Autocorrelation Technique Research Articles

Improving microvascular sensitivity of color Doppler using phase mask based flow recycling algorithm


Blood flow sensitivity is a crucial metric for appraising the effectiveness of color Doppler flow imaging (CDFI). Color Doppler velocity maps based on classic autocorrelation techniques are widely used in clinical practice. However, these techniques often produce twinkling artifacts in noisy regions due to the inherent randomness of noise phases. To mitigate artifacts and improve image quality, Power Mask (PoM) technology becomes imperative. Nevertheless, PoM technology unintentionally filters out small flow signals that have similar power and frequency characteristics to noise signals, thereby reducing the imaging system's sensitivity to flow. 
Approach:
To address this issue, a novel Flow Recycling Algorithm (FRA) based on phase anomaly is introduced in this study. This algorithm, excavating small flow signals from noise, aims to enhance the small flow signals with low-velocity by the phase characteristics of the color Doppler flow information. 
Main results: 
Experiments in multi-organ imaging have shown that the FRA-CDFI approach is more effective in suppressing twinkling artifacts in noisy regions, preserving intricate small flow signals, and markedly improving small blood flow sensitivity. This novel approach provides adequate technical support for clinical ultrasound imaging of organs with dense small blood vessels, such as the brain, kidneys, liver, and more. 
Significance: 
As a novel post-processing method, FRA-CDFI holds significant potential for future deployment in clinical high-frame-rate ultrasound imaging devices.

Spatial variations in health service utilization among migrant population: a perspective on health equity.

As health equity becomes a prioritized goal in global health policy, extensive research has revealed that socio-economic and geographical factors jointly exacerbate barriers to medical service access for both internal and international migrant populations, further accelerating existing health disparities. This study explores healthcare service utilization disparities among internal migrants in China, a population profoundly affected by the country's economic reforms and urbanization since the late 1970s. These transformations have led to significant migratory movements and subsequent healthcare challenges for these populations. Leveraging data from the 2017 China Migrant Dynamic Survey, comprising 169,989 samples across 28 provinces, we introduce a novel metric-the "No Treatment ratio" (NT-ratio). This ratio quantifies the proportion of migrants who, after falling ill, choose not to seek treatment relative to the total migrant population in a given province or region, serving as a critical measure of health risk. Building upon Anderson's Behavioral Model of Health Services Use, we adapted the model to better reflect the unique circumstances of migrant populations. The study employs spatial autocorrelation, hotspot analysis, and geodetector techniques to dissect the multifaceted factors influencing healthcare disparities. Our Findings reveal that the NT-ratio is significantly higher in eastern and northeastern China. Key factors influencing the NT-ratio include age, left-behind experiences, health education, and per capita medical resources. In response to these disparities, we recommend optimizing the distribution of medical resource, strengthening tiered diagnosis and treatment systems, and integrating health, education, and social security resources. These measures aim to improve healthcare utilization among migrant populations and reduce health inequities, aligning with global health objectives.

Advanced Integration of Urban Street Greenery and Pedestrian Flow: A Multidimensional Analysis in Chengdu’s Central Urban District

As urbanization accelerates, urban greenery, particularly street greenery, emerges as a vital strategy for enhancing residents’ quality of life, demanding attention for its alignment with pedestrian flows to foster sustainable urban development and ensure urban dwellers’ wellbeing. The advent of diverse urban data has significantly advanced this area of study. Focusing on Chengdu’s central urban district, this research assesses street greening metrics against pedestrian flow indicators, employing spatial autocorrelation techniques to investigate the interplay between street greenery and pedestrian flow over time and space. Our findings reveal a prevalent negative spatial autocorrelation between street greenery and pedestrian flow within the area, underscored by temporal disparities in greenery demands across various urban functions during weekdays versus weekends. This study innovatively incorporates mobile phone signal-based population heat maps into the mismatch analysis of street greenery for the first time, moving beyond the conventional static approach of space syntax topology in assessing pedestrian flow. By leveraging dynamic pedestrian flow data, it enriches our understanding of the disconnect between street greening plans and pedestrian circulation, highlighting the concept of urban flow and delving into the intricate nexus among time, space, and human activity. Moreover, this study meticulously examines multiple street usage scenarios, reflecting diverse behavior patterns, with the objective of providing nuanced and actionable strategies for urban renewal initiatives aimed at creating more inviting and sustainable urban habitats.

