Automated Analysis Techniques Research Articles

Automatic Analysis of Microaneurysms Turnover to Diagnose the Progression of Diabetic Retinopathy

Diabetic retinopathy (DR) is one of the most common microvascular complications and its early detection is critical for the prevention of vision loss. Recent studies have indicated that microaneurysms (MAs) are the hallmark of DR. However, the detection of MAs relies on trained clinicians and relatively expensive software. Moreover, manual errors often lower the accuracy of this detection. Therefore, an automatic analysis technique is highly demanded in the detection of DR progression. In this paper, we present a novel and complete methodology involving two different ways from the view of MAs turnover and pathological risk factors to diagnose the progression of DR. Specifically, one approach follows the traditional image analysis-based roadmap to obtain MAs turnover. The other investigates seven pathological features, related with MAs turnover, to classify the unchanged, new, and resolved MAs by means of statistical analysis and pattern classification techniques. The evaluations on Grampian diabetes database show that the proposed image analysis method could achieve a sensitivity of 94% and a specificity of 93%, while the classification model could achieve 89% sensitivity and 88% specificity, respectively. We also analyzed the potential weight of pathological risk factors leading to the MAs turnover, which could provide an alternative guidance for the progression of DR than traditional detection methods. In conclusion, this study provides a novel and noninvasive detection technique for early diagnosis of diabetic retinopathy with a competitive accuracy.

A Hybrid Approach to Visual Vessel Extraction with Segmentation

Vessel extraction for retinal images play crucial role in medical field for proper diagnosis and treatment of various diseases like diabetic retinopathy, hypertensive retinopathy and other cardio vascular diseases. In this paper we propose a novel technique for automatic analysis of fundus retinal image using improved vessel of infinite perimeter active contour model. This approach uses intensity information and local phase based enhancement filter technique and Cauchy based matched filter to provide better accuracy. The performance evolution is carried on DRIVE data set and on real images too. The result shows good performance than previous IPACHI method.

Using Text Mining to Compare Online Pro- and Anti-Vaccine Headlines: Word Usage, Sentiments, and Online Popularity

This study aims to explore differences between health misinformation and true information by comparing word usage, sentiments, and online popularity between pro- and anti-vaccine headlines (PVHs and AVHs). Text mining and sentiment analysis showed that AVHs were more likely to use negative sentiment words and trust-related words. PVHs were more likely to use words related to positive sentiments. Anti-vaccine messages (AVMs) were more popular online than pro-vaccine messages (PVMs). AVMs’ online popularity was not related to its emotion words usage. Among PVMs, those with more positive sentiment words were more likely to be shared, commented on, and reacted to online. Wordclouds and word networks were created to visualize the word usage and clustering. Future directions regarding message design and automatic detection and analysis techniques are provided.

Upregulation of angiogenesis in oral lichen planus.

As angiogenesis is fundamental to the pathogenesis of many chronic inflammatory disorders, this study investigated the expression of various vascular markers in oral lichen planus and non-specific oral mucosal inflammatory tissues. Archival specimens of oral lichen planus (n=15) and inflamed tissues (n=13) were stained using immunohistochemistry with antibodies to CD34, vascular endothelial growth factor, vascular endothelial growth factor receptor and vasohibin. Nine representative sites at the epithelial-connective tissue junction and through the fibrous connective tissue were selected, and automated analysis techniques were used to determine the extent of positivity expressed as the percentage of positive cells. Significance was denoted when P<.05. The expression of pro-angiogenic factors was higher in lichen planus samples compared with inflamed controls. A higher level of CD34 was observed in the deeper parts of the connective tissue of Oral lichen planus (OLP) (P=.04), whereas VEGF and VEGFR2 expressions were higher all through the tissues (respectively, P<.02 and P<.01). The expression of the anti-angiogenic VASH1 was higher in inflamed tissue compared with lichen planus in all sites evaluated (P<.01). The findings indicate that angiogenic factors are differentially expressed in oral lichen planus compared with inflamed controls, with increased expression of pro-angiogenic factors and decreased anti-angiogenic expression.

Magnetic resonance elastography measured shear stiffness as a biomarker of fibrosis in pediatric nonalcoholic fatty liver disease.

2D MRE can estimate hepatic stiffness in children with NAFLD. Further refinement and validation of automated analysis techniques will be an important step in standardizing MRE. How to best integrate MRE into clinical protocols for the assessment of NAFLD in children will require prospective evaluation. (Hepatology 2017;66:1474-1485).

