Texture Analysis Techniques Research Articles

Automatic gauze tracking in laparoscopic surgery using image texture analysis

Background and ObjectiveInadvertent retained surgical gauzes are an infrequent medical error but can have devastating consequences in the patient health and in the surgeon professional reputation. This problem seems easily preventable implementing standardized protocols for counting but due to human errors it still persists in surgery. The omnipresence of gauzes, their small size, and their similar appearance with tissues when they are soaked in blood make this error eradication really complex. In order to reduce the risk of accidental retention of surgical sponges in laparoscopy operations, in this paper we present an image processing system that tracks the gauzes on the video captured by the endoscope. MethodsThe proposed image processing application detects the presence of gauzes in the video images using texture analysis techniques. The process starts dividing the video frames into square blocks and each of these blocks is analyzed to determine whether it is similar to the gauze pattern. The video processing algorithm has been tested in a laparoscopic simulator under different conditions: with clean, slightly stained and soaked in blood gauzes as well as against different biological background tissues. Several methods, including different Local Binary Patterns (LBP) techniques and a convolutional neural network (CNN), have been analyzed in order to achieve a reliable detection in real time. ResultsThe proposed LBP algorithm classifies the individual blocks in the image with 98% precision and 94% sensitivity which is sufficient to make a robust detection of any gauze that appears in the endoscopic video even if it is stained or soaked in blood. The results provided by the CNN are superior with 100% precision and 97% sensitivity, but due to the high computational demand, real-time video processing is not attainable in this case with standard hardware. ConclusionsThe algorithm presented in this paper is a valuable tool to avoid the retention of surgical gauzes not only because of its reliability but also because it processes the video transparently and unattended, without the need for additional manipulation of special equipment in the operating room.

Performance evaluation of various texture analysis techniques for machine vision-based characterisation of machined surfaces

Machine vision-based inspection of surface quality leverages the principle of surface-texture characterisation, capitalising on image data characteristics. Frequently, surface-texture analysis adopts statistical and filter-based techniques, for this purpose. For surface texture characterisation, traditionally researchers prefer – parameterised histograms, grey level co-occurrence matrices, discrete Fourier transforms as well as discrete wavelet transforms. Despite popular usage, extant literature features very little in terms of comparative analyses amongst these techniques. Accordingly, this paper evaluates comparative performance of these techniques, for characterisation of machined surfaces and also recommends a novel hybrid technique that leverages higher discriminating capability. This hybrid discriminant-analysis methodology is derived from characterisation of 532 images of multi-textured machined surfaces. The results prove that the proposed technique, provides superior performance with higher accuracy, while requiring reduced optimal set of parameters, for inspection of surface quality.

Performance evaluation of various texture analysis techniques for machine vision-based characterisation of machined surfaces

Performance evaluation of various texture analysis techniques for machine vision-based characterisation of machined surfaces

Damage Diagnosis of Reactive Powder Concrete under Fatigue Loading Using 3D Laser Scanning Technology

Damage mechanisms of Reactive Powder Concrete (RPC) under fatigue loading are investigated using the 3D laser scanning technology. An independently configured 3D laser scanning system is used to monitor the damaging procedure. Texture analysis technique is also applied to enhance the understanding of the damage mechanisms of RPC under fatigue loading. In order to obtain the characteristic parameters of point cloud data, a point clouds projection algorithm is proposed. Damage evolution is described by the change of point cloud data of the damage in the 2D plane and 3D space during fatigue loading. The Gray Level Co-occurrence Matrix (GLCM) method is used to extract the characteristic parameters to evaluate the statue of the structural. Angular Second Moment and Cluster Shadow of typical sensitive characteristic indexes is screened by using the Digital Feature Screening. The reliability of the damage index was verified by image texture analysis and data expansion. Indexes extracted in this paper can be used as a new structural health monitoring indicator to assess health condition.

Efficient removal of parabens from real water matrices by a metal-free carbon nitride photocatalyst.