Automatic gait events detection with inertial measurement units: healthy subjects and moderate to severe impaired patients

BackgroundRecently, the use of inertial measurement units (IMUs) in quantitative gait analysis has been widely developed in clinical practice. Numerous methods have been developed for the automatic detection of gait events (GEs). While many of them have achieved high levels of efficiency in healthy subjects, detecting GEs in highly degraded gait from moderate to severely impaired patients remains a challenge. In this paper, we aim to present a method for improving GE detection from IMU recordings in such cases.MethodsWe recorded 10-meter gait IMU signals from 13 healthy subjects, 29 patients with multiple sclerosis, and 21 patients with post-stroke equino varus foot. An instrumented mat was used as the gold standard. Our method detects GEs from filtered acceleration free from gravity and gyration signals. Firstly, we use autocorrelation and pattern detection techniques to identify a reference stride pattern. Next, we apply multiparametric Dynamic Time Warping to annotate this pattern from a model stride, in order to detect all GEs in the signal.ResultsWe analyzed 16,819 GEs recorded from healthy subjects and achieved an F1-score of 100%, with a median absolute error of 8 ms (IQR [3–13] ms). In multiple sclerosis and equino varus foot cohorts, we analyzed 6067 and 8951 GEs, respectively, with F1-scores of 99.4% and 96.3%, and median absolute errors of 18 ms (IQR [8–39] ms) and 26 ms (IQR [12–50] ms).ConclusionsOur results are consistent with the state of the art for healthy subjects and demonstrate a good accuracy in GEs detection for pathological patients. Therefore, our proposed method provides an efficient way to detect GEs from IMU signals, even in degraded gaits. However, it should be evaluated in each cohort before being used to ensure its reliability.

Spectrum estimation for voiced speech using average weighted linear prediction

In many different areas, such as neurophysics, geophysics, and communication, it is vital to effectively handle signals when there is unwanted disturbance. Linear prediction (LP) methods provide precise modeling capabilities for various parametric models and are used in a wide range of fields such as predicting future data, compressing speech, analyzing spectra, filling in missing signals, and decreasing noise. LP is especially beneficial in speech processing systems. Speech analysis and synthesis greatly relies on spectral envelopes, which are closely connected to models of speech production and perception. However, the performance of the LP method may decline when limited information is involved in pitch synchronous analysis. In order to overcome this constraint, we developed the implementation of the Average Weighted Linear Prediction (AWLP) technique, which does not require the use of a window. The AWLP utilizes a signal of weighted prediction error acquired from the autocorrelation process of LP. By performing this action, it provides a more precise estimation of the power spectrum and coefficients used for vocal tract parametric models. Importantly, the effectiveness of the AWLP can be seen in both pitch synchronous and asynchronous analyses, which means it is appropriate for situations where segments are chosen randomly. We confirm the effectiveness of the AWLP technique by employing it on both synthetic voice and real speech. Additionally, we assess its effectiveness by comparing it to the direct autocorrelation technique and a modified autocorrelation (MA) approach. To sum up, the AWLP method presents a hopeful solution for reliable spectral analysis that addresses the divide between pitch synchronous and asynchronous situations.

Influence of land use change on habitat quality: a case study of coal mining subsidence areas.

Revealing the spatiotemporal evolution characteristics and key driving processes behind the habitat quality is of great significance for the scientific management of production, living, and ecological spaces in resource-based cities, as well as for the efficient allocation of resources. Focusing on the largest coal-mining subsidence area in Jiangsu Province of China, this study examines the spatiotemporal evolution of land use intensity, morphology, and functionality across different time periods. It evaluates the habitat quality characteristics of the Pan'an Lake area by utilizing the InVEST model, spatial autocorrelation, and hotspot analysis techniques. Subsequently, by employing the GTWR model, it quantifies the influence of key factors, unveiling the spatially varying characteristics of their impact on habitat quality. The findings reveal a notable surge in construction activity within the Pan'an Lake area, indicative of pronounced human intervention. Concurrently, habitat degradation intensifies, alongside an expanding spatial heterogeneity in degradation levels. The worst habitat quality occurs during the periods of coal mining and large-scale urban construction. The escalation in land use intensity emerges as the primary catalyst for habitat quality decline in the Pan'an Lake area, with other factors exhibiting spatial variability in their effects and intensities across different stages.

