Texture Analysis Methods Research Articles

Three-Dimensional Surface Texture Analysis of Fluoride's Effect on Enamel Erosion.

Enamel erosion has become a common clinical finding that often impairs dental esthetics and function. In the current study, we aimed to implement the three-dimensional surface texture analysis (3DST) method to explore the protective effect of fluoride on surface texture prior to erosive conditions. For each of the 50 teeth used in this study, the polished buccal enamel surface was divided into three separate areas: the first area was untouched polished enamel, the two other surfaces were immersed in 0.3% citric acid for 30 s. One was treated with high-concentration (19,000 ppm) sodium fluoride (NaF) solution prior to acid attack, and the other had no treatment prior to acid exposure. Enamel surface texture and step height measurements were obtained using a high-resolution disk scanning confocal microscope, and SEM images were also acquired. Surfaces treated with fluoride showed fewer variations in 3-D surface texture parameters than the eroded surface compared to the control group (p = 0.001). This was in accordance with the SEM descriptive images. The findings indicate that pre-fluoridated enamel areas were less affected by the acid and showed similar features to the untouched enamel. Moreover, a protective effect of the fluoride treatment against irreversible enamel damage was noted as the surface loss (step-height) was significantly reduced (p = 0.03). The study showed that 3DST analysis is a valuable methodology for detecting and quantifying subtle differences between the surfaces. When exploring the combination of all surface texture parameters, it was revealed that the pre-fluoridated eroded enamel surfaces showed considerable similarity to the untouched enamel.

Diffraction Methods for Qualitative and Quantitative Texture Analysis of Ferroelectric Ceramics.

Crystallographic textures are pervasive in ferroelectrics and underpin the functional properties of devices utilizing these materials because many macroscopic properties (e.g., piezoelectricity) require a non-random distribution of dipoles. Inducing a preferred grain texture has become a viable route to improve these functional properties. X-ray and neutron diffraction have become valuable tools to probe crystallographic textures. This paper presents an overview of qualitative and quantitative methods for assessing crystallographic textures in electroceramics (domain and grain textures) and discusses their strengths and weaknesses.

ELECTROCHEMICAL POLYMORPHIC PHASE FORMATION IN METALS

The phenomenon of electrochemical phase formation in metals and alloys via a supercooled liquid state stage was discussed. Assuming the electrodeposited metal to be a product of formation and ultrarapid solidification of supercooled metallic liquid, a possibility of metastable phase formation during electrodeposition of polymorphous metals was suggested. It was anticipated that the polymorphic transition of the metal’s metastable form to the stable one occurs by shear, as does the martensitic transformation. To enable revealing an orientation relationship between grains of the two phases, a method for X-ray texture analysis of metals was developed using a combination of direct pole figures. It was established that the phase formation during electrodeposition of polymorphous metals produces metastable modifications typical of entities that crystallized from a liquid state at extremely high rates. In regards polymorphic transitions in metal electrodeposition, certain orientation relationships were observed between grains of the stable and the metastable phase, which is typical of phase transformations proceeding at extremely high rates. The results obtained provided additional arguments in favor of the phenomenon under discussion.

Texture analysis of dorsal striatum in functional neurological (conversion) disorder.

In this study, it was aimed to evaluate the dorsal striatum nuclei of patients diagnosed with Functional Neurological Disorder by texture analysis method from magnetic resonance imaging images and to compare them with healthy controls. Study groups consisted of 20 female patients and 20 healthy women. The brains of patients and controls were scanned for high-resolution images with a 1.5T scanner using the sagittal plane and 3D spiral fast spin echo sequence. Using the texture analysis method, mean, standard deviation, minimum, maximum, median, variance, entropy, size %L, size %U, size %M, kurtosis, skewness and homogeneity values of the dorsal striatum nuclei were calculated from the images. The data were compared with comparison tests according to Kolmogorov-Smirnov test results. There was no statistically significant difference between paired regions in terms of texture analysis findings in the cross-sectional images of the participants. In patients, mean, standard deviation, minimum, maximum, median, variance and entropy values for the putamen nucleus, and mean, standard deviation, minimum, maximum, median, variance, entropy and kurtosis values for the caudate nucleus were found significantly higher than controls. Additional receiver operating characteristic curve and logistic regression analyzes were performed. The implications of the results of the study are that there are significant microstructural changes in the dorsal striatum nuclei of patients and their reflection on brain images. Texture analysis is a useful technique to show tissue changes in the dorsal striatum of patients using images. It is highly recommended to use texture analysis to identify and evaluate potentially affected areas of the brain in new studies.