Metal-free graphite-like carbon nitride (GCN-500) was obtained by thermal post-treatment of bulk polymeric carbon nitride at 500°C. The catalyst was thoroughly characterized by morphological, optical and textural analysis techniques. The efficiency of GCN-500 was evaluated under visible (λexc=417nm) LED excitation for the photocatalytic degradation of methyl-, ethyl- and propyl-paraben in different water matrices either isolated or in a mixture of the three compounds. The GCN-500 proved to be more efficient than the benchmark TiO2 P25, with complete conversion of the individual parabens within 20min of irradiation, contrasting with 120min needed for total degradation using TiO2. Experiments in the presence of selected scavengers confirmed the high importance of superoxide radicals in the photocatalytic oxidation of parabens using GCN-500. The effect of the nature of the aqueous matrix in the kinetics of the photocatalytic process was assessed using ultrapure, tap and river waters spiked with a mixture of the three parabens. Although still very efficient, the complexity of the real water samples turned the degradation process slower due to the presence of other components such as ions and dissolved organic matter. GCN-500 proved to be stable in a continuous-flow system using GCN-500 coated glass rings (GCN-500-GR) to remove MP, EP and PP from real water matrices.

Key Parameter Identification and Defective Wafer Detection of Semiconductor Manufacturing Processes Using Image Processing Techniques

The semiconductor industry has become fully automated during the manufacturing process and abundant process parameters are collected online by sensors for fault detection and classification purposes. Analyzing process parameters and identifying a smaller set of key parameters that have crucial influence on wafer quality will bring great benefits in stabilizing the manufacturing process and enhancing productive yield. Typically, this type of the parameter set is called the “raw trace data.” This paper considers image processing techniques as a novel approach for analyzing and visualizing the raw trace data. First, the 1-D time series data of a wafer batch was transformed into a 2-D image. Fisher’s criterion ratios of the labeled good and defective wafer image maps are computed to identify the key parameters. The key parameters identified by the proposed image processing technique are consistent with the technical experience of the process engineers. Furthermore, the texture analysis technique with 2-D Fourier transform is utilized to analyze the images of the key parameters to detect defective wafers. The proposed key parameter identification and wafer classification method proves to be a viable solution under the paradigm of advanced process control practice for semiconductor manufacturing.

3D surface texture analysis of high-resolution normal fields for facial skin condition assessment.

BackgroundThis paper investigates the use of a light stage to capture high‐resolution, 3D facial surface textures and proposes novel methods to use the data for skin condition assessment.Materials and MethodsWe introduce new methods for analysing 3D surface texture using high‐resolution normal fields and apply these to the detection and assessment of skin conditions in human faces, specifically wrinkles, pores and acne. The use of high‐resolution normal maps as input to our texture measures enables us to investigate the 3D nature of texture, while retaining aspects of some well‐known 2D texture measures. The main contributions are as follows: the introduction of three novel methods for extracting texture descriptors from high‐resolution surface orientation fields; a comparative study of 2D and 3D skin texture analysis techniques; and an extensive data set of high‐resolution 3D facial scans presenting various skin conditions, with human ratings as “ground truth.”ResultsOur results demonstrate an improvement on state‐of‐the‐art methods for the analysis of pores and comparable results to the state of the art for wrinkles and acne using a considerably more compact model.ConclusionsThe use of high‐resolution normal maps, captured by a light stage, and the methods described, represent an important new set of tools in the analysis of skin texture.

Two-photon images reveal unique texture features for label-free identification of ovarian cancer peritoneal metastases.

For cancer patients, treatment selection fundamentally relies on staging, with "under-staging" considered a common problem. Imaging modalities that can complement conventional white-light laparoscopy are needed to detect more accurately small metastatic lesions in patients undergoing operative cancer care. Biopsies from healthy parietal peritoneum and ovarian peritoneal metastases obtained from 8 patients were imaged employing a two-photon laser scanning microscope to generate collagen-second harmonic generation (SHG) and fluorescence images at 755 nm and 900 nm excitation and 460 ± 20 nm and 525 ± 25 nm emission. Forty-one images were analyzed by automated image processing algorithms and statistical textural analysis techniques, namely gray level co-occurrence matrices. Two textural features (contrast and correlation) were employed to describe the spatial intensity variations within the captured images and outcomes were used for discriminant analysis. We found that healthy tissues displayed large variations in contrast and correlation features as a function of distance, corresponding to repetitive, increased local intensity fluctuations. Metastatic tissue images exhibited decreased contrast and correlation related values, representing more uniform intensity patterns and smaller fibers, indicating the destruction of the healthy stroma by the cancerous infiltration. The textural outcomes resulted in high classification accuracy as evaluated quantitatively by discriminant analysis.

