Automated delineation of thyroid nodules in ultrasound images using spatial neutrosophic clustering and level set

An accurate contour estimation plays a significant role in classification and estimation of shape, size, and position of thyroid nodule. This helps to reduce the number of false positives, improves the accurate detection and efficient diagnosis of thyroid nodules. This paper introduces an automated delineation method that integrates spatial information with neutrosophic clustering and level-sets for accurate and effective segmentation of thyroid nodules in ultrasound images. The proposed delineation method named as Spatial Neutrosophic Distance Regularized Level Set (SNDRLS) is based on Neutrosophic L-Means (NLM) clustering which incorporates spatial information for Level Set evolution. The SNDRLS takes rough estimation of region of interest (ROI) as input provided by Spatial NLM (SNLM) clustering for precise delineation of one or more nodules. The performance of the proposed method is compared with level set, NLM clustering, Active Contour Without Edges (ACWE), Fuzzy C-Means (FCM) clustering and Neutrosophic based Watershed segmentation methods using the same image dataset. To validate the SNDRLS method, the manual demarcations from three expert radiologists are employed as ground truth. The SNDRLS yields the closest boundaries to the ground truth compared to other methods as revealed by six assessment measures (true positive rate is 95.45 ± 3.5%, false positive rate is 7.32 ± 5.3% and overlap is 93.15 ± 5. 2%, mean absolute distance is 1.8 ± 1.4 pixels, Hausdorff distance is 0.7 ± 0.4 pixels and Dice metric is 94.25 ± 4.6%). The experimental results show that the SNDRLS is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. The proposed method achieves the automated nodule boundary even for low-contrast, blurred, and noisy thyroid ultrasound images without any human intervention. Additionally, the SNDRLS has the ability to determine the controlling parameters adaptively from SNLM clustering.     

Effect of the field of view on perceiving world-referenced image motion during concurrent head movements

We have previously shown that concurrent head movements impair head-referenced image motion perception when compensatory eye movements are suppressed (Li, Adelstein, & Ellis, 2009) [16]. In this paper, we examined the effect of the field of view on perceiving world-referenced image motion during concurrent head movements. Participants rated the motion magnitude of a horizontally oscillating checkerboard image presented on a large screen while making yaw or pitch head movements, or holding their heads still. As the image motion was world-referenced, head motion elicited compensatory eye movements from the vestibular-ocular reflex to maintain the gaze on the display. The checkerboard image had either a large (73°H × 73°V) or a small (25°H × 25°V) field of view (FOV). We found that perceptual sensitivity to world-referenced image motion was reduced by 20% during yaw and pitch head movements compared to the veridical levels when the head was still, and this reduction did not depend on the display FOV size. Reducing the display FOV from 73°H × 73°V to 25°H × 25°V caused an overall underestimation of image motion by 7% across the head movement and head still conditions. We conclude that observers have reduced perceptual sensitivity to world-referenced image motion during concurrent head movements independent of the FOV size. The findings are applicable in the design of virtual environment countermeasures to mitigate perception of spurious motion arising from head tracking system latency.     

Guaranteed-quality all-quadrilateral mesh generation with feature preservation

In this paper, a quadtree-based mesh generation method is described to create guaranteed-quality, geometry-adapted all-quadrilateral (all-quad) meshes with feature preservation for arbitrary planar domains. Given point cloud, our method generates all-quad meshes with these points as vertices and all the angles are within [45°, 135°]. For given planar curves, quadtree-based spatial decomposition is governed by the curvature of the boundaries and narrow regions. 2-refinement templates are chosen for local mesh refinement without creating any hanging nodes. A buffer zone is created by removing elements around the boundary. To guarantee the mesh quality, the angles facing the boundary are improved via template implementation, and two buffer layers are inserted in the buffer zone. It is proved that all the elements of the final mesh are quads with angles between 45° ± ε and 135° ± ε (ε ≤ 5°) with the exception of badly shaped elements that may be required by the sharp angles in the input geometry. We also prove that the scaled Jacobians defined by two edge vectors are in the range of [sin(45° ? ε), sin90°], or [0.64, 1.0]. Furthermore, sharp features and narrow regions are detected and preserved automatically. Boundary layer meshes are generated by splitting elements of the second buffer layer. We have applied our algorithm to a set of complicated geometries, including the Lake Superior map and the air foil with multiple components.     