Detection of Glaucoma using ORB (Oriented FAST and Rotated BRIEF) Feature Extraction

This research was utilized to identify glaucoma, a type of eye illness. This endeavor necessitates the use of pictures from the fundus camera for image processing. This study reflects the effort done to detect glaucoma-affected eyes utilizing image feature extraction using Oriented FAST and Rotated BRIEF (ORB). ORB is a binary descriptor approach that is based on BRIEF and is highly fast. This technique is insensitive to picture noise and is invariant to any rotation. ORB is two orders of magnitude faster than SURF and performs similarly to SIFT. It is more efficient than other texture analysis methods. It is less computationally difficult than other approaches in the literature. This technique extracts features and detects texture by inspecting each pixel of the retina picture. It was trained on 160 fundus pictures of normal and glaucoma-affected retinas. After that, any healthy or glaucoma-affected eye may be easily recognized by obtaining an accurate eye picture. The results reveal that this technique has a precision and accuracy of more than 90%.

A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers.

We propose a novel approach to develop a computer-aided decision support system for radiologists to help them classify brain degeneration process as physiological or pathological, aiding in early prognosis of brain degenerative diseases. Our approach applies computational and mathematical formulations to extract quantitative information from biomedical images. Our study explores the longitudinal OASIS-3 dataset, which consists of 4096 brain MRI scans collected over a period of 15 years. We perform feature extraction using Pyradiomics python package that quantizes brain MRI images using different texture analysis methods. Studies indicate that Radiomics has rarely been used for analysis of brain cognition; hence, our study is also a novel effort to determine the efficiency of Radiomics features extracted from structural MRI scans for classification of brain degenerative diseases and to create awareness about Radiomics. For classification tasks, we explore various ensemble learning classification algorithms such as random forests, bagging-based ensemble classifiers, and gradient-boosted ensemble classifiers such as XGBoost and AdaBoost. Such ensemble learning classifiers have not been used for biomedical image classification. We also propose a novel texture analysis matrix, Decreasing Gray-Level Matrix or DGLM. The features extracted from this filter helped to further improve the accuracy of our decision support system. The proposed system based on XGBoost ensemble learning classifiers achieves an accuracy of 97.38%, with sensitivity 99.82% and specificity 97.01%.

A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity.

Simple SummaryDosiomics is born directly as an extension of radiomics: it entails extracting features from the patients’ three-dimensional (3D) radiotherapy dose distribution rather than from conventional medical images to obtain specific spatial and statistical information. Dosiomic studies, in a multicentre setting, require assessing the features’ stability to dose calculation settings and the features’ capability in distinguishing different dose distributions. This study provides the first multicentre evaluation of the dosiomic features in terms of reproducibility, stability and sensitivity across various dose distributions obtained from multiple technologies and techniques and considering different dose calculation algorithms of TPS and two different resolutions of the dose grid. Harmonisation strategies to account for a possible variation in the dose distribution due to these confounding factors should be adopted when investigating a correlation between dosiomic features and clinical outcomes in multicentre studies.Dosiomics is a texture analysis method to produce dose features that encode the spatial 3D distribution of radiotherapy dose. Dosiomic studies, in a multicentre setting, require assessing the features’ stability to dose calculation settings and the features’ capability in distinguishing different dose distributions. Dose distributions were generated by eight Italian centres on a shared image dataset acquired on a dedicated phantom. Treatment planning protocols, in terms of planning target volume coverage and dose–volume constraints to the organs at risk, were shared among the centres to produce comparable dose distributions for measuring reproducibility/stability and sensitivity of dosiomic features. In addition, coefficient of variation (CV) was employed to evaluate the dosiomic features’ variation. We extracted 38,160 features from 30 different dose distributions from six regions of interest, grouped by four features’ families. A selected group of features (CV < 3 for the reproducibility/stability studies, CV > 1 for the sensitivity studies) were identified to support future multicentre studies, assuring both stable features when dose distributions variation is minimal and sensitive features when dose distribution variations need to be clearly identified. Dosiomic is a promising tool that could support multicentre studies, especially for predictive models, and encode the spatial and statistical characteristics of the 3D dose distribution.