Magnetic Resonance Imaging, texture analysis and regression techniques to non-destructively predict the quality characteristics of meat pieces

The quality of meat products is traditionally assessed by chemical or sensorial analysis, which are time consuming, need specialized technicians and destroy the products. The development of new technologies to monitor meat pieces using non-destructive methods in order to establish their quality is earning importance in the last years. An increasing number of studies have been carried out on meat pieces combining Magnetic Resonance Imaging (MRI), texture descriptors and regression techniques to predict several physico-chemical or sensorial attributes of the meat, mainly different types of pig ham and loins. In spite of the importance of the problem, the conclusions of these works are still preliminary because they only use the most classical texture descriptors and regressors instead of stronger methods, and because the methodology used to measure the performance is optimistic. In this work, we test a wide range of texture analysis techniques and regression methods using a realistic methodology to predict several physico-chemical and sensorial attributes of different meat pieces of Iberian pigs. The texture descriptors include statistical techniques, like Haralick descriptors, local binary patterns, fractal features and frequential descriptors, like Gabor or wavelet features. The regression techniques include linear regressors, neural networks, deep learning, support vector machines, regression trees, ensembles, boosting machines and random forests, among others. We developed experiments using 15 texture feature vectors, 28 regressors over 4 datasets of Iberian pig meat pieces to predict 39 physico-chemical and sensorial attributes, summarizing16,380 experiments. There is not any combination of texture vector and regressor which provides the best result for all attributes tested. Nevertheless, all these experiments provided the following conclusions: (1) the regressor performance, measured using the squared correlation (R2), is from good to excellent (above 0.5625) for 29 out of 39 attributes tested; (2) the WAPE (Weighted Absolute Percent Error) is lower than 2% for 32 out of 37 attributes; (3) the dispersion in computer predictions around the true attributes is lower or similar than the dispersion in the labeling expert’s for the majority of attributes (85%); and (4) differences between predicted and true values are not statistically significant for 29 out of 37 attributes using the Wilcoxon ranksum statistical test. We can conclude that these results provide a high reliability for an automatic system to predict the quality of meat pieces, which may operate on-line in the meat industries in the future.

Image processing based automatic recognition of asphalt pavement patch using a metaheuristic optimized machine learning approach

Patch detection is an important task in pavement condition survey. This study establishes an automatic approach for asphalt pavement patch recognition based on image texture analysis and hybrid machine learning algorithms. Features based on image texture that employs statistical properties of color channels and the gray-scale co-occurrence matrix are used by the Least Squares Support Vector Machine (LSSVM) for discriminating patched areas from non-patch ones. In addition, to optimize the LSSVM training phase, the Differential Flower Pollination (DFP) metaheuristic is used. A data set constructed from a set of 1000 image samples has been utilized to train and verify the proposed integration of image texture analysis techniques, LSSVM, and DFP. Experimental results show that the new model can achieve a good prediction result with Classification Accuracy Rate = 95.30%, Positive Predictive Value = 0.96, and the Negative Predictive Value = 0.95. Additionally, a patch detection program has been developed and compiled in Visual C# .NET to ease the implementation of the hybrid model. Thus, the newly developed method can be a potential tool for traffic management agencies during the phase of pavement condition evaluation.

TAU AND AMYLOID PATHOLOGY IN ASSOCIATION WITH SUBJECTIVE COGNITIVE PERFORMANCE IN NORMAL ELDERLY AND EARLY MILD COGNITIVE IMPAIRMENT