Thin film temperature sensor for real-time measurement of electrolyte temperature in a polymer electrolyte fuel cell

This paper describes a technique for the measurement of the electrolyte temperature in an operating polymer electrolyte fuel cell (PEFC). A patterned thin film gold thermistor embedded in a 16 μm thick parylene film was laminated in the Nafion^® electrolyte layer for in situ temperature measurements. Experimental results show that the sensor has a linear response of (3.03 ± 0.09) × 10⁻³ °C⁻¹ in the 20–100 °C temperature range and is robust enough to withstand the electrolyte expansion forces that occur during water uptake. An electrolyte temperature increase of 1.5 °C was observed in real-time when operating the fuel cell at 0.2 V and a current density of 0.19 A/cm². The temperature sensitivity of the present sensor is in an order of magnitude better than the conventional micro-thermocouples that have been reported. Additionally, use of micro-fabrication techniques allows for an accurate placement of the temperature sensor within the fuel cell. Simulation results show that the sensor has no significant effect on the local temperature distribution.     

Early-stage atherosclerosis detection using deep learning over carotid ultrasound images

This paper proposes a computer-aided diagnosis tool for the early detection of atherosclerosis. This pathology is responsible for major cardiovascular diseases, which are the main cause of death worldwide. Among preventive measures, the intima-media thickness (IMT) of the common carotid artery stands out as early indicator of atherosclerosis and cardiovascular risk. In particular, IMT is evaluated by means of ultrasound scans. Usually, during the radiological examination, the specialist detects the optimal measurement area, identifies the layers of the arterial wall and manually marks pairs of points on the image to estimate the thickness of the artery. Therefore, this manual procedure entails subjectivity and variability in the IMT evaluation. Instead, this article suggests a fully automatic segmentation technique for ultrasound images of the common carotid artery. The proposed methodology is based on machine learning and artificial neural networks for the recognition of IMT intensity patterns in the images. For this purpose, a deep learning strategy has been developed to obtain abstract and efficient data representations by means of auto-encoders with multiple hidden layers. In particular, the considered deep architecture has been designed under the concept of extreme learning machine (ELM). The correct identification of the arterial layers is achieved in a totally user-independent and repeatable manner, which not only improves the IMT measurement in daily clinical practice but also facilitates the clinical research. A database consisting of 67 ultrasound images has been used in the validation of the suggested system, in which the resulting automatic contours for each image have been compared with the average of four manual segmentations performed by two different observers (ground-truth). Specifically, the IMT measured by the proposed algorithm is 0.625 ± 0.167 mm (mean ± standard deviation), whereas the corresponding ground-truth value is 0.619 ± 0.176 mm. Thus, our method shows a difference between automatic and manual measures of only 5.79 ± 34.42 μm. Furthermore, different quantitative evaluations reported in this paper indicate that this procedure outperforms other methods presented in the literature.     

Performance comparison of artificial neural network and logistic regression model for differentiating lung nodules on CT scans