T1 and ECV mapping texture analysis distinguishing hypertrophic cardiomyopathy from athletes heart better than median values

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Ph.D fellowship of the Research Foundation Flanders (FWO). The Master@Heart trial is funded by the FWO. Introduction Differentiating intensive training induced hypertrophy from hyperthropic cardiomyopathy (HCM) is important to identify those young athletes at risk of sudden cardiac death. Swoboda and colleagues demonstrated that T1 and ECV mapping can aid such a differentiation between athletic and pathological hypertrophy, particularly in subjects with indeterminate wall thickness (1). Recently texture analysis (TA) methods of CMR data have demonstrated improved diagnostic accuracy over conventional qualitative analysis in various heart diseases. Only few studies have applied TA to T1 and ECV mapping data (2-4). Here we aimed to demonstrate that a TA approach provides superior capacity to distinguish HCM from athlete’s heart over average native T1 and ECV values. Purpose It was our hypothesis that a texture analysis of T1 and ECV mapping images would identify features that could discriminate between a HCM and athlete’s heart with a higher classification accuracy (CA) than average T1 and ECV values. Methods This study included data from 97 subjects diagnosed with HCM (acc. to guidelines; 5) and 28 athletes that took part in the Master@Heart trial (an ongoing study assessing the beneficial effects of long-term endurance exercise for the prevention of coronary artery disease, 6). Long and short axis T1 mapping data was acquired on a 1.5T Philips Ingenia system using MOLLI (seconds scheme). After offline motion correction and T1 and ECV map calculation (7), the left ventricular myocardium was manually delineated (3D Slicer; 8). Texture analysis of the masked images resulted in 194 features (Pyradiomics, standard settings; 9). The dataset was then split (75/25%) for training and testing purposes keeping images from the same subject within the same set. A fast correlation based filter rank was applied to the training data to derive relevant features. A further reduction to only two features was based on the CA of a support vector machine (SVM) learning method (linear kernel; cost 0.9 regression loss epsilon 0.1; leave-one-out). Finally, ROC analysis on the test data was used to determine the diagnostic accuracy for the following predictors: (1) median T1 and ECV (2) two most relevant features (training) (3) combination of (1) and (2) (ROC AUC statistics (10)). Results The two most relevant features were the histogram feature ECV energy and the gray level size zone matrix (GLSZM) feature native T1 zone entropy, a measure of heterogeneity in the texture pattern. A model to distinguish HCM from athletes based on these features outperformed the model using only median T1 and ECV values with both higher sensitivity and specificity (table 1) and a significantly higher AUC in the ROC analysis (p &amp;lt; 0.05, figure 1). Combining these two features with median values did not improve the CA further. Conclusion Texture analysis of motion-corrected T1 and ECV mapping images out-performs classical analysis based on average values in distinguishing HCM from athlete"s heart.

Retinal OCT Texture Analysis for Differentiating Healthy Controls from Multiple Sclerosis (MS) with/without Optic Neuritis.

Multiple sclerosis (MS) is an inflammatory disease damaging the myelin sheath in the central and peripheral nervous system in the brain and spinal cord. Optic Neuritis (ON) is one of the most prevalent ocular demonstrations of MS. The current diagnosis protocol for MS is MRI, but newer modalities like Optical Coherence Tomography (OCT) are already of interest in early detection and progression analysis. OCT reveals the symptoms of MS in the Central Nervous System (CNS) through cross-sectional images from neural retinal layers. Previous works on OCT were mostly focused on the thickness of retinal layers; however, texture features seem also to have information in this regard. In this research, we introduce a new pipeline that constructs layer-stacked (LS) images containing data from each specific layer. A variety of texture features are then extracted from LS images to differentiate between healthy controls and ON/None-ON MS cases. Furthermore, the definition of texture extraction methods is tailored for this application. After performing a vast survey on available texture analysis methods, a treasury of powerful features is collected in this paper. As a primary work, this paper shows the ability of such features in the diagnosis of HC and MS (ON and None-ON) cases. Our findings show that the texture features are powerful to diagnose MS cases. Furthermore, adding information of conventional thickness values to texture features improves considerably the discrimination between most of the target groups including HC vs. MS, HC vs. MS-None-ON, and HC vs. MS-ON.

