Analysis of combined approaches of CBIR systems by clustering at varying precision levels

<span lang="EN-US">The image retrieving system is used to retrieve images from the image database. Two types of Image retrieval techniques are commonly used: content-based and text-based techniques. One of the well-known image retrieval techniques that extract the images in an unsupervised way, known as the cluster-based image retrieval technique. In this cluster-based image retrieval, all visual features of an image are combined to find a better retrieval rate and precisions. The objectives of the study were to develop a new model by combining the three traits i.e., color, shape, and texture of an image. The color-shape and color-texture models were compared to a threshold value with various precision levels. A union was formed of a newly developed model with a color-shape, and color-texture model to find the retrieval rate in terms of precisions of the image retrieval system. The results were experimented on on the COREL standard database and it was found that the union of three models gives better results than the image retrieval from the individual models. The newly developed model and the union of the given models also gives better results than the existing system named cluster-based retrieval of images by unsupervised learning (CLUE).</span>

Radiomics for the Prediction of Epilepsy in Patients With Frontal Glioma

ObjectiveThis study was conducted in order to investigate the association between radiomics features and frontal glioma-associated epilepsy (GAE) and propose a reliable radiomics-based model to predict frontal GAE.MethodsThis retrospective study consecutively enrolled 166 adult patients with frontal glioma (111 in the training cohort and 55 in the testing cohort). A total 1,130 features were extracted from T2 fluid-attenuated inversion recovery images, including first-order statistics, 3D shape, texture, and wavelet features. Regions of interest, including the entire tumor and peritumoral edema, were drawn manually. Pearson correlation coefficient, 10-fold cross-validation, area under curve (AUC) analysis, and support vector machine were adopted to select the most relevant features to build a clinical model, a radiomics model, and a clinical–radiomics model for GAE. The receiver operating characteristic curve (ROC) and AUC were used to evaluate the classification performance of the models in each cohort, and DeLong’s test was used to compare the performance of the models. A two-sided t-test and Fisher’s exact test were used to compare the clinical variables. Statistical analysis was performed using SPSS software (version 22.0; IBM, Armonk, New York), and p &lt;0.05 was set as the threshold for significance.ResultsThe classification accuracy of seven scout models, except the wavelet first-order model (0.793) and the wavelet texture model (0.784), was &lt;0.75 in cross-validation. The clinical–radiomics model, including 17 magnetic resonance imaging-based features selected among the 1,130 radiomics features and two clinical features (patient age and tumor grade), achieved better discriminative performance for GAE prediction in both the training [AUC = 0.886, 95% confidence interval (CI) = 0.819–0.940] and testing cohorts (AUC = 0.836, 95% CI = 0.707–0.937) than the radiomics model (p = 0.008) with 82.0% and 78.2% accuracy, respectively.ConclusionRadiomics analysis can non-invasively predict GAE, thus allowing adequate treatment of frontal glioma. The clinical–radiomics model may enable a more precise prediction of frontal GAE. Furthermore, age and pathology grade are important risk factors for GAE.

Robust Screening Criteria for Foam-Assisted Water-Alternating Gas FAWAG Injection

Foam-Assisted-Water-Alternating-Gas (FAWAG) injection has been proposed to improve the inherent unfavorable mobility ratio of gas and liquid in WAG process. The foam reduces gravity override and gas channeling as to improve volumetric sweep efficiency and thus oil recovery. There are still a lot of uncertainties yet to be understood in foam dynamics, surfactant adsorption, and foam stability when contacting oil, which impact the actual foam propagation into the reservoir. Although some insights are gained from laboratory and field experiments, the performance, and design of the injection strategy and facilities as part of the field development of FAWAG is not trivial and field data is sparse. Extensive laboratory experiments and simulation studies are necessary to de-risk enhanced oil recovery (EOR) application, but these processes are time consuming and expensive. For this reason, a screening study is normally conducted to increase the possibility of selecting high potential candidates prior to embarking on the detailed feasibility studies. Unfortunately for FAWAG, the screening criteria are not readily established nor commonly available in commercial screening tools unlike for other matured EOR methods, largely contributed by the limited database on FAWAG field implementations worldwide. This paper presents a robust FAWAG screening tool which accounts for important reservoir properties, uncertainties in foam model parameters, as well as various reservoir conditions of oil and gas production and injection plans. The FAWAG process is modelled from the assumption of local equilibrium of foam creation and coalescence using an Implicit Texture model. Relevant foam scan experiments/steady state coreflood data were analyzed to derive parameters that characterize foam dynamics. The sensitivity study in this paper ranks and identifies the main risks and opportunities for the FAWAG process, quantifies the reliability of the model and increases the understanding of the effective dynamic behaviour. The sensitivity study was the basis for the development and validation of a proxy model by design of experiments. The screening tool employs this proxy model to generate immediate screening results without the need to run additional simulations. The screening tool was further validated with upscaled experimental data. A set of prediction results on the range of oil recovery for numerous plausible field scenarios was established; these screening criteria will be used as the basis for high-level decision making.