PurposeTo compare the diagnostic performances of artificial neural networks (ANNs) and multivariable logistic regression (LR) analyses for differentiating between malignant and benign lung nodules on computed tomography (CT) scans.MethodsThis study evaluated 135 malignant nodules and 65 benign nodules. For each nodule, morphologic features (size, margins, contour, internal characteristics) on CT images and the patient’s age, sex and history of bloody sputum were recorded. Based on 200 bootstrap samples generated from the initial dataset, 200 pairs of ANN and LR models were built and tested. The area under the receiver operating characteristic (ROC) curve, Hosmer–Lemeshow statistic and overall accuracy rate were used for the performance comparison.ResultsANNs had a higher discriminative performance than LR models (area under the ROC curve: 0.955 ± 0.015 (mean ± standard error) and 0.929 ± 0.017, respectively, p < 0.05). The overall accuracy rate for ANNs (90.0 ± 2.0%) was greater than that for LR models (86.9 ± 1.6%, p < 0.05). The Hosmer–Lemeshow statistic for the ANNs was 8.76 ± 6.59 vs. 6.62 ± 4.03 (p > 0.05) for the LR models.ConclusionsWhen used to differentiate between malignant and benign lung nodules on CT scans based on both objective and subjective features, ANNs outperformed LR models in both discrimination and clinical usefulness, but did not outperform for the calibration.     

Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system

Recently, significant of the robust texture image classification has increased. The texture image classification is used for many areas such as medicine image processing, radar image processing, etc. In this study, a new method for invariant pixel regions texture image classification is presented. Wavelet packet entropy adaptive network based fuzzy inference system (WPEANFIS) was developed for classification of the twenty 512 × 512 texture images obtained from Brodatz image album. There, sixty 32 × 32 image regions were randomly selected (overlapping or non-overlapping) from each of these 20 images. Thirty of these image regions and other 30 of these image regions are used for training and testing processing of the WPEANFIS, respectively. In this application study, Daubechies, biorthogonal, coiflets, and symlets wavelet families were used for wavelet packet transform part of the WPEANFIS algorithm, respectively. In this way, effects to correct texture classification performance of these wavelet families were compared. Efficiency of WPEANFIS developed method was tested and a mean %93.12 recognition success was obtained.     

Computer-based analysis of explicit approximations to the implicit Colebrook–White equation in turbulent flow friction factor calculation

The implicit Colebrook–White equation has been widely used to estimate the friction factor for turbulent fluid-flow in rough-pipes. In this paper, the state-of-the-art review for the most currently available explicit alternatives to the Colebrook–White equation, is presented. An extensive comparison test was established on the 20 × 500 grid, for a wide range of relative roughness (ε/D) and Reynolds number (R) values (1 × 10^?6 ? ε/D ? 5 × 10^?2; 4 × 10³ ? R ? 10⁸), covering a large portion of turbulent flow zone in Moody’s diagram. Based on the comprehensive error analysis, the magnitude points in which the maximum absolute and the maximum relative error are occurred at the pair of ε/D and R values, are observed. A limiting case of the most of these approximations provided friction factor estimates that are characterized by a mean absolute error of 5 × 10^?4, a maximum absolute error of 4 × 10^?3 whereas, a mean relative error of 1.3% and a maximum relative error of 5.8%, over the entire range of ε/D and R values, respectively. For practical purposes, the complete results for the maximum and the mean relative errors versus the 20 sets of ε/D value, are also indicated in two comparative figures. The examination results for error properties of these approximations gives one an opportunity to practically evaluate the most accurate formula among of all the previous explicit models; and showing in this way its great flexibility for estimating turbulent flow friction factor. Comparative analysis for the mean relative error profile revealed, the classification for the best-fitted six equations examined was in a good agreement with those of the best model selection criterion claimed in the recent literature, for all performed simulations.     

Development of a novel micromirror based on surface micromaching technology

A novel 3 × 3 micromirror array is designed and successfully fabricated with multi-layer silicon surface micromaching technology. It is composed of bottom electrode, support part and mirror plate, in which a T type beam structure is used to support the mirror plate. It can provide mirror with the vertical movement and the rotation about two horizontal axes, thus enabling phase modulation and amplitude modulation for the incident light. The test results show that the maximum deflection length along the vertical direction of the mirror plate is 2 μm, while the rotation angle about X- and Y-axis are ±2.3° and ±1.45°, respectively.     

