spatial (generated by overland and watercourse distances among sites) variables on the structure of macroinvertebrates using variation partitioning methods. Our results showed that both space and environment had significant effects on macroinvertebrates community composition, and the former explained more variations. Moreover, spatial variables based on the watercourse distance captured slightly more variation of macroinvertebrates community in stream networks than those derived from the overland distance.