Sampling and Estimation of Pairwise Similarity in Spatio-Temporal Data Based on Neural Networks

Increasingly fast computing systems for simulations and high-accuracy measurement techniques drive the generation of time-dependent volumetric data sets with high resolution in both time and space. To gain insights from this spatio-temporal data, the computation and direct visualization of pairwise distances between time steps not only supports interactive user exploration, but also drives automatic analysis techniques like the generation of a meaningful static overview visualization, the identification of rare events, or the visual analysis of recurrent processes. However, the computation of pairwise differences between all time steps is prohibitively expensive for large-scale data not only due to the significant cost of computing expressive distance between high-resolution spatial data, but in particular owing to the large number of distance computations ( O ( | T | 2 ) ) , with | T | being the number of time steps). Addressing this issue, we present and evaluate different strategies for the progressive computation of similarity information in a time series, as well as an approach for estimating distance information that has not been determined so far. In particular, we investigate and analyze the utility of using neural networks for estimating pairwise distances. On this basis, our approach automatically determines the sampling strategy yielding the best result in combination with trained networks for estimation. We evaluate our approach with a variety of time-dependent 2D and 3D data from simulations and measurements as well as artificially generated data, and compare it against an alternative technique. Finally, we discuss prospects and limitations, and discuss different directions for improvement in future work.

Automatic detection of hemorrhagic pericardial effusion on PMCT using deep learning - a feasibility study.

Post mortem computed tomography (PMCT) can be used as a triage tool to better identify cases with a possibly non-natural cause of death, especially when high caseloads make it impossible to perform autopsies on all cases. Substantial data can be generated by modern medical scanners, especially in a forensic setting where the entire body is documented at high resolution. A solution for the resulting issues could be the use of deep learning techniques for automatic analysis of radiological images. In this article, we wanted to test the feasibility of such methods for forensic imaging by hypothesizing that deep learning methods can detect and segment a hemopericardium in PMCT. For deep learning image analysis software, we used the ViDi Suite 2.0. We retrospectively selected 28 cases with, and 24 cases without, hemopericardium. Based on these data, we trained two separate deep learning networks. The first one classified images into hemopericardium/not hemopericardium, and the second one segmented the blood content. We randomly selected 50% of the data for training and 50% for validation. This process was repeated 20 times. The best performing classification network classified all cases of hemopericardium from the validation images correctly with only a few false positives. The best performing segmentation network would tend to underestimate the amount of blood in the pericardium, which is the case for most networks. This is the first study that shows that deep learning has potential for automated image analysis of radiological images in forensic medicine.

Robust normal estimation and region growing segmentation of infrastructure 3D point cloud models

Modern remote sensing technologies such as three-dimensional (3D) laser scanners and image-based 3D scene reconstruction are in increasing demand for applications in civil infrastructure design, maintenance, operation, and as-built construction verification. The complex nature of the 3D point clouds these technologies generate, as well as the often massive scale of the 3D data, make it inefficient and time consuming to manually analyze and manipulate point clouds, and highlights the need for automated analysis techniques. This paper presents one such technique, a new region growing algorithm for the automated segmentation of both planar and non-planar surfaces in point clouds. A core component of the algorithm is a new point normal estimation method, an essential task for many point cloud processing algorithms. The newly developed estimation method utilizes robust multivariate statistical outlier analysis for reliable normal estimation in complex 3D models, considering that these models often contain regions of varying surface roughness, a mixture of high curvature and low curvature regions, and sharp features. An adaptation of Mahalanobis distance, in which the mean vector and covariance matrix are derived from a high-breakdown multivariate location and scale estimator called Deterministic MM-estimator (DetMM) is used to find and discard outlier points prior to estimating the best local tangent plane around any point in a cloud. This approach is capable of more accurately estimating point normals located in highly curved regions or near sharp features. Thereafter, the estimated point normals serve a region growing segmentation algorithm that only requires a single input parameter, an improvement over existing methods which typically require two control parameters. The reliability and robustness of the normal estimation subroutine was compared against well-known normal estimation methods including the Minimum Volume Ellipsoid (MVE) and Minimum Covariance Determinant (MCD) estimators, along with Maximum Likelihood Sample Consensus (MLESAC). The overall region growing segmentation algorithm was then experimentally validated on several challenging 3D point clouds of real-world infrastructure systems. The results indicate that the developed approach performs more accurately and robustly in comparison with conventional region growing methods, particularly in the presence of sharp features, outliers and noise.

Advanced Data Exploitation in Speech Analysis: An overview

With recent advances in machine-learning techniques for automatic speech analysis (ASA)-the computerized extraction of information from speech signals-there is a greater need for high-quality, diverse, and very large amounts of data. Such data could be game-changing in terms of ASA system accuracy and robustness, enabling the extraction of feature representations or the learning of model parameters immune to confounding factors, such as acoustic variations, unrelated to the task at hand. However, many current ASA data sets do not meet the desired properties. Instead, they are often recorded under less than ideal conditions, with the corresponding labels sparse or unreliable.

Long-Term Monitoring of Dolphin Biosonar Activity in Deep Pelagic Waters of the Mediterranean Sea

Dolphins emit short ultrasonic pulses (clicks) to acquire information about the surrounding environment, prey and habitat features. We investigated Delphinidae activity over multiple temporal scales through the detection of their echolocation clicks, using long-term Passive Acoustic Monitoring (PAM). The Istituto Nazionale di Fisica Nucleare operates multidisciplinary seafloor observatories in a deep area of the Central Mediterranean Sea. The Ocean noise Detection Experiment collected data offshore the Gulf of Catania from January 2005 to November 2006, allowing the study of temporal patterns of dolphin activity in this deep pelagic zone for the first time. Nearly 5,500 five-minute recordings acquired over two years were examined using spectrogram analysis and through development and testing of an automatic detection algorithm. Echolocation activity of dolphins was mostly confined to nighttime and crepuscular hours, in contrast with communicative signals (whistles). Seasonal variation, with a peak number of clicks in August, was also evident, but no effect of lunar cycle was observed. Temporal trends in echolocation corresponded to environmental and trophic variability known in the deep pelagic waters of the Ionian Sea. Long-term PAM and the continued development of automatic analysis techniques are essential to advancing the study of pelagic marine mammal distribution and behaviour patterns.

A systematic comparison of cardiovascular magnetic resonance and high resolution histological fibrosis quantification in a chronic porcine infarct model

The noninvasive reference standard for myocardial fibrosis detection on cardiovascular magnetic resonance imaging (CMR) is late gadolinium enhancement (LGE). Currently there is no consensus on the preferred method for LGE quantification. Moreover myocardial wall thickening (WT) and strain are measures of regional deformation and function. The aim of this research was to systematically compare in vivo CMR parameters, such as LGE, WT and strain, with histological fibrosis quantification. Eight weeks after 90 min ischemia/reperfusion of the LAD artery, 16 pigs underwent in vivo Cine and LGE CMR. Histological sections from transverse heart slices were digitally analysed for fibrosis quantification. Mean fibrosis percentage of analysed sections was related to the different CMR techniques (using segmentation or feature tracking software) for each slice using a linear mixed model analysis. The full width at half maximum (FWHM) technique for quantification of LGE yielded the highest R2 of 60%. Cine derived myocardial WT explained 16–36% of the histological myocardial fibrosis. The peak circumferential and radial strain measured by feature tracking could explain 15 and 10% of the variance of myocardial fibrosis, respectively. The used method to systematically compare CMR image data with digital histological images is novel and feasible. Myocardial WT and strain were only modestly related with the amount of fibrosis. The fully automatic FWHM analysis technique is the preferred method to detect myocardial fibrosis.

A Fully Automated High-Throughput Zebrafish Behavioral Ototoxicity Assay.

Zebrafish animal models lend themselves to behavioral assays that can facilitate rapid screening of ototoxic, otoprotective, and otoregenerative drugs. Structurally similar to human inner ear hair cells, the mechanosensory hair cells on their lateral line allow the zebrafish to sense water flow and orient head-to-current in a behavior called rheotaxis. This rheotaxis behavior deteriorates in a dose-dependent manner with increased exposure to the ototoxin cisplatin, thereby establishing itself as an excellent biomarker for anatomic damage to lateral line hair cells. Building on work by our group and others, we have built a new, fully automated high-throughput behavioral assay system that uses automated image analysis techniques to quantify rheotaxis behavior. This novel system consists of a custom-designed swimming apparatus and imaging system consisting of network-controlled Raspberry Pi microcomputers capturing infrared video. Automated analysis techniques detect individual zebrafish, compute their orientation, and quantify the rheotaxis behavior of a zebrafish test population, producing a powerful, high-throughput behavioral assay. Using our fully automated biological assay to test a standardized ototoxic dose of cisplatin against varying doses of compounds that protect or regenerate hair cells may facilitate rapid translation of candidate drugs into preclinical mammalian models of hearing loss.