Research Progress in Remote Sensing Image Texture Analysis

Current remote sensing image texture analysis and research is based texture feature. Specialist, who home and abroad, has been continuously explore an optimal extraction process and texture analyze method or through a variety of methods to realize extraction of remote sensing texture information. There are three directions, which including the research of optimal scale, the fusion of method, the proposal and improvement of new algorithm, could comprehensive summary about recent research progress in remote sensing image texture analysis by sorting and analyzing information extraction of remote sensing texture features, the progress of texture analysis methods and the application of analysis methods in recent years. At the same time, a prediction was made for future development of remote sensing image texture analysis. Five development directions are summarized: fusion method, scale selection, continuation and improvement of traditional methods based on symbiotic gray matrix and wavelet analysis, research and exploration of object-oriented and deep learning methods, and research of new algorithms to improve the information extraction effect.

A. Karamanov’s piano concerts: “curve” of the style and language evolution

The article is devoted to the peculiarities of the concert-piano style of A. Karamanov, an outstanding Ukrainian composer, whose works have only recently become the subject of musicological discourse. The “curve” of style and language evolution is outlined, the stages of which are marked by three large-scale concerts of the composer, the last of which called “Ave Maria” has already gained worldwide popularity and entered the repertoire of outstanding performers both in Ukraine and abroad. Based on a comprehensive analysis of the genre-stylistic complex of the phenomenon of “piano concert”, a definition of the concept of “concert-piano style” is offered, which is extrapolated to the author’s style of A. Karamanov in this genre. The characteristic features of the composer’s three piano concerts are highlighted and generalized, a kind of triptych cycle, in which through the work of A. Karamanov as a composer-pianist, constant and individually innovative features of the genre are embodied, which became symphonic conceptual model not only in his work, but also in the styles of other authors of the late XX — early XXI centuries.&#x0D; The relevance of the chosen topic is determined by the need for a systematic consideration of the triptych of piano concerts by A. Karamanov as a significant phenomenon of Ukrainian and world pianism at the turn of the XX–XXI centuries.&#x0D; The purpose of the research is to reveal the peculiarities of the style and language evolution of A. Karamanov’s work, the stages of which are represented by his three piano concerts.&#x0D; The methodology is a combination of general scientific and special musicological approaches to the disclosure of the stated topic. Among them are: the method of deduction, which determines the movement of research from the general (concert as a principle of musical thinking) to special (piano concert) and specific phenomenon (triptych piano concerts by A. Karamanov); methods of genre and stylistic analysis used in considering the drama and form of all three concerts; methods of performance and texture analysis aimed at identifying the features of Karamanov’s concert pianism.&#x0D; The results of the research reveal the combination of traditions and innovations in the interpretation of the genre in the concert-piano style of A. Karamanov. All three of his piano concertos are based on different models of drama and form, but common to them is the attraction to the symphonic concept, which brings the concert forms in his music closer to the symphony as a genre of “high generalizations” (Asafiev, 1971).&#x0D; The scientific topicality of this research is determined by the fact that for the first time In Ukrainian musicology it presents a comprehensive review of the aesthetics and poetics of three piano concerts by A. Karamanov, which reflect the characteristics of modern interpretation of the genre with a tendency to symphonization.&#x0D; The practical significance of this scientific research is determined by the possibility of applying its provisions and conclusions in the further study of the laws of modern concert-piano style in the works of other composers. The article can be useful for performers who turn to interpretations of Karamanov’s piano concerts, as well as for teachers and students of piano departments and branches of art universities of Ukraine.&#x0D; Conclusions. The review carried out in this article showed that the concert-piano style of A. Karamanov is an original phenomenon of world piano culture, and therefore requires more in-depth study and performance attention. After all, it embodies the characteristics of a new worldview paradigm, formed on the basis of classical traditions and it is considered as a “bridge” between them and modernity.

Evaluation of solid pulmonary nodules in patients with bladder cancer with computed tomography based texture analysis method: Is it possible to differentiate metastatic - non-metastatic nodules?