Introduction Subjective cognitive decline (SCD) is increasingly recognized as the initial outward sign of preclinical Alzheimer's disease. The ability to explore associations between individual domains (memory, language, executive function, and visuospatial processing) within SCD and pathological biomarkers of early Alzheimer's disease (AD) would provide an important tool to understand the pathological basis of SCD and the risk of developing AD. This study utilizes multiple data analysis strategies to characterize the severity of tau and amyloid burden in clinically normal elderly adults (CN) and early mild cognitive impairment (EMCI) subjects in association with their subjective cognitive decline. Methods All subject data were downloaded from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. These included [18F]flortaucipir tau and [18F]florbetapir amyloid positron emission tomography (PET) images as well as the subjects’ SCD scores. For SCD, we used the self- and informant-reported individual items of Everyday Cognition questionnaire, including Everyday Memory, Everyday Language, Everyday Visuospatial abilities, Everyday Planning, Everyday Organization, and Everyday Divided Attention. The severity of the amyloid burden was calculated as the global PET standardized uptake value ratio (SUVR). The severity of the tau burden was assessed with both standard uptake-based approaches (tau SUVR in entorhinal, limbic, and isocortical Braak regions) and with a novel texture analysis tool, the weighted two-point correlation (wS2) analysis, which characterizes the spatial clustering of the tau-PET image as a sensitive early biomarker of abnormal tau accumulation and spread. Using a series of backward-elimination regression models, we identified the combination of predictors (from tau PET, global amyloid-PET, sex, APOE, and age) that constructed the best model to predict each Everyday Cognition item. Results Across the different cognitive tests, the wS2-based tau measures explained more variability, as indicated by p-values and elevated R-squared values, than standardized uptake value ratios from different anatomical regions, including the entorhinal cortex which is believed to be among the first regions to show signs of tau pathology. Our backward-elimination regression analyses (figure 1) suggested that the strongest models for predicting some of the subjective cognitive items (Everyday Memory, Everyday Language, Everyday Organization) were more likely amyloid-related whereas other items (Everyday Visuospatial, Everyday Planning) were more tau-related (together with age and sex). Conclusions Different pathological pathways may influence the manifestation of different subjective cognitive items. In vivo texture analysis techniques may be used as sensitive tools to explore these pathological pathways in association with the earliest signs of cognitive changes.

Comparative Study on Local Binary Patterns for Mammographic Density and Risk Scoring.

Breast density is considered to be one of the major risk factors in developing breast cancer. High breast density can also affect the accuracy of mammographic abnormality detection due to the breast tissue characteristics and patterns. We reviewed variants of local binary pattern descriptors to classify breast tissue which are widely used as texture descriptors for local feature extraction. In our study, we compared the classification results for the variants of local binary patterns such as classic LBP (Local Binary Pattern), ELBP (Elliptical Local Binary Pattern), Uniform ELBP, LDP (Local Directional Pattern) and M-ELBP (Mean-ELBP). A wider comparison with alternative texture analysis techniques was studied to investigate the potential of LBP variants in density classification. In addition, we investigated the effect on classification when using descriptors for the fibroglandular disk region and the whole breast region. We also studied the effect of the Region-of-Interest (ROI) size and location, the descriptor size, and the choice of classifier. The classification results were evaluated based on the MIAS database using a ten-run ten-fold cross validation approach. The experimental results showed that the Elliptical Local Binary Pattern descriptors and Local Directional Patterns extracted most relevant features for mammographic tissue classification indicating the relevance of directional filters. Similarly, the study showed that classification of features from ROIs of the fibroglandular disk region performed better than classification based on the whole breast region.

Application of Texture Analysis Technique in Formulation Development of Lyophilized Orally Disintegrating Tablets Containing Mannitol, Polyvinylpyrrolidone and Amino Acids.

Orally disintegrating tablets (ODTs) attract a great attention as this easy swallowing dosage form often improves patient compliance. In the current work, orally disintegrating tablets comprising mannitol, polyvinylpyrrolidone (PVP) and an amino acid (alanine, glycine or serine) with various PVP-to-amino acid ratios were formulated. The combination of mannitol and an amino acid was aimed to use the advantages of mannitol, the matrix-supporting and disintegration agent, and to reduce the total amount of sugar/polyol in tablets. Tablets were manufactured by freeze-drying and their properties (appearance, internal structure, disintegration, mechanical and texture properties, moisture uptake, shrinkage, thermal properties) were assessed. In the work, great emphasis was placed on illustrating the applicability of the Texture Analysis of the freeze-dried cakes directly in vials in formulation development. The results show that the appearance, mechanical properties, disintegration and shrinkage of the freeze-dried ODTs depend significantly on the excipient composition with PVP playing the leading role. Partial mannitol replacement with an amino acid has a limited impact on the tablet properties. The presence of an amino acid also has no impact on the PVP-mannitol interaction. The mechanical and texture properties of freeze-dried ODTs depend non-linearly on the PVP content. The transition between the different types of textures occurs in a narrow range of PVP concentrations regardless of the type of amino acid in a formulation. The non-linear effect of PVP on various tablet properties should be taken into account when designing ODT formulations as it can compromise the robustness of the manufacturing process.