Optimized combinatorial clustering for stochastic processes

As a new data processing era like Big Data, Cloud Computing, and Internet of Things approaches, the amount of data being collected in databases far exceeds the ability to reduce and analyze these data without the use of automated analysis techniques, data mining. As the importance of data mining has grown, one of the critical issues to emerge is how to scale data mining techniques to larger and complex databases so that it is particularly imperative for computationally intensive data mining tasks such as identifying natural clusters of instances. In this paper, we suggest an optimized combinatorial clustering algorithm for noisy performance which is essential for large data with random sampling. The algorithm outperforms conventional approaches through various numerical and qualitative thresholds like mean and standard deviation of accuracy and computation speed.

Asians' Facial Responsiveness to Basic Tastes by Automated Facial Expression Analysis System.

Growing evidence shows that consumer choices in real life are mostly driven by unconscious mechanisms rather than conscious. The unconscious process could be measured by behavioral measurements. This study aims to apply automatic facial expression analysis technique for consumers' emotion representation, and explore the relationships between sensory perception and facial responses. Basic taste solutions (sourness, sweetness, bitterness, umami, and saltiness) with 6 levels plus water were used, which could cover most of the tastes found in food and drink. The other contribution of this study is to analyze the characteristics of facial expressions and correlation between facial expressions and perceptive hedonic liking for Asian consumers. Up until now, the facial expression application researches only reported for western consumers, while few related researches investigated the facial responses during food consuming for Asian consumers. Experimental results indicated that facial expressions could identify different stimuli with various concentrations and different hedonic levels. The perceived liking increased at lower concentrations and decreased at higher concentrations, while samples with medium concentrations were perceived as the most pleasant except sweetness and bitterness. High correlations were founded between perceived intensities of bitterness, umami, saltiness, and facial reactions of disgust and fear. Facial expression disgust and anger could characterize emotion "dislike," and happiness could characterize emotion "like," while neutral could represent "neither like nor dislike." The identified facial expressions agree with the perceived sensory emotions elicited by basic taste solutions. The correlation analysis between hedonic levels and facial expression intensities obtained in this study are in accordance with that discussed for western consumers.

Generation of phase edge singularities by coplanar three-beam interference and their detection.

In recent years singular optics has gained considerable attention in science and technology. Up to now optical vortices (phase point dislocations) have been of main interest. This paper presents the first general analysis of formation of phase edge singularities by coplanar three-beam interference. They can be generated, for example, by three-slit interference or self-imaging in the Fresnel diffraction field of a sinusoidal grating. We derive a general condition for the ratio of amplitudes of interfering beams resulting in phase edge dislocations, lateral separation of dislocations depends on this ratio as well. Analytically derived properties are corroborated by numerical and experimental studies. We develop a simple, robust, common path optical self-imaging configuration aided by a coherent tilted reference wave and spatial filtering. Finally, we propose an automatic fringe pattern analysis technique for detecting phase edge dislocations, based on the continuous wavelet transform. Presented studies open new possibilities for developing grating based sensing techniques for precision metrology of very small phase differences.

Contrast enhancement for cephalometric images using wavelet-based modified adaptive histogram equalization

Cephalometric images usually have low contrast. The existing techniques for automatic cephalometric analysis usually use histogram equalization for image enhancement. This technique has the advantage of being fully automatic and nonlinear. However, it suffers from spikes, excessive enhancement, and lack of brightness preservation. The proposed technique is an adaptive histogram equalization technique that uses wavelet based gradient histograms. This paper compares its performance with two traditional techniques, three histogram modification based techniques, and two wavelet based techniques. Forty digital and scanned cephalograms are used to conduct tests. In addition to visual histograms and intensity profiles, the proposed method is compared in terms of eight quantitative measures. The various measures are applied to analyze the results in terms of contrast enhancement (EME, CNR), brightness preservation (AMBE), edge conservation and enhancement (H, TEN), preservation of image structures and non-addition distortion (MSSIM, SVD-M). The proposed method gives good contrast enhancement, with better brightness preservation without losing edge information and with the minimum addition of distortions to the enhanced cephalometric images.

Analyzing execution traces: critical-path analysis and distance analysis

System designers make trade-offs between metrics of interest such as execution time, functional quality and cost to create a properly balanced system. Execution traces, which are sequences of timestamped start and end events of system tasks, are a general and powerful means to understand the system behavior that gives rise to these trade-offs. Such traces can be produced by, e.g., executable models or prototype systems. Their interpretation, however, often is non-trivial. We present two automated analysis techniques that work on execution traces to help the system designer with interpretation. First, critical-path analysis can be used to answer the typical “what is the bottleneck” question, and we extend earlier work of [16] with a technique that uses application information to refine the analysis. Second, we define a pseudo-metric on execution traces, which is useful for calibration and validation purposes, and which can be used to visualize the differences between traces. Both techniques are based on a common graph representation of execution traces. We have implemented our techniques in the Trace visualization tool [12], and have applied them in a case study from the digital printing domain.