Solid Pulmonary Nodule (SPN) is defined as parenchymal radiopacity smaller than 3 cm in diameter. Evaluating the metastatic nature of the SPNs detected in the thorax computed tomography (TCT) examination for staging purposes in cancer patients becomes a fundamental problem for the physician. Invasive procedures, additional imaging or follow-up imaging, are often used to differentiate metastatic and non-metastatic nodules. In this study, we aimed to distinguish SPNs detected in patients diagnosed with bladder cancer (BC) as metastatic and non-metastatic nodules by texture analysis. TCT images of patients diagnosed with BC in our hospital from January 2007 until December 2017 were retrospectively evaluated. A total of 46 patients with SPN, including metastatic (n= 19) and non-metastatic (n= 27), were included in the study. Short axis diameter, long-axis diameter, nodule volume and volume histogram values of the nodules were obtained. Chisquare test was used to evaluate dependent variables, and the Mann-Whitney U test was used to evaluate independent variables. ROC curves of the obtained data were plotted. Statistically, the significant p-value was determined as less than 0.05. A significant difference was found between SPN long axis, short axis and volume values. In the volumetric histogram analysis, the maximum density value and the mean density value were found to be statistically significant. When the average of the highest densities in the volume histogram data was evaluated, the area under the curve value was 0.702 (95% CI, 519-854). The metastatic nodule could be distinguished with a sensitivity of 88% and a specificity of 70% when the volume histogram has the maximum density threshold of 50 HU. In this study, we concluded that SPN detected on CT images can be distinguished as metastatic and non-metastatic nodules using texture analysis method without invasive procedures.

CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes.

Childhood medulloblastoma (MB) is a threatening malignant tumor affecting children all over the globe. It is believed to be the foremost common pediatric brain tumor causing death. Early and accurate classification of childhood MB and its classes are of great importance to help doctors choose the suitable treatment and observation plan, avoid tumor progression, and lower death rates. The current gold standard for diagnosing MB is the histopathology of biopsy samples. However, manual analysis of such images is complicated, costly, time-consuming, and highly dependent on the expertise and skills of pathologists, which might cause inaccurate results. This study aims to introduce a reliable computer-assisted pipeline called CoMB-Deep to automatically classify MB and its classes with high accuracy from histopathological images. This key challenge of the study is the lack of childhood MB datasets, especially its four categories (defined by the WHO) and the inadequate related studies. All relevant works were based on either deep learning (DL) or textural analysis feature extractions. Also, such studies employed distinct features to accomplish the classification procedure. Besides, most of them only extracted spatial features. Nevertheless, CoMB-Deep blends the advantages of textural analysis feature extraction techniques and DL approaches. The CoMB-Deep consists of a composite of DL techniques. Initially, it extracts deep spatial features from 10 convolutional neural networks (CNNs). It then performs a feature fusion step using discrete wavelet transform (DWT), a texture analysis method capable of reducing the dimension of fused features. Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. A bi-directional long-short term memory (Bi-LSTM) network; a DL-based approach that is utilized for the classification phase. CoMB-Deep maintains two classification categories: binary category for distinguishing between the abnormal and normal cases and multi-class category to identify the subclasses of MB. The results of the CoMB-Deep for both classification categories prove that it is reliable. The results also indicate that the feature sets selected using both search strategies have enhanced the performance of Bi-LSTM compared to individual spatial deep features. CoMB-Deep is compared to related studies to verify its competitiveness, and this comparison confirmed its robustness and outperformance. Hence, CoMB-Deep can help pathologists perform accurate diagnoses, reduce misdiagnosis risks that could occur with manual diagnosis, accelerate the classification procedure, and decrease diagnosis costs.

File Fragment Type Classification by Bag-Of-Visual-Words

File fragment’s type classification in the absence of header and file system information, is a major building block in various solutions devoted to file carving, memory analysis and network forensics. Over the past decades, a substantial amount of effort has been put into developing methods to classify file fragments. Meanwhile, there has been little innovation on the basics of approaches given into file and fragment type classification. In this research, by mapping each fragment as an 8-bit grayscale image, a method of texture analysis has been used in place of a classifier. Essentially, we show how to construct a vocabulary of visual words with the Bag-of-Visual-Words method. Using the n-gram technique, the feature vector is comprised of visual words occurrence. On the classification of 31 file types over 31000 fragments, our approach reached a maximum overall accuracy of 74.9% in classifying 512 byte fragments and 87.3% in classifying 4096 byte fragments.