Analysing the Grain size and asymmetry of the particle distribution using auto-correlation technique

Analysing the Grain size and asymmetry of the particle distribution using auto-correlation technique

Investigating ultra-low frequency emissions and total electron content anomalies as earthquake precursors in Sumatra (2019–2020)

Ultra-low frequency (ULF) emissions and total electron content (TEC) anomalies are potential earthquake precursors for short-term earthquake prediction. This study comprehensively examines ULF emissions and TEC anomalies associated with the Sumatra earthquakes spanning 2019–2020. Precursor ULF emissions are identified through the polarization power ratio calculations involving Z-component and H-component geomagnetic data, meticulously recorded by the Magnetik Acquisition Data System (MAGDAS). Employing the Single Station Function Transfer technique, ULF anomalies directed toward the epicenter of earthquakes are recognized as reliable precursors. Among the 29 earthquakes scrutinized, 25 were heralded by ULF emissions acting as precursors. Among these, 15 instances solely exhibited ULF emissions, unaccompanied by TEC anomalies, while the remaining ten were linked to TEC anomalies. The investigation of TEC anomalies employs advanced autocorrelation techniques rooted in the analysis of correlation coefficient deviations. TEC anomalies are meticulously acquired from the observations of the Sumatran GPS Array (SuGAr) stations. Furthermore, this study illuminates a positive correlation (R = 0.65) between the lead time of ULF emissions and TEC anomalies. Moreover, the frequency of ULF emissions and TEC anomalies also showcases a direct relationship with earthquake magnitude. Heightened frequencies of ULF emissions and TEC anomalies as earthquake precursors correspond to more significant magnitudes in ensuing earthquakes. The outcomes of this research significantly augment the understanding of employing ULF emissions and TEC anomalies as effective earthquake precursors in the context of Sumatra.

Response time of mixed platoons with traditional and autonomous vehicles in field trials: impact assessment on flow stability and safety

This study investigates the response times of autonomous vehicles (AVs) equipped with adaptive cruise control (ACC) and traditional human-driven vehicles (TVs) in mixed traffic scenarios. The primary objective is to assess how these response times impact the stability and safety of mixed traffic flow, considering the growing prevalence of ACC technology in vehicles worldwide. Utilising a trajectory dataset from OpenACC totalling 3389.70 s, this research introduces a response time estimation framework that combines cross-correlation and partial autocorrelation techniques. The study calibrates Gazis, Herman, and Rothery's (GHR) car-following model to evaluate mixed traffic flow stability and employs a modified time-to-collision (MTTC) surrogate for safety analysis. The study also delves into the influence of vehicle manufacturer diversity on study outcomes. Key findings reveal that the AVs exhibit significantly longer response times, ranging from 1.10–3.20 s, compared to the 0.30–1.90-second range of traditional vehicles (p value < 0.005). These extended response times in AVs contribute to prolonged traffic flow instability and increased traffic conflicts. Moreover, the type of lead vehicle does not significantly affect the response times of either AVs or TVs (p value > 0.005). The study also highlights that vehicle manufacturer diversity does not substantially affect these response times. Additionally, the examination of fitted GHR parameters underscores AVs’ heightened sensitivity to spacing and relative speed, providing insights into AV dynamics in the presence of mixed traffic.

Carbon Emission and Annual Income in the Britain in 2019: An Intra-regional Spatial Analysis

This study conducts a comprehensive spatial analysis of carbon emissions and annual income in Great Britain in 2019 using data from the Office for National Statistics (ONS) and the Annual Survey of Hours and Earnings (ASHE). Using spatial autocorrelation techniques, especially Local Morans I, I identify significant spatial clusters, where Low - Low carbon emission areas are located in Manchester, West Yorkshire, Dorset and Wiltshire. In contrast, High-High clusters occur in Moray, the Midlands, South Yorkshire and Lancashire, highlighting areas where carbon emissions are spatially correlated. In addition, this research uses geographically weighted regression (GWR) analysis to identify any existing relationship between annual income and carbon emissions with accounting of geography. The results show a consistent trend in the strength of this relationship, decreasing from northwest to southeast across the Great Britain. The highest coefficients are concentrated in eastern regions such as the South East, London and the East of England. This research aims to contribute to the understanding of spatial heterogeneity of the local environmental economics by informing relevant stakeholders and politicians about which regions are most in demand of targeted interventions. By identifying vulnerable areas, this study provides valuable insights into the UK's pursuit of sustainable development and low-carbon economy with the global movement towards green economy.