Model Checking Software with First Order Logic Specifications Using AIG Solvers

Static verification techniques leverage Boolean formula satisfiability solvers such as SAT and SMT solvers that operate on conjunctive normal form and first order logic formulae, respectively, to validate programs. They force bounds on variable ranges and execution time and translate the program and its specifications into a Boolean formula. They are limited to programs of relatively low complexity for the following reasons. (1) A small increase in the bounds can cause a large increase in the size of the translated formula. (2) Boolean satisfiability solvers are restricted to using optimizations that apply at the level of the formula. Finally, (3) the Boolean formulae often need to be regenerated with higher bounds to ensure the correctness of the translation. We present a method that uses And-Inverter-Graph (AIG) sequential circuits, and AIG synthesis and verification frameworks to validate programs. An AIG is a Boolean formula with memory elements, logically complete negated conjunction gates, and a hierarchical structure. Encoding the validation problem of a program as an AIG (1) typically provides a more succinct representation than a Boolean formulae encoding with no memory elements, (2) preserves the high-level structure of the program, and (3) enables the use of a number of powerful automated analysis techniques that have no counterparts for other Boolean formulae such as CNF. Our method takes an imperative program with a first order logic specification consisting of a precondition and a postcondition pair, and a bound on the program variable ranges, and produces an AIG with a designated output that is ${true}$ when the program violates the specification. Our method uses AIG synthesis reduction techniques to reduce the AIG, and then uses AIG verification techniques to check the satisfiability of the designated output. The results show that our method can validate designs that are not possible with other state of the art techniques, and with bounds that are an order of magnitude larger.

Dialogue as Data in Learning Analytics for Productive Educational Dialogue

Accounts of the nature and role of productive dialogue in fostering educational outcomes are now well established in the learning sciences and are underpinned by bodies of strong empirical research and theorising. Allied to this there has been longstanding interest in fostering computer-supported collaborative learning (CSCL) in support of such dialogue. Learning analytic environments such as massive open online courses (moocs) and online learning environments (such as virtual learning environments, VLEs and learning management systems, LMSs) provide ripe potential spaces for learning dialogue. In prior research, preliminary steps have been taken to detect occurrences of productive dialogue automatically through the use of automated analysis techniques. Such advances have the potential to foster effective dialogue through the use of learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting, such dialogue. However, the translation of learning science research to the online context is complex, requiring the operationalization of constructs theorized in different contexts (often face to face), and based on different data-sets and structures (often spoken dialogue).. In this paper we explore what could constitute the effective analysis of this kind of productive dialogue, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. We begin by outlining what we mean by ‘productive educational dialogue’, before going on to discuss prior work that has been undertaken to date on its manual and automated analysis. We then highlight ongoing challenges for the development of computational analytic approaches to such data, discussing the representation of features, segments, and temporality in computational modelling. The paper thus foregrounds, to both learning-science-oriented and computationally-oriented researchers, key considerations in respect of the analysis dialogue data in emerging learning analytics environments. The paper provides a novel, conceptually driven, stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on and offline sites of learning.

Research on Methods for Discovering and Selecting Cloud Infrastructure Services Based on Feature Modeling

Nowadays more and more cloud infrastructure service providers are providing large numbers of service instances which are a combination of diversified resources, such as computing, storage, and network. However, for cloud infrastructure services, the lack of a description standard and the inadequate research of systematic discovery and selection methods have exposed difficulties in discovering and choosing services for users. First, considering the highly configurable properties of a cloud infrastructure service, the feature model method is used to describe such a service. Second, based on the description of the cloud infrastructure service, a systematic discovery and selection method for cloud infrastructure services are proposed. The automatic analysis techniques of the feature model are introduced to verify the model’s validity and to perform the matching of the service and demand models. Finally, we determine the critical decision metrics and their corresponding measurement methods for cloud infrastructure services, where the subjective and objective weighting results are combined to determine the weights of the decision metrics. The best matching instances from various providers are then ranked by their comprehensive evaluations. Experimental results show that the proposed methods can effectively improve the accuracy and efficiency of cloud infrastructure service discovery and selection.