A distributed optical fiber sensor for temperature detection in power cables

The power transfer capacity of an underground power cable is limited by high-temperature regions that occur along the cable. It is very difficult to determine and control these ‘hot spots’. Optimum use and temperature profile control of power cables before and during load transmission can be achieved with real-time processing of temperature data. There are various methods developed for this purpose such as conventional point temperature measurement method, where a large number of sensors and connectors are required, and methods based on mathematical models which can only approach real values by approximation.In this study, temperature detection in an XLPE insulated 154 kV power cable is performed using a distributed sensing method where the optical fiber itself behaves as a sensor. Therefore, there is no need for the devices of conventional method. Moreover, contrary to methods based on mathematical models, where it is difficult to predict environmental variations, this method considers the variations with a temperature resolution of ±1 °C. Distributed temperature sensing (DTS) method, detection system configuration and required system parameters are explained in the paper. Experimental results obtained for 126 and 412 m cables show a temperature resolution of ±1 °C and a spatial resolution of 1.22 m. Simulations for a 10 km cable are also given. Results show that DTS is a reliable method for both short and long range cable systems.     

Hexagon-based all-quadrilateral mesh generation with guaranteed angle bounds

In this paper, we present a novel hexagon-based mesh generation method which creates all-quadrilateral (all-quad) meshes with guaranteed angle bounds and feature preservation for arbitrary planar domains. Given any planar curves, an adaptive hexagon-tree structure is constructed by using the curvature of the boundaries and narrow regions. Then a buffer zone and a hexagonal core mesh are created by removing elements outside or around the boundary. To guarantee the mesh quality, boundary edges of the core mesh are adjusted to improve their formed angles facing the boundary, and two layers of quad elements are inserted in the buffer zone. For any curve with sharp features, a corresponding smooth curve is firstly constructed and meshed, and then another layer of elements is inserted to match the smooth curve with the original one. It is proved that for any planar smooth curve all the element angles are within [60° ? ε, 120° + ε] (ε ? 5°). We also prove that the scaled Jacobians defined by two edge vectors are in the range of [sin (60° ? ε),  sin 90°], or [0.82, 1.0]. The same angle range can be guaranteed for curves with sharp features, with the exception of small angles in the input curve. Furthermore, an approach is introduced to match the generated interior and exterior meshes with a relaxed angle range, [30°, 150°]. We have applied our algorithm to a set of complicated geometries, including the China map, the Lake Superior map, and a three-component air foil with sharp features. In addition, all the elements in the final mesh are grouped into five types, and most elements only need a few flops to construct the stiffness matrix for finite element analysis. This will significantly reduce the computational time and the required memory during the stiffness matrix construction.     

Electrical properties of nickel oxide thin films for flow sensor application

In this work, Ni oxide thin films, with thermal sensitivity superior to Pt and Ni thin films, were formed through annealing of Ni films deposited by a r.f. magnetron sputtering. The annealing was carried out in the temperature range of 300–500 °C under atmospheric conditions. Resistivity of the resulting Ni oxide films were in the range of 10.5 μΩ cm/°C to 2.84 × 10⁴ μΩ cm/°C, depending on the extent of Ni oxidation. The temperature coefficient of resistance (TCR) of the Ni oxide films also depended on the extent of Ni oxidation; the average TCR of Ni oxide resistors, measured between 0 and 150 °C, were 5630 ppm/°C for the 300 °C and 2188 ppm/°C for 500 °C films. Because of their high resistivity and very linear TCR, Ni oxide thin films are superior to pure Ni and Pt thin films for flow and temperature sensor applications.     

Calorimetric measurement of the standard enthalpy of formation of ZnSb at 298 K

After a critical review of the literature data on the standard enthalpy of formation of ZnSb, discrepancies between various experimental studies are highlighted. Moreover, experimental values disagree with values calculated by ab initio methods. As many of the experimental methods used suffer from some serious drawbacks, a new determination of the standard enthalpy of formation of ZnSb by an alternative calorimetric method, drop solution calorimetry in liquid tin, has been performed. Two different synthesis methods have been used to obtain a pure ZnSb phase and drop solution experiments were performed at 665 and at 974 K. The standard enthalpy of formation values derived from these experiments are − 6.1±2.5 kJ mol of atoms⁻¹ for the ZnSb sample prepared by ball milling and − 7.9±0.4 kJ mol of atoms⁻¹ for the ZnSb sample prepared by melting. These results are discussed and compared to literature data.     