Estimation of soil texture from permanent wilting point measured with a chilled‐mirror dewpoint technique

AbstractSoil texture is an important factor governing a range of physical properties and processes in soil. The clay and fine fractions of soil are particularly important in controlling soil water retention, hydraulic properties, water flow and transport. Modern soil texture analysis techniques (x‐ray attenuation, laser diffraction and particle counting) are very laborious with expensive instrumentation. Chilled‐mirror dewpoint potentiameters allows for the rapid measurement of the permanent wilting point (PWP) of soil. As the PWP is strongly dictated by soil texture, we tested the applicability of PWP measured by a dewpoint potentiameter in predicting the clay, silt and sand content of humid tropical soils. The clay, silt, and sand content, organic matter and PWP were determined for 21 soils. Three regression models were developed to estimate the fine fractions and validated using independent soil data. While the first model showed reasonable accuracy (RMSE 16.4%; MAE 13.5%) in estimating the clay, incorporating the organic matter into the equation improved the predictions of the second model (RMSE 17.3%; MAE 10.9%). When used on all soil data, the accuracy of the third model in predicting the fine fraction was poor (RMSE 31.9%; MAE 24.5%). However, for soils with silt content greater than 30%, the model prediction was quite accurate (RMSE 7–12%; MAE 7–9%). The models were used to estimate the sand content and soil textures of soils, which proved relatively accurate. The dewpoint potentiometer can serve a dual purpose of rapidly estimating the PWP and the clay, fine fraction, and soil texture of soils in a cost efficient way.

Synthesis and characterization of pecan nutshell-based adsorbent with high specific area and high methylene blue adsorption capacity

In this work, a pecan nutshell was used as a precursor to prepare activated hydrochar (HPN-ATV) with a high specific area. HPN-ATV was engineered as an adsorbent for the removal of methylene blue (MB) from aqueous environments. The obtained materials were characterized using textural, morphological, and chemical analysis techniques. HPN-ATV showed a BET specific area of 2342 m2 g−1 and an average pore diameter of 2.24 nm. Boehm titration and FTIR spectral analysis indicated the presence of acidic and basic functional groups on the surface of HPN-ATV, which rendered the removal of cationic molecules more favorable. The kinetic experimental data better correlated with the pseudo-second-order kinetic and Elovich equations, and the Langmuir model showed a better fit for the data of MB adsorption. The adjusting parameters indicated a monolayer with an adsorption capacity of 1190.62 mg g−1, which is higher than that of the other adsorbents reported in literature. In addition, the thermodynamic data suggested a spontaneous adsorption process with endothermic characteristics.

A Review of Machine Vision based Evaluation of Surface Roughness using Texture Analysis Techniques

A Review of Machine Vision based Evaluation of Surface Roughness using Texture Analysis Techniques

Quantitative ultrasound of trapezius muscle involvement in myofascial pain: comparison of clinical and healthy population using texture analysis.

Ultrasound is a non-invasive quantitative method to characterize sonographic textures of skeletal muscles. To date, there is no information available on the trapezius muscle. This study assessed the trapezius muscles of patients with myofascial pain compared with normal healthy participants. The trapezius muscles of 15 healthy and 17 myofascial pain participants were assessed using B-mode ultrasound to obtain 120 images for healthy and 162 images from myofascial pain participants. Texture features such as blob area, count and local binary patterns (LBP) were calculated. Multi-feature classification and analysis were performed using principal component analysis (PCA) and MANOVA to determine whether there were statistical differences. We demonstrate the two principal components composed of a combination of LBP and blob parameters which explain 92.55% of the cumulative variance of our data set. In addition, blob characteristics were significantly different between healthy and myofascial pain participants. Our study provides evidence that texture analysis techniques can differentiate between healthy and myofascial pain affected trapezius muscles. Further research is necessary to evaluate the nature of these differences.

