High-energy synchrotron radiation has been used to study
annealing of cold-rolled Cu and Ti. The measurements were performed using a high-vacuum furnace in transmission geometry and an area detector. ...The diffraction images were subsequently processed to extract the orientation distribution. The recrystallization process could be followed with a time resolution of the order of 10 s, and good pole figures could be obtained from the very limited amount of data in single diffraction images. The pole figures compare favorably with pole figures of the same material measured
with a conventional pole figure goniometer. For Cu, a rapid and complete change from a typical rolling texture to a cube texture was observed after annealing for 20 min at 650 °C. For Ti, changes were more subtle with a tendency for
-axes to diminish near the normal direction, as well as for
-axes to become aligned with the rolling direction. The method makes it feasible to study the kinetics of recrystallization with quantitative texture analysis.
OