Multiple Distance Measurements Research Articles

Correlation Among Neighborhood-level Measures of the Tobacco Retail Environment.

Tobacco retailer density and distance to tobacco retailers are understood to influence tobacco-related behaviors; however, there is no general agreement on how to best characterize or measure the tobacco retail environment (TRE). In this data-driven analysis, we examine similarities among neighborhood-level measures of the TRE and assess how geographic resolution of the neighborhood units may affect them. We used locations of likely tobacco retailers in the United States (US) to calculate multiple retailer count, density, and distance measures. Measures were calculated at the Census block group, tract, and county (including county equivalents) levels of geographic aggregation. Spearman's correlation was used to evaluate similarity among the TRE measures. At the block group and tract level, correlation among all TRE measures ranged from slightly negative (ρ = -0.03) to nearly perfect (ρ = 0.99). At both levels of aggregation, distance-based TRE measures were highly correlated (ρ > 0.76). At the block group level, simple count of retailers was highly correlated with the density measures (ρ > 0.83), and at the tract level, simple count was moderately to highly correlated with the density measures (ρ > 0.5). Findings were generally similar at the county level; a notable deviation was that retailers per person was negatively correlated with all other TRE measures (range from ρ = -0.08 to ρ = -0.32). Some common measures were not correlated, suggesting they capture different aspects of the TRE; similarity among the various measures also varied by level of geographic aggregation. Because the tobacco retail environment shapes people's tobacco-related behaviors, using appropriate measures to characterize it at a neighborhood-level is paramount. Our work highlights both the similarities and differences among a set of common measures, thereby suggesting the measures may be capturing different aspects of the overall retail environment. Our findings regarding geographic level of aggregation underscore the importance of neighborhood definition in any tobacco retail environment analysis.

A Multi-Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views.

The categorization of human diseases is mainly based on the affected organ system and phenotypic characteristics. This is limiting the view to the pathological manifestations, while it neglects mechanistic relationships that are crucial to develop therapeutic strategies. This work aims to advance the understanding of diseases and their relatedness beyond traditional phenotypic views. Hence, the similarity among 502 diseases is mapped using six different data dimensions encompassing molecular, clinical, and pharmacological information retrieved from public sources. Multiple distance measures and multi-view clustering are used to assess the patterns of disease relatedness. The integration of all six dimensions into a consensus map of disease relationships reveals a divergent disease view from the International Classification of Diseases (ICD), emphasizing novel insights offered by a multi-view disease map. Disease features such as genes, pathways, and chemicals that are enriched in distinct disease groups are identified. Finally, an evaluation of the top similar diseases of three candidate diseases common in the Western population shows concordance with known epidemiological associations and reveals rare features shared between Type 2 diabetes (T2D) and Alzheimer's disease. A revision of disease relationships holds promise for facilitating the reconstruction of comorbidity patterns, repurposing drugs, and advancing drug discovery in the future.

A neural network model to optimize the measure of spatial proximity in geographically weighted regression approach: a case study on house price in Wuhan

The estimation of spatial heterogeneity within real estate markets holds significant importance in house price modelling. However, employing a single or straightforward distance to measure spatial proximity is probably insufficient in complex urban areas, thereby resulting in an inadequate modelling of spatial heterogeneity. To address this issue, this paper incorporates multiple distance measures within a neural network framework to achieve an optimized measure of spatial proximity (OSP). Consequently, a geographically neural network weighted regression model with optimized measure of spatial proximity (osp-GNNWR) is devised for the purpose of spatially heterogeneous modeling. Trained as a unified model, osp-GNNWR obviates the need for separate pretraining of OSP. This enables OSP to delineate the modeled spatial process through a post hoc calculated value. Through simulation experiments and a real-world case study on house prices, the proposed model reaches more accurate descriptions of diverse spatial processes and exhibits better overall performance. The interpretable results of the case study in Wuhan demonstrate the efficacy of the osp-GNNWR model in addressing spatial heterogeneity within real estate markets, suggesting its potential for modelling and predicting complex geographical phenomena.

Three Decades of REDOR in Protein Science: A Solid-State NMR Technique for Distance Measurement and Spectral Editing.

Solid-state NMR (ss-NMR) is a powerful tool to investigate noncrystallizable, poorly soluble molecular systems, such as membrane proteins, amyloids, and cell walls, in environments that closely resemble their physical sites of action. Rotational-echo double resonance (REDOR) is an ss-NMR methodology, which by reintroducing heteronuclear dipolar coupling under magic angle spinning conditions provides intramolecular and intermolecular distance restraints at the atomic level. In addition, REDOR can be exploited as a selection tool to filter spectra based on dipolar couplings. Used extensively as a spectroscopic ruler between isolated spins in site-specifically labeled systems and more recently as a building block in multidimensional ss-NMR pulse sequences allowing the simultaneous measurement of multiple distances, REDOR yields atomic-scale information on the structure and interaction of proteins. By extending REDOR to the determination of 1H-X dipolar couplings in recent years, the limit of measurable distances has reached ~15-20 Å, making it an attractive method of choice for the study of complex biomolecular assemblies. Following a methodological introduction including the most recent implementations, examples are discussed to illustrate the versatility of REDOR in the study of biological systems.

Mobile user identification based on road networks and multiple distance measures

AbstractMobile user identification aims at matching different mobile devices of the same user using trajectory data, which has attracted extensive research in recent years. Most of the previous work extracted trajectory features based on regular grids, which will lead to incorrect feature representation due to lack of geographic information. Besides, most trajectory similarity models only considered one single distance measure to calculate the similarity between users, which ignore the connection between different distance measures and may lead to some false matches. In light of this, we present a novel user identification method based on road networks and multiple distance measures in this article. The proposed method segments a city map into several grids and road segments based on road networks. Then it extracts location and road information of trajectories to jointly construct user features. Multiple distance measures are fused by a discriminant model to improve the effect of user identification. Experiments on real GPS trajectory datasets show that our proposed method outperforms related similarity measure methods and is stable for mobile user identification. Meanwhile, our method can also achieve good identification results even on sparse trajectory datasets.

Hybrid Clustering Approach for Time Series Data

The clustering of data series was already demonstrated to provide helpful information in several fields. Initial data for the period is divided into sub-clusters Recorded in the data resemblance. The grouping of data series takes 3 categories, based on which users operate in frequencies or programming interfaces on original data explicitly or implicitly with the characteristics derived from physical information or through a framework based on raw material. The bases of series data grouping are provided. The conditions for the evaluation of the outcomes of grouping are multi-purpose time constant frequently employed in dataset grouping research. A clustering method splits data into different groups so that the resemblance between organisations is better. K-means++ offers an excellent convergence rate compared to other methods. To distinguish the correlation between items the maximum distance is employed. Distance measure metrics are frequently utilized with most methods by many academics. Genetic algorithm for the resolution of cluster issues is worldwide optimization technologies in recent times. The much more prevalent partitioning strategies of large volumes of data are K-Median & K-Median methods. This analysis is focusing on the multiple distance measures, such as Euclidean, Public Square and Shebyshev, hybrid K-means++ and PSO clubs techniques. Comparison to orgorganization-basedthods reveals an excellent classification result compared to the other methods with the K++ PSO method utilizing the Chebyshev distance measure.

Multilateration with Self-Calibration: Uncertainty Assessment, Experimental Measurements and Monte-Carlo Simulations

Large-volume metrology is essential to many high-value industries and contributes to the factories of the future. In this context, we have developed a tri-dimensional coordinate measurement system based on a multilateration technique with self-calibration. In practice, an absolute distance meter, traceable to the SI metre, is shared between four measurement heads by fibre-optic links. From these stations, multiple distance measurements of several target positions are then performed to, at the end, determine the coordinates of these targets. The uncertainty on these distance measurements has been determined with a consistent metrological approach and it is better than 5 µm. However, the propagation of this uncertainty into the measured positions is not a trivial task. In this paper, an analytical solution for the uncertainty assessment of the positions of both targets and heads under a multilateration scenario with self-calibration is provided. The proposed solution is then compared to Monte-Carlo simulations and to experimental measurements: it follows that all three approaches are well agreed, which suggests that the proposed analytical model is accurate. The confidence ellipsoids provided by the analytical solution described well the geometry of the errors.

Lisfranc injury: Refined diagnostic methodology using weightbearing and non-weightbearing radiographs

BackgroundTo compare diagnostic parameters for Lisfranc instability on WB and NWB radiographs and to assess the inter-observer reliability of a standardized diagnostic protocol. Patients and methodsPatients who had undergone surgical treatment for subtle, purely ligamentous Lisfranc injury with both WB and NWB post-injury, pre-surgery films (n = 26) were included in this multicentre, retrospective comparative study. Also included was a control group (n = 26) of patients with isolated fifth metatarsal avulsion fractures who similarly had both WB and NWB films. Multiple midfoot distance and alignment measurements were used to evaluate the Lisfranc joint on both WB and NWB views. To evaluate interobserver reliability, measurements were made by two independent observers across a cohort subset. ResultsWhen comparing the NWB views between groups, only C1M2 (medial cuneiform- second metatarsal) distance was found to be significantly larger (∆ = 1.35 mm, p <0.001) for Lisfranc injuries. Most notably, C2M2 (Intermediate cuneiform – second metatarsal) step off—caused by lateral translation of the second metatarsal base—was not significantly different (∆ = 0.39 mm, p = 0.101) between Lisfranc patients and controls. On WB views, Lisfranc patients had significantly larger changes to C1M2 distance and C2M2 step-off as compared to controls (∆ = 2.97 mm, p <0.001 and ∆ = 1.98 mm, p <0.001 respectively). M1M2 (first to second intermetatarsal) distance was not significantly different between patients and controls in WB films. Within the cohort of ligamentous Lisfranc patients, C1M2 distance and C2M2 step-off were significantly larger in WB when compared to NWB films (∆ = 1.77 mm, p <0.001 and ∆ = 1.58 mm, p <0.001 respectively). For these parameters, inter-observer reliability scores (ICC) of >0. 90 were found when interpreting WB radiographs and ICC's ranging between 0.61 and 0.80 were found when interpreting NWB radiographs. ConclusionUsing WB imaging for diagnosing subtle Lisfranc instability reveals larger diastasis in the tarsometatarsal joint and has a higher interobserver reliability compared to NWB imaging. Clinical concern for subtle or occult Lisfranc instability in any patient should therefore trigger WB radiographic assessment since such injuries may be missed on NWB views.

Subject-transfer framework with unlabeled data based on multiple distance measures for surface electromyogram pattern recognition

To improve the initial accuracy of wearable sensor-driven human interfaces, inter-subject variabilities must be reduced through transfer learning. If subject transfer can be performed without labeling the target user’s calibration data, an interface that provides stable accuracy can be easily achieved without a cumbersome calibration protocol. Herein, we propose a subject-transfer framework based on multiple distance measures that enables subject transfer using only unlabeled calibration data by minimizing the distance between the data distributions of the target and the source. To assess the performance of this framework, we used two surface electromyogram databases (one private database and one public database called the NinaPro database 5) acquired from the same wearable sensor, the Myo Gesture Control Armband. The proposed framework improved the pattern recognition accuracy compared with well-established classifiers constructed from randomly selected source subject data. In the future, we will apply this framework to online human interfaces that are not based on a specific calibration protocol. The scripts used in this study can be downloaded from https://github.com/aistairc/Unlabeled_STM.

RFID-Based Indoor Positioning Using Edge Machine Learning

Indoor positioning of objects and people is becoming of great importance in the Internet of Things (IoT), in-home automation, and navigation in malls, airports, or very large buildings. Positioning is determined by multiple distance measurements between reference points and sensors. Distance measurement uses the time of flight of an ultrasonic signal traveling from an emitter to receiving sensors. This requires close synchronization between the emitter and the sensors and a sharp time resolution of the time of arrival (TOA) of the ultrasonic signal. Usually, TOA is detected using cross-correlation processing requiring significant computational resources at the sensors level. In this work, the synchronization is done using the RFID standard protocol features. The TOA detection is performed firstly by training off-line a Machine Learning model using as input the peaks indexes of the ultrasonic signal received and the output of a cross-correlation based positioning system, as ground-truth. In a second phase, the positioning is evaluated and tested on-board using the previously trained model on a microcontroller. The system architecture is presented and experimental results on the positioning accuracy are shown accordingly. Results show a mean positioning error below 25 cm in 95% of the positionings in a typical room.

An ensemble filter-based heuristic approach for cancerous gene expression classification

Gene expression data of cancer has a huge feature set size, making its categorization a challenge for the existing classification methods. It contains redundancy, noise, and irrelevant genes. Therefore, feature selection/reduction plays a crucial role in the classification of such gene expression datasets. This work presents an ensemble of three filter methods, namely, Symmetrical Uncertainty (SU), chi square (X2), and Relief to reduce the feature dimensions by eliminating redundant and noisy genes. The present work designs a novel heuristic called Local Search-based Feature Selection (LSFS) that further reduces noise generated by the ensemble method. The resulting selected features are then optimized using a genetic algorithm. Afterwards, the optimal set of features is classified using three models; Support Vector Machine (SVM), k-NN (k-nearest neighbor), and Random Forest (RF) to find cancer relevant genes. Experiments are conducted using six benchmark datasets. The obtained results are compared with five state-of-the-art algorithms based on accuracy, sensitivity, specificity, F-measure, entropy, and precision. Additional experiments are carried out by manipulating the SVM kernel as a fitness value as well as using multiple distance measures and various values of k for k-NN. Prediction accuracy of the proposed system on the six benchmark datasets is 99%, 90%, 98%, 94%, 98%, and 99%. Significant outcomes obtained from experimental analysis indicate that the proposed approach improves classification of cancerous gene expression data and can be used as a practical tool for the analysis of gene expression data.

Tully–Fisher Distances and Dynamical Mass Constraints for 24 Host Galaxies of Reverberation-mapped AGNs

Abstract We present Tully–Fisher distances for 24 active galactic nucleus (AGN) host galaxies with black hole mass (M BH ) measurements from reverberation mapping, as well as the first calibration of the V-band Tully–Fisher relation. Combining our measurements of H i 21 cm emission with Hubble Space Telescope and ground-based optical and near-infrared images allows multiple distance measurements for 19 galaxies and single measurements for the remaining 5. Separation of the nucleus from its host galaxy via surface brightness decomposition yields galaxy-only luminosities, thus allowing measurements of the distance moduli free of contamination from the AGNs. For 14 AGN hosts, these are the first reported distances independent of redshift, and hence independent of peculiar velocities. For the remaining galaxies, we show good agreement between our distances and those previously reported from surface brightness fluctuations and Cepheids. We also determine the total galaxy mass enclosed within the estimated H i radius, which when compared to the baryonic content allows for constraints on the dark matter masses. We find a typical mass fraction of M DM /M DYN = 62%, and find significant correlations between M BH –M DYN and M BH –M DM . Finally, we scale our galaxy radii based on estimated relationships between visible and halo radii and assume a flat rotation curve out to the halo radius to approximate M HALO . Over the range of M BH and M HALO in this sample, we find good agreement with observationally constrained relationships between M BH and M HALO and with hydrodynamical simulations.

Selective 1H-14N Distance Measurements by 14N Overtone Solid-State NMR Spectroscopy at Fast MAS.

Accurate distance measurements between proton and nitrogen can provide detailed information on the structures and dynamics of various molecules. The combination of broadband phase-modulated (PM) pulse and rotational-echo saturation-pulse double-resonance (RESPDOR) sequence at fast magic-angle spinning (MAS) has enabled the measurement of multiple 1H-14N distances with high accuracy. However, complications may arise when applying this sequence to systems with multiple inequivalent 14N nuclei, especially a single 1H sitting close to multiple 14N atoms. Due to its broadband characteristics, the PM pulse saturates all 14N atoms; hence, the single 1H simultaneously experiences the RESPDOR effect from multiple 1H-14N couplings. Consequently, no reliable H-N distances are obtained. To overcome the problem, selective 14N saturation is desired, but it is difficult because 14N is an integer quadrupolar nucleus. Alternatively, 14N overtone (OT) NMR spectroscopy can be employed owing to its narrow bandwidth for selectivity. Moreover, owing to the sole presence of two energy levels (m = ± 1), the 14N OT spin dynamics behaves similarly to that of spin-1/2. This allows the interchangeability between RESPDOR and rotational-echo double-resonance (REDOR) since their principles are the same except the degree of 14N OT population transfer; saturation for the former whereas inversion for the latter. As the ideal saturation/inversion is impractical due to the slow and orientation-dependent effective nutation of 14N OT, the working condition is usually an intermediate between REDOR and RESPDOR. The degree of 14N OT population transfer can be determined from the results of protons with short distances to 14N and then can be used to obtain long-distance determination of other protons to the same 14N site. Herein, we combine the 14N OT and REDOR/RESPDOR to explore the feasibility of selective 1H-14N distance measurements. Experimental demonstrations on simple biological compounds of L-tyrosine.HCl, N-acetyl-L-alanine, and L-alanyl-L-alanine were performed at 14.1 T and MAS frequency of 62.5 kHz. The former two consist of a single 14N site, whereas the latter consists of two 14N sites. The experimental optimizations and reliable fittings by the universal curves are described. The extracted 1H-14N distances by OT-REDOR are in good agreement with those determined by PM-RESPDOR and diffraction techniques.

MiRKAT: kernel machine regression-based global association tests for the microbiome.

Distance-based tests of microbiome beta diversity are an integral part of many microbiome analyses. MiRKAT enables distance-based association testing with a wide variety of outcome types, including continuous, binary, censored time-to-event, multivariate, correlated and high-dimensional outcomes. Omnibus tests allow simultaneous consideration of multiple distance and dissimilarity measures, providing higher power across a range of simulation scenarios. Two measures of effect size, a modified R-squared coefficient and a kernel RV coefficient, are incorporated to allow comparison of effect sizes across multiple kernels. MiRKAT is available on CRAN as an R package. Supplementary data are available at Bioinformatics online.

Uncertainty assessment of a prototype of multilateration coordinate measurement system

Large Volume Metrology is essential to many high value industries to go towards the factory of the future, but also to many science facilities for fine alignment of large structures. In this context, we have developed a multilateration coordinate measurement system, traceable to SI metre, and suitable for outdoor measurements or industrial environments. It is based on a high accuracy absolute distance meter developed in-house and shared between several measurement heads by fibre-optic links. Thus, from these measurement stations, multiple distance measurements of several positions of a target can be performed. At the end, coordinates of the heads and of the different target locations are determined using a multilateration algorithm with self-calibration.In this paper, the uncertainty of this multilateration coordinate measurement system is determined with a consistent metrological approach. First, 13 different sources of errors are listed and quantified. Then, thanks to Monte Carlo simulations, the standard uncertainty on a single absolute distance measurement is assessed to 4.7 μm. This includes the uncertainty contribution of the telemetric system itself, but also the contributions of the mechanical designs of the measurement heads and the target. Lastly, measurements of three-dimensional coordinates of target positions are performed in a control environment, then in a large workshop without temperature control: these measurements validate the uncertainty assessment of the system.

Utility of 1 Measurement Versus Multiple Measurements of Near Point of Convergence After Concussion.

Increased near point of convergence (NPC) distance is a common finding after concussion and is associated with physical symptoms and worsened neurocognitive performance. Vestibular/Ocular Motor Screening measures NPC distance across 3 trials and uses the average measurement to inform clinical care. However, whether 3 trials are necessary, are consistent, or add clinical utility is unknown. To investigate the consistency across 3 trials of NPC and establish the classification accuracy (ie, clinical utility) of 1 or 2 trials compared with the standardized average of 3 trials. Retrospective cohort study. Sports medicine clinic and research laboratory. Consecutively enrolled patients aged 10 to 22 years with diagnosed concussions (74% sport related; n = 380). The previously reported clinical cutoff value of ≥5 cm across 3 trials was used. Pearson correlation and intraclass correlation coefficients were used to evaluate agreement between trials and average scores. Reliable change indices (RCIs) using 95% confidence intervals were also calculated. The Pearson correlation (r = .98) and intraclass correlation (0.98) coefficients revealed excellent agreement between the first measurement and average NPC distance across 3 measurements. The RCI across all trials was 2 cm. When the first NPC measurement was ≤3 cm or ≥7 cm, agreement existed within the RCI between the first measurement and the average of 3 measurements in 99.6% of cases. When we averaged the first and second measurements, 379/380 (99.7%) participants had the same classification (ie, <5 cm = normal, ≥5 cm = abnormal) as the average NPC distance across 3 measurements. Our findings suggest limited utility of multiple or average NPC distance measurements when the initial NPC distance is outside of RCI clinical cutoffs (ie, ≤3 cm or ≥7 cm). Given the high consistency between the first measurement and average NPC distance across 3 trials, only 1 measurement of NPC distance is warranted unless the first measurement is between 3 and 7 cm.

Sift and Deep Convolutional Features for Closeness-Based Leaf Image Recognition

Plant leaf recognition has been carried out widely using low level features. Scale invariant feature transform techniques have been used to extract the low level features. Leaves that match based on low level features but do not do so in the semantic perspective cannot not be recognized. To address that, global features have been extracted and used using convolutional neural networks. Even then there are issues like leaf images in various illuminations, rotations, taken in different angles, and so on. To address such issues, the closeness among low level features and global features are computed using multiple distance measures and a leaf recognition framework has been proposed. The matched patches are evaluated both quantitatively and qualitatively. Experimental results obtained are promising for the proposed closeness-based leaf recognition framework.

Mobile Privacy: Scalable Ensemble Matching for User Identification Attacks

Mobile privacy is broadly concerning in the mobile big data era, as user mobility behaviors are privacy-sensitive and unique. User identification attacks consist of one of the most critical privacy concerns on mobile big data. In this paper, we study mobile privacy in terms of user identifiability from the perspective of privacy adversaries. User identification in two datasets from the same data source or two different data sources is generally formulated as a linear assignment problem (LAP), in which the cost matrix of users is generated by a single distance measure. However, user identification via one single distance measure may lead to a large portion of false matches, especially when only a few users coexist across these two datasets. In addition, the cubic computational complexity of LAP limits the scale of user identification analysis. In this paper, we propose a multi-feature ensemble matching framework to improve the user identification precision based on a majority voting rule, by integrating multiple distance measures. The computational complexity of the proposed ensemble matching algorithm is an order of magnitude less than that of the single-distance based approach, which results from solving an LAP on a highly sparse matrix rather than a dense matrix. Experiments demonstrate the superior performance of our proposed scalable ensemble matching framework with respect to matching precision as well as the vulnerability of mobile network subscribers' privacy.

Adaptive weighted locality-constrained sparse coding for glaucoma diagnosis.

Glaucoma is a sight-threading disease which can lead to irreversible blindness. Currently, extracting the vertical cup-to-disc ratio (CDR) from 2D retinal fundus images is promising for automatic glaucoma diagnosis. In this paper, we present a novel sparse coding approach for glaucoma diagnosis called adaptive weighted locality-constrained sparse coding (AWLCSC). Different from the existing reconstruction-based glaucoma diagnosis approaches, the weighted matrix in AWLCSC is constructed by adaptively fusing multiple distance measurement information between the reference images and the testing image, making our approach more robust and effective to glaucoma diagnosis. In our approach, the disc image is firstly extracted and reconstructed according to the proposed AWLCSC technique. Then, with the usage of the obtained reconstruction coefficients and a series of reference disc images with known CDRs, the CDR of the testing disc image can be automated estimation for glaucoma diagnosis. The performance of the proposed AWLCSC is evaluated on two publicly available DRISHTI-GS1 and RIM-ONE r2 databases. The experimental results indicate that the proposed approach outperforms the state-of-the-art approaches. Graphical abstract The flowchart of the proposed approach for glaucoma diagnosis.

Functional near infrared spectroscopy using spatially resolved data to account for tissue scattering: A numerical study and arm-cuff experiment.

Functional Near‐Infrared Spectroscopy (fNIRS) aims to recover changes in tissue optical parameters relating to tissue hemodynamics, to infer functional information in biological tissue. A widely‐used application of fNIRS relies on continuous wave (CW) methodology that utilizes multiple distance measurements on human head for study of brain health. The typical method used is spatially resolved spectroscopy (SRS), which is shown to recover tissue oxygenation index (TOI) based on gradient of light intensity measured between two detectors. However, this methodology does not account for tissue scattering which is often assumed. A new parameter recovery algorithm is developed, which directly recovers both the scattering parameter and scaled chromophore concentrations and hence TOI from the measured gradient of light‐attenuation at multiple wavelengths. It is shown through simulations that in comparison to conventional SRS which estimates cerebral TOI values with an error of ±12.3%, the proposed method provides more accurate estimate of TOI exhibiting an error of ±5.7% without any prior assumptions of tissue scatter, and can be easily implemented within CW fNIRS systems. Using an arm‐cuff experiment, the obtained TOI using the proposed method is shown to provide a higher and more realistic value as compared to utilizing any prior assumptions of tissue scatter.