Personality Trait Detection Based on ASM Localization and Deep Learning

Global competition is the competition of human resources, the social demand for high-quality talents is increasing, and the demand for all kinds of talents is increasing. Therefore, how to scientifically and efficiently complete the preliminary screening of college students’ mental health, so as to provide services for them, has become an important task. In order to solve the above problems, by combining the relevant professional knowledge of psychology, statistics, image processing, and artificial intelligence technology, a personality trait detection method based on active shape model (ASM) localization and deep learning is proposed. Firstly, the traditional ASM algorithm is improved and applied to facial feature point location, which provides training basis for further deep learning. It mainly includes three aspects of improvement: (1) 2D texture model based on Gabor wavelet and gradient features; (2) new multiresolution pyramid decomposition method; and (3) improved multiresolution pyramid search strategy. Secondly, the deep belief network model is used to train and classify the students’ four personality traits and facial features, so as to dig out the relationship between the four personality traits and facial features. The experimental results show that the localization effect of the improved ASM algorithm is obviously better than that of the traditional algorithm, and the classifier after learning and training has a good effect in analyzing the four personality traits.

Mapping of Meteorological Observations over the Island of Ireland to Enhance the Understanding and Prediction of Rain Erosion in Wind Turbine Blades

Leading edge erosion is becoming increasingly important as wind turbine size and rainfall are predicted to increase. Understanding environmental conditions is key for laboratory testing, maintenance schedules and lifetime estimations to be improved, which in turn could reduce costs. This paper uses weather data in conjunction with a rain texture model and wind turbine RPM curve to predict and characterise rain erosion conditions across Ireland during rainfall events in terms of droplet size, temperature, humidity and chemical composition, as well as the relative erosivity, in terms of number of annual impacts and kinetic energy, as well as seasonal variations in these properties. Using a linear regression, the total annual kinetic energy, mean temperature and the mean humidity during impact are mapped geospatially. The results indicate that the west coast of Ireland and elevated regions are more erosive with higher kinetic energy. During rain events, northern regions tend to have lower temperatures and lower humidities and mountainous regions have lower temperatures and higher humidities. Irish rain has high levels of sea salt, and in recent years, only a slightly acidic pH. Most erosion likely occurs during winters with frequent rain infused with salt due to increased winds. After this analysis, it is concluded that Ireland’s largest wind park (Galway) is placed in a moderate-highly erosive environment and that RET protocols should be revisited.

Evaluating the Tire/Pavement Noise and Surface Texture of Low-Noise Micro-Surface Using 3D Digital Image Technology

As a common preventive maintenance technique for asphalt pavement, micro-surface (MS) has the advantages of waterproofing and crack sealing. However, issues such as the fact that the conventional MS generates large noise and the evaluation of the indexes of tire-road noise are relatively less studied. The traditional surface texture index cannot reveal the range and distribution of pavement surface texture, thus hindering research of low-noise MS. To study the mechanism of tire-road noise generated by MS, and propose the tire-road noise and surface texture indicators for MS. In this study, the mechanism of five low-noise MS was systematically analyzed and compared through surface texture and noise tests. Then, a three-dimensional digital texture model (3D-DTM) of MS surface texture was constructed using a series of digital image processing techniques, including grayscale identification, binary conversion, and noise reduction. The results show that optimizing the gradation, adding sound-absorbing materials, and improving the workability of construction can improve the noise reduction performance of MS, it is worth mentioning that the MS prepared with sound-absorbing materials and low-noise gradation has the greatest noise reduction effect, with a maximum reduction of 6.3 dB(A). In addition, it was also found that the 3D-DTM can well reflect the surface texture characteristics of MS. The probability of convex peak distribution (PCD) and the proportion of convex peak area (PCA) with peak heights greater than 0.25 mm (Kh ≥ 0.25), which are extracted from the 3D-DTM, can well reflect the surface texture, tire-road noise, respectively. The results show that the 3D-DTM is a promising tool to optimize the design of low-noise MS.

Ultrasonography in the Differentiation of Endometriomas from Hemorrhagic Ovarian Cysts: The Role of Texture Analysis

The ultrasonographic (US) features of endometriomas and hemorrhagic ovarian cysts (HOCs) are often overlapping. With the emergence of new computer-aided diagnosis techniques, this is the first study to investigate whether texture analysis (TA) could improve the discrimination between the two lesions in comparison with classic US evaluation. Fifty-six ovarian cysts (endometriomas, 30; HOCs, 26) were retrospectively included. Four classic US features of endometriomas (low-level internal echoes, perceptible walls, no solid components, and less than five locules) and 275 texture parameters were assessed for every lesion, and the ability to identify endometriomas was evaluated through univariate, multivariate, and receiver operating characteristics analyses. The sensitivity (Se) and specificity (Sp) were calculated with 95% confidence intervals (CIs). The texture model, consisting of seven independent predictors (five variations of difference of variance, image contrast, and the 10th percentile; 100% Se and 100% Sp), was able to outperform the ultrasound model composed of three independent features (low-level internal echoes, perceptible walls, and less than five locules; 74.19% Se and 84.62% Sp) in the diagnosis of endometriomas. The TA showed statistically significant differences between the groups and high diagnostic value, but it remains unclear if the textures reflect the intrinsic histological characteristics of the two lesions.