Method for Predicting Transverse Wave Velocity Using a Gated Recurrent Unit Based on Spatiotemporal Attention Mechanism

Abstract Transverse wave velocity plays an important role in seismic exploration and reservoir assessment in the oil and gas industry. Due to the lack of transverse wave velocity data from actual production activities, it is necessary to predict transverse wave velocity based on longitudinal wave velocity and other reservoir parameters. This paper proposes a fusion network based on spatiotemporal attention mechanism and gated recurrent unit (STAGRU) due to the significant correlation between the transverse wave velocity and reservoir parameters in the spatiotemporal domain. In the case of tight sandstone reservoirs in the Junggar Basin, the intersection plot technique is used to select four well logging parameters that are sensitive to transverse wave velocity: longitudinal wave velocity, density, natural gamma, and neutron porosity. The autocorrelation technique is employed to analyze the depth-related correlation of well logging curves. The relationship between the spatiotemporal characteristics of these well logging data and the network attention weights is also examined to validate the rationale behind incorporating the spatiotemporal attention mechanism. Finally, the actual measurement data from multiple wells are utilized to analyze the performance of the training set and test set separately. The results indicate that the predictive accuracy and generalization ability of the proposed STAGRU method are superior to the single-parameter fitting method, multiparameter fitting method, Xu-White model method, GRU network, and 2DCNN-GRU hybrid network. This demonstrates the feasibility of the transverse wave velocity prediction method based on the spatiotemporal attention mechanism in the study of rock physics modeling for tight sandstone reservoirs.

Clustering analysis of the seismicity of Van Province and its surroundings via spatial autocorrelation techniques filters

Clustering analysis of the seismicity of Van Province and its surroundings via spatial autocorrelation techniques filters

Expanding the field of view for a scattering imaging system by illumination modulating with attenuation plates

Seeing through a scattering medium is challenging in optical imaging. The speckle autocorrelation technique based on the optical memory effect (OME) is one of the most promising methods for the noninvasive observation of hidden objects. However, the field of view is usually limited to a very small area where the optical memory effect is exerted. Here, we propose and experimentally demonstrate the “ratio function extremum method” to image two separated but small objects regardless of the optical memory effect range. The independent speckle components of each object are blindly separated from two captured mixed speckle patterns by modulating the illuminating light intensity. With these separated, independent speckle images, we can easily reconstruct the two hidden objects located at different memory effect ranges using the famous speckle autocorrelation technique. In this sense, our method enlarges the field of view regarding the typical speckle autocorrelation imaging technique which relied on the OME principle. Additionally, we demonstrate that our method has high efficiency in separating the speckle components.

Exploring spatial patterns of cardiovascular disease among women in India

Abstract Cardiovascular disease (CVD) has become a leading cause of mortality and morbidity in India, surpassing the global rate. While CVD is influenced by gender due to sex-specific risk factors in women, it remains underdiagnosed, and undertreated in women. WHO provides a list of ‘best buys’ which does not consider sex-specific risk factors. Hence, we assessed prevalence, unawareness and spatial variations of CVDs in women (15-49 years). We used NFHS (2019-20) WHO causal links between underlying drivers, behavioral risk factors, metabolic/physiological risk factors, and CVDs. We used ordinary kriging interpolation techniques to measure the spatial heterogeneity of CVD prevalence and spatial autocorrelation techniques using global Moran I statistic. Furthermore, ordinary least square were used to examine spatial predictors and geographically weighted regression and multiscale geographically weighted regression model were used to map the local spatial variations. CVDs prevalence in women is 11.7% [95%, CI: 0.117-0.118], its unawareness is 12% [95%, CI: 0.116-0.117] among women. Spatial regression analysis suggests observed geographical variation of CVDs significantly correlated with risk factors- metabolic (BMI), behavioral (tobacco consumption), and underlying (sex-specific - women's education as a proxy for autonomy and socioeconomic depriviation- household wealth). India has a high burden of CVDs in women, with state-level disparities and a lack of awareness about the disease. Women are not a target population for CVDs in SDG 3.4 and India's National Health policy, 2017; however, its rising epidemic burden on women demands a need for policy choices targeted at women. The study provides evidence to inform the policy choices in reducing CVDs burden in women. It suggests a need to develop gender-specific guidelines for CVD prevention and management in India. Key messages • The study provides evidence to inform the policy choices in reducing CVDs burden in women and suggests a need to develop gender-specific guidelines for CVD prevention and management in India. • India has high burden &amp; unawareness of CVDs in women with risk factors- BMI, tobacco consumption, sex-specific - women's education as a proxy for autonomy; socioeconomic deprivation- household wealth.