An integrated texture analysis and machine learning approach for durability assessment of lightweight cement composites with hydrophobic coatings modified by nanocellulose

The aim of the study was to determine a set of image texture features of the lightweight cementitious composites (LLC) with hydrophobic coatings modified with nanocellulose and use them to assess the materials' durability. A novel method based on a combination of image texture analysis and machine learning methods was proposed. Textural features were extracted from the images obtained with a scanning microscope. The best classification model was built by the Support Vector Machine method using 16 features selected by the Sequential Forward Selection algorithm. The model recognizes one of the four ranges of the contact angle, which is closely related to the degree of resistance of the analyzed material, with an accuracy of 82%. The results obtained show a relationship between the effectiveness of hydrophobic coatings in LCC and images of their surfaces. This relationship can be used with machine learning methods for conducting strength diagnostics of building materials.

Grape disease detection using dual channel Convolution Neural Network method

Grapes are one type of fruit that is usually used to make grape juice, jelly, grapes, grape seed oil and raisins, or to be eaten directly. So far, checking for disease in grapes is still done manually, by checking the leaves of the grapes by experts. This method certainly takes a long time considering the extent of the vineyards that must be evaluated. To solve this problem, it is necessary to apply a method of detecting grape disease, so that it can help the common people to detect grape disease. This research will use the Dual-Channel Convolutional Neural Network method. The process of detecting grape disease using the DCCNN method will begin with the extraction of the leaves from the input image using the Gabor Filter method. After that, the Segmentation Based Fractal Co-Occurrence Texture Analysis method will be used to extract the features, color, and texture of the extracted leaves. The result is the number of datasets will affect the accuracy of the results of disease identification using the DCCNN method. However, more datasets will cause the execution process to take longer. Changes in the angle and frequency values in the Gabor method at the time of testing will reduce the accuracy of the test results. The conclusion of this study are the DCCNN method can be used to detect the type of leaf disease in grapes and the number of datasets will affect the accuracy of the results of disease identification using the DCCNN method.

English

Several instrumental texture analysis methods have been developed for use in sweetpotato. However, there are very few reports on the use of the wedge fracture test. The purpose of the study was to develop a texture analysis method using a wedge fracture and evaluate its performance against compression test in assessing sweetpotato varieties with different cooking times. The optimal cooking time (OCT) of five sweetpotato varieties was determined by boiling 2.5 cm3 cubes until soft. Samples for texture analysis were prepared under four conditions: 85&deg;C for 10 and 15 min; and 95&deg;C for 5 and 10 min. Peak positive force (firmness) and total work done (toughness) were determined using the wedge fracture texture analysis. The correlation between the OCT and texture measurements was evaluated, and samples incubated at 85&deg;C for 15 min had the highest correlation with OCT (R2 = 0.725). Using this heat treatment, texture measurements from the wedge fracture were compared to those obtained from a compression test. The wedge fracture test gave significant discrimination of sweetpotato varieties (p &le; 0.05) while the compression test did not. The wedge fracture test is thus recommended for determining the instrumental firmness of boiled sweetpotato varieties with different cooking times. &nbsp; Key words: Texture, wedge fracture test, orange-fleshed sweetpotato, optimal cooking time.

Feasibility of the Quantitative Assessment Method for CT Quality Control in Phantom Image Evaluation

Computed tomography (CT) quality control (QC) is regularly performed with standard phantoms, to bar faulty equipment from medical use. Its accuracy may be improved by replacing qualitative methods based on good visual distinction with pixel value-based quantitative methods. We hypothesized that statistical texture analysis (TA) that covers the entire phantom image would be a more appropriate tool. Therefore, our study devised a novel QC method based on the TA for contrast resolution (CR) and spatial resolution (SR) and proposed new, quantitative CT QC criteria. TA of CR and SR images on an American Association of Physicists in Medicine (AAPM) CT Performance Phantom were performed with nine CT scanner models. Six texture descriptors derived from first-order statistics of grayscale image histograms were analyzed. Principal component analysis was used to reveal descriptors with high utility. For CR evaluation, contrast and softness were the most accurate descriptors. For SR evaluation, contrast, softness, and skewness were the most useful descriptors. We propose the following ranges: contrast for CR, 29.5 ± 15%, for SR, 29 ± 10%; softness for CR, &lt;0.015, for SR, &lt;0.014; and skewness for SR, &gt;−1.85. Our novel TA method may improve the assessment of CR and SR of AAPM phantom images.