Action recognition via bio-inspired features: The richness of center–surround interaction

Motion is a key feature for a wide class of computer vision approaches to recognize actions. In this article, we show how to define bio-inspired features for action recognition. To do so, we start from a well-established bio-inspired motion model of cortical areas V1 and MT. The primary visual cortex, designated as V1, is the first cortical area encountered in the visual stream processing and early responses of V1 cells consist in tiled sets of selective spatiotemporal filters. The second cortical area of interest in this article is area MT where MT cells pool incoming information from V1 according to the shape and characteristic of their receptive field. To go beyond the classical models and following the observations from Xiao et al. [61], we propose here to model different surround geometries for MT cells receptive fields. Then, we define the so-called bio-inspired features associated to an input video, based on the average activity of MT cells. Finally, we show how these features can be used in a standard classification method to perform action recognition. Results are given for the Weizmann and KTH databases. Interestingly, we show that the diversity of motion representation at the MT level (different surround geometries), is a major advantage for action recognition. On the Weizmann database, the inclusion of different MT surround geometries improved the recognition rate from 63.01 ± 2.07% up to 99.26 ± 1.66% in the best case. Similarly, on the KTH database, the recognition rate was significantly improved with the inclusion of MT different surround geometries (from 47.82 ± 2.71% up to 92.44 ± 0.01% in the best case). We also discussed the limitations of the current approach which are closely related to the input video duration. These promising results encourage us to further develop bio-inspired models incorporating other brain mechanisms and cortical areas in order to deal with more complex videos.     

Investigation of the phase equilibria in Ti-Ni-Hf system using diffusion triples and equilibrated alloys

In present work, the phase equilibrium relations in the Ti-Ni-Hf ternary system, which are of great importance for the design of Ti-Ni based high temperature shape memory alloys, were investigated using diffusion triples and sixteen key equilibrated alloys. Based on the experimental results from electron-probe microscopy analysis (EPMA) and X-ray diffraction (XRD) techniques, two isothermal sections were constructed, which consist of 13 and 12 three-phase regions at 900 °C and 800 °C, respectively. Hf can substitute for Ti in TiNi and Ti₂Ni phases increasing from 30, 62 at% at 800 °C to 36, 64 at% at 900 °C, respectively. The Hf₇Ni₁₀ and Hf₉Ni₁₁ phases show wide ternary composition ranges, while the solubility of Ti in HfNi₅, Hf₂Ni₇, and HfNi phases are relatively limited. A new ternary phase of τ was detected for the first time, and the stoichiometry of τ phase is close to Ni:(Hf,Ti) = 11:14, with Ti substituting for Hf from ～5 at% to ～22 at%. The single-phase region of the τ phase became narrow as the decreasing of annealing temperature. Based on comparison of phase relations at 900 °C and 800 °C, it is speculated there is an invariant reaction TiNi + τ → HfNi + Ti₂Ni at between 900 °C and 800 °C.     

Guided local search as a network planning algorithm that incorporates uncertain traffic demands

A search based heuristic for the optimisation of communication networks where traffic forecasts are uncertain and the problem is NP-complete is presented. While algorithms such as genetic algorithms (GA) and simulated annealing (SA) are often used for this class of problem, this work applies a combination of newer optimisation techniques specifically: fast local search (FLS) as an improved hill climbing method and guided local search (GLS) to allow escape from local minima. The GLS + FLS combination is compared with an optimised GA and SA approaches. It is found that in terms of implementation, the parameterisation of the GLS + FLS technique is significantly simpler than that for a GA and SA. Also, the self-regularisation feature of the GLS + FLS approach provides a distinctive advantage over the other techniques which require manual parameterisation. To compare numerical performance, the three techniques were tested over a number of network sets varying in size, number of switch circuit demands (network bandwidth demands) and levels of uncertainties on the switch circuit demands. The results show that the GLS + FLS outperforms the GA and SA techniques in terms of both solution quality and optimisation speed but even more importantly GLS + FLS has significantly reduced parameterisation time.     