Spatial comparison of London's three waves of Spanish Flu.

England and Wales experienced three waves of influenza during the 1918/19 Spanish Flu pandemic. A previous analysis showed that these three waves had fundamentally different spatial and temporal characteristics. This present study compares London's experience of the three waves to discern possible geographic differences on a metropolitan level. Borough mortality data for each wave were normalized and then scaled, with spatial autocorrelation techniques displayed by GIS software and analysed for each wave. Registrar General in England and Wales reporting provided data concerning measures of 'health' and 'wealth' for each metropolitan borough. Spearman's rank correlation determined the correlation of each wave's mortality to each of the other waves including the 'health,' 'wealth' and population density factors. The comparisons showed that there is a spatial difference among the waves. The first two are spatially similar, with both exhibiting 'random' autocorrelation patterns, while the third wave exhibits a 'clustered' pattern. The borough mortality of the first two waves strongly correlated with each other, with both having similar 'health,' 'wealth' and population density factors. However, the third wave's mortality did not correlate with any of the first two and actually behaved in an opposite manner with regard to the 'health,' 'wealth,' and population density factors. These results do not appear in the literature and create new opportunities for research to explain London's mortality during the Spanish Flu pandemic of 1918/19.

Measurements of visual double stars with PISCO2 at the Nice 76‐cm refractor in 2013–2014 (new data) and in 2015

AbstractWe present relative astrometric and photometric measurements of visual double stars made in 2013–2015, with PISCO2 installed at the 76‐cm refractor of Côte d'Azur Observatory in Nice (France). Our observing list contains orbital couples as well as double stars whose motion is still uncertain. Most of the observations were done in 2015, but some other observations of wide couples were done in 2013–2014 (0.3% of the total of the observations). Three different techniques were used for obtaining measurements: lucky imaging, speckle interferometry and the direct vector autocorrelation method. From our observations of 2837 multiple stars, we obtained 5182 new measurements with angular separations in the range of 0.1–32 and an average accuracy of . The mean error on the position angles is 0°.8. Most of the position angles were determined without the usual 180° ambiguity with the application of the direct vector autocorrelation technique and/or by inspection of the Lucky images or the long integration files. We managed to routinely monitor faint systems () with large magnitude difference (up to ). We have thus been able to measure 21 systems containing red dwarf stars that had been poorly monitored since their discovery, from which we estimated the stellar masses thanks to Gaia measurements. We also measured the magnitude difference of the two components of 1079 double stars with an estimated error of 0.2 mag. Except for a few objects that are discussed, our measurements are in good agreement with the ephemerides computed with published orbital elements, even for the double stars whose separation is smaller than the diffraction limit. Thanks to good seeing images and with the use of high‐contrast numerical filters, we have also been able to obtain 196 measurements with an angular separation smaller than the diffraction limit of our instrumentation, and consistent with those obtained with larger telescopes. We also report measurements of the 164 new double stars that we found in the files obtained during the observations. Finally, from a study of the DR3 Gaia release, it is shown that all the objects of the Tycho Double Star Catalog that we did not resolve in 2015 are probably false detections by Tycho.

Analysis of Doppler spectrum of a spaceborne Doppler scatterometer using an echoed signal simulation model

ABSTRACT The Doppler spectrum of a microwave scatterometer signal is determined by the relative motion between the scatterometer and the ocean surface. It is also determined by the modulation from the ocean surface backscattering coefficient. The relative motion includes contributions mainly from the motion of the platform, the phase velocity of the resonant Bragg waves, the orbital motions of ocean waves, and the ocean surface current. Estimating the Doppler centroid from the complex Doppler spectrum of the received signal is an important issue for the application of a spaceborne Doppler scatterometer in ocean surface current retrieval. In this study, we use an echoed signal simulation model based on a two-dimensional rough dynamic ocean surface to estimate the Doppler spectrum of a spaceborne Doppler scatterometer. We analyze the Doppler spectrum with different observation geometry and ocean state, and the effects of modulation on the Doppler spectrum with the pulse-pair method. Our results show that the high platform velocity and the modulation of the backscattering coefficient from the ocean surface are the main influencing factors that affect the shape of the Doppler spectrum of the received signal, which affects the accuracy of estimation of the Doppler centroid. For a Ka-band pencil-beam rotating Doppler scatterometer using the pulse-pair method, the modulation can lead to a measurement error of 1.2 m·s−1.