Interpretation of the Dominant Channel of Co2 Migration Using 4d Seismic Texture Attributes

Conventional seismic attributes can be effectively used in 4D seismic monitoring. However, these seismic attributes have limitations for the identification of reservoir heterogeneity or anisotropy, and could not explain the characteristics of reservoir changes in the process of CO2 injection and storage. Due to the heterogeneity of the reservoir, the reservoir property has not been interpreted clearly during CO2-EOR and storage in G89 block of Sinopec Shengli Oil Company,China. Based on gray level cooccurrence matric, we applied seismic texture analysis method into 4D seismic data to discriminate CO2 migration pathways and distribution. The difficulty of texture attributes analysis of 4D seismic difference lies in attributes selection firstly. That means which attribute can be chosen and used to identify the changes of reservoir parameters before and after CO2 injection. And then how to obtain the 4D seismic difference attributes of the reservoir. We calculate the seismic texture attributes of the reservoir by means of the caprock consistency processing method of two vintages of 3D seismic datasets. Then the difference attributes background interference is reduced much, and the contrast between the fault and background information is increased, which obviously enhances the visibility of the plane characterization of the reservoir. The difference of 3D seismic texture attributes shows the better image of breakthrough path and distribution of CO2. It indicates the dominate channel lead to the CO2 breakthrough and moved to the highest location of the geological structure

Comparison and validation of automated scoring of SPECT phantom cold rod visibility.

Evaluation of phantom image quality is an integral component of the quality assurance of SPECT systems. This evaluation often is done by visual assessment of the resolution of known structures of a specified size, such as arrays of cold rods in a warm background. Although this method is rapid and convenient, it is qualitative and is subject to inter- and intraobserver variability. Thus an automated quantitative analysis would be preferable. Several metrics of cold rod visibility have been developed, although their suitability for SPECT quality assurance depends on how well they correspond to visual scoring by experienced observers. Various metrics of cold rod visibility, derived from either texture analysis or template-based analysis, were investigated. The texture analysis methods measured the normalized gray-level co-occurrence matrix (GLCM) energy ("Energy%") and entropy ("Entropy%") of each region and an associated combination of the two ("EnergyEntropy%"). One template-based method measured the rods-to-background contrast ("Contrast") and an associated visibility index (Contrast×area="Contrast Visibility"). Another template-based method performed binary classification (BC) of the rods and background to compute the area under curve (AUC) of its receiver operating characteristics (ROC) curve ("BC-AUC") and the corresponding signal-to-noise ratio ("BC-SNR"). All these metrics were computed for 90 SPECT acquisitions of the standard American College of Radiology ("Jaszczak") phantom. Cold rod visibility was scored independently by two experienced nuclear medicine physicists on both dichotomous and 5-point scales. Scoring was performed twice by each observer to evaluate variability. Interobserver agreement (Cohen's kappa statistic) was 0.78, and intraobserver reproducibility was 0.86 and 0.88, respectively, for each observer. Mean and median scores differed significantly between observers. Accuracy of each metric was assessed according to AUC of ROC analysis with respect to mean dichotomous score. The binary classification metrics had the highest accuracy (BC-AUC=0.995, BC-SNR=0.994), above that of the texture analysis metrics (Entropy%=0.992, Energy%=0.988, EnergyEntropy%=0.992) and conventional template analysis (Contrast=0.984, Contrast Visibility=0.989). The metrics were similar in terms of rank correlation to mean visibility score. BC-AUC correlated linearly with mean visibility score (R2 =0.95) and consistently performed among the highest of the metrics vs rod diameter and count level. Automated quantitative analysis of SPECT phantom cold rods correlated well with visual scoring. The metrics based on binary classification performed particularly well for this task, across the range of rod diameters and count levels. The suboptimal interobserver agreement highlights the importance of developing automated algorithms for evaluating scanner performance.