Compound Linguistic Scale

Rating scales are the essential interfaces for many research areas such as in decision making and recommendation. Some issues concerning syntactic and sematic structures are still open to discuss. This research proposes a Compound Linguistic Scale (CLS), a two dimension rating scale, as a promising rating interface. The CLS is comprised of Compound Linguistic Variable (CLV) and Deductive Rating Strategy (DRS). CLV can ideally produce 21 to 73 ((7 ± 2)((7 ± 2) − 1) + 1) ordinal-in-ordinal rating items, which extends the classic rating scales usually on the basis of 7 ± 2 principle, to better reflect the raters’ preferences whilst DRS is a double step rating approach for a rater to choose a compound linguistic term among two dimensional options on a dynamic rating interface. The numerical analyses show that the proposed CLS can address rating dilemma for a single rater and more accurately reflects consistency among various raters. CLS can be applied to surveys, questionnaires, psychometrics, recommender systems and decision analysis of various application domains.     

Fuzzy logic and statistical-based modelling of the Marshall Stability of asphalt concrete under varying temperatures and exposure times

In this study, the Marshall Stability (MS) of asphalt concrete under varying temperature and exposure times was modelled by using fuzzy logic and statistical method. This is an experimental study conducted using statistics and fuzzy logic methods. In order to investigate the Marshall Stability of asphalt concrete based on exposure time and environment temperature, exposure times of 1.5, 3, 4.5 and 6 h and temperatures of 30, 40 and 50 °C were selected. The MS of the asphalt concrete at 17 °C (in laboratory environment temperature) was used as reference. The results showed that the MS of the asphalt core samples decreased 40.16% at 30 °C after 1.5 h and 62.39% after 6 h. At 40 °C the decrease was 74.31% after 1.5 h, and 78.10% after 6 h. At 50 °C the stability of the asphalt decreased 83.22% after 1.5 h, 88.66% after 6 h. The relationships between experimental results, fuzzy logic model and statistical results exhibited good correlation. The correlation coefficient was R = 0.99 for fuzzy logic model and R² = 0.9 for statistical method. Based on the results of the study, it could be said that both the fuzzy logic method and statistical methods could be used for modelling of the stability of asphalt concrete under varying temperature and exposure time.     

Bayesian modelling of algal mass occurrences—using adaptive MCMC methods with a lake water quality model

Our study aims to estimate confounded effects of nutrients and grazing zooplankton (Crustacea) on phytoplankton groups—specifically on nitrogen-fixing Cyanobacteria—in the shallow, mesotrophic Lake Pyhäjärvi in the northern hemisphere (Finland, northern Europe, lat. 60°54′–61°06′, long. 22°09′–22°22′). Phytoplankton is modelled with a non-linear dynamic model which describes the succession of three dominant algae groups (Diatomophyceae, Chrysophyceae, nitrogen-fixing Cyanobacteria) and minor groups summed together as a function of total phosphorus, total nitrogen, temperature, global irradiance and crustacean zooplankton grazing. The model is fitted using 8 years of in situ observations and adaptive Markov chain Monte Carlo (MCMC) methods for estimation of model parameters. The approach offers a way to deal with noisy data and a large number of weakly identifiable parameters in a model. From our posterior simulations we calculate the lower limit for zooplankton carbon mass concentration (45 μgC L⁻¹) and the upper limit for total phosphorus concentration (16 μg L⁻¹) that satisfy with 0.95 probability our predefined water quality criteria (Cyanobacteria concentration during late summer period does not exceed the value 0.86 mg L⁻¹). Within the observational range total phosphorus has marginal effect on Cyanobacteria compared to the zooplankton grazing effect, which is temperature-dependent. Extensive fishing efforts are needed to attain the criteria.