Quantitative assessment of myocardial scar heterogeneity using cardiovascular magnetic resonance texture analysis to risk stratify patients post-myocardial infarction

To determine whether heterogeneity of cardiac scar, as assessed by cardiovascular magnetic resonance (CMR) texture analysis, may provide insight into better risk stratification for patients with previous myocardial infarction (MI). Patients with previous MI (n=76) were followed for a median of 371.5 days after late gadolinium enhancement (LGE) CMR. The primary endpoint was a composite of ventricular tachycardia, ventricular fibrillation, or unexplained syncope. Areas of LGE were identified and manually segmented on a short-axis projection. The characteristics of the scar heterogeneity were evaluated via CMR texture analysis. This is a filtration-histogram technique, where images are filtered using the Laplacian of a Gaussian filter to extract features different sizes (2-6 mm in radius) corresponding to fine, medium, and coarse texture scales followed by a quantification step using histogram analysis (skewness and kurtosis). Patients suffering arrhythmic events during the follow-up period demonstrated significantly higher kurtosis (coarse-scale, p=0.005) and lower skewness (fine-scale, p=0.046) compared to those suffering no arrhythmic events. Furthermore, Kaplan-Meier analysis showed significantly higher coarse kurtosis (p=0.004), and lower fine skewness (p=0.035) were able to predict increased incidence of ventricular arrhythmic events. In this pilot study, indices of texture analysis reflecting textural heterogeneity were significantly associated with a greater incidence of arrhythmic events. Further work is required to delineate the role of texture analysis techniques in risk stratification post-MI.

New Image Texture Analysis, and Application to Polymer Membrane Surface Morphologies and Roughness.

A new method of image texture analysis is presented, based on the mean and standard deviation of gray levels within domains in an image. The calculations are performed recursively on domains of various sizes within the images. These gray level calculations are used as the input matrix for principal component analysis. The technique analyzes the entire image as a whole and is not for image segmentation. The analysis routine operates across all distances, frequencies and directions in the image, and is not computationally burdensome. The method was applied to scanning electron microscope images of reverse osmosis membranes on domains from 23 nm to 3 µm. The texture analysis technique performed well in identifying the surface morphology and, once calibrated, in predicting the surface roughness as measured by atomic force microscopy.

Single extracorporeal shock-wave lithotripsy for proximal ureter stones: Can CT texture analysis technique help predict the therapeutic effect?

PurposeTo explore whether the computed tomography texture analysis (CTTA) technique can help predict the curative effects of a single extracorporeal shock-wave lithotripsy (ESWL) for proximal ureteral stones. Materials and methodsIn all, 100 patients with proximal ureteral stone underwent non-enhanced multi-detector computed tomography (MDCT) before ESWL. The patients were divided into success and failure groups. Success of ESWL was defined as the patients being stone-free or having residual stone fragments of ≤2 mm. Traditional characteristics, such as stone size, body mass index (BMI), and skin-to-stone distance (SSD), and CTTA metrics, such as the mean Hounsfield unit (HU) density, entropy, kurtosis, and skewness, were analyzed and compared between two groups by univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC) curves were generated to determine Youden index-based cutoff values. ResultFailure of stone removal was observed in 36 patients (36%). Stone height, stone cross-sectional diameter, largest cross-sectional area, stone volume, stone density (mean HU), and CTTA metrics (kurtosis and entropy) were the significant independent predictors of ESWL success on univariate analysis (p < 0.05). On multivariate analysis, mean HU, skewness, and kurtosis were shown to be significant predictors of ESWL success (p < 0.05). In subgroup analysis based on the cutoff value of mean stone density (HU = 857), the only significant independent factor associated with both subgroups was kurtosis (p < 0.05). ConclusionsAs a quantitative analysis method, CTTA may be helpful in selecting appropriate ESWL patients. High kurtosis and low mean HU values simultaneously indicate a relatively higher ESWL success rate.