Spatial Differentiation and Driving Factors of Traditional Villages in Jiangsu Province

Jiangsu Province, situated in the Yangtze River basin, has rich traditional village resources and a prominent position in economic development and cultural integration. This study focuses on the analysis of the variation distribution pattern of traditional villages in Jiangsu Province using six batches of traditional village directories with data until 2023 as research samples. By employing ANN, Voronoi graph analysis, and Moran’s I index, the researchers determined the spatial distribution characteristics of rural settlements. Additionally, kernel density and spatial autocorrelation techniques were used to further examine the spatial distribution patterns, and geographic detector detection was introduced. The results showed the following: (1) The spatial distribution of traditional village settlements in Jiangsu Province showed a significant clustering distribution that is mainly concentrated in central Jiangsu Province. (2) The driving factors reflected a strong symbiotic relationship of “air–water–soil–man”. The spatial distribution of traditional villages was mainly driven by the annual mean temperature and soil type. The interaction between factors was dominated by the enhancement relationship between the two factors. (3) According to the detection results of risk areas in the region, the average annual temperature was 17~17.6 °C, the annual precipitation was 133.0~145.7 billion m3, the average annual wind speed was 0.549~0.565 m/s, the GDP was 85,100~204,000 CNY/km−2, and the population density was 2.32~3.91 thousand/km−2. Arable land was the main type of area and was conducive to the gathering of traditional villages. The preservation of rural settlements should take into account the complex and diverse factors that affect their distribution. Additionally, it is crucial to tailor protection strategies to specific local conditions and conduct flexible research.

Rotation in Event Horizon Telescope Movies

The Event Horizon Telescope (EHT) has produced images of M87* and Sagittarius A*, and will soon produce time sequences of images, or movies. In anticipation of this, we describe a technique to measure the rotation rate, or pattern speed Ω p , from movies using an autocorrelation technique. We validate the technique on Gaussian random field models with a known rotation rate and apply it to a library of synthetic images of Sgr A* based on general relativistic magnetohydrodynamics simulations. We predict that EHT movies will have Ω p ≈ 1° per GMc −3, which is of order 15% of the Keplerian orbital frequency in the emitting region. We can plausibly attribute the slow rotation seen in our models to the pattern speed of inward-propagating spiral shocks. We also find that Ω p depends strongly on inclination. Application of this technique will enable us to compare future EHT movies with the clockwise rotation of Sgr A* seen in near-infrared flares by GRAVITY. Pattern speed analysis of future EHT observations of M87* and Sgr A* may also provide novel constraints on black hole inclination and spin, as well as an independent measurement of black hole mass.

Enhancement of Ultrasound B-Mode Image Quality Using Nonlinear Filtered-Multiply-and-Sum Compounding for Improved Carotid Artery Segmentation.

In ultrasound B-mode imaging, the axial resolution (AR) is commonly determined by the duration or bandwidth of an excitation signal. A shorter-duration pulse will produce better resolution compared to a longer one but with compromised penetration depth. Instead of relying on the pulse duration or bandwidth to improve the AR, an alternative method termed filtered multiply and sum (FMAS) has been introduced in our previous work. For spatial-compounding, FMAS uses the autocorrelation technique as used in filtered-delay multiply and sum (FDMAS), instead of conventional averaging. FMAS enables a higher frame rate and less computational complexity than conventional plane-wave compound imaging beamformed with delay and sum (DAS) and FDMAS. Moreover, it can provide an improved contrast ratio and AR. In previous work, no explanation was given on how FMAS was able to improve the AR. Thus, in this work, we discuss in detail the theory behind the proposed FMAS algorithm and how it is able to improve the spatial resolution mainly in the axial direction. Simulations, experimental phantom measurements and in vivo studies were conducted to benchmark the performance of the proposed method. We also demonstrate how the suggested new algorithm may be used in a practical biomedical imaging application. The balloon snake active contour segmentation technique was applied to the ultrasound B-mode image of a common carotid artery produced with FMAS. The suggested method is capable of reducing the number of iterations for the snake to settle on the region-of-interest contour, accelerating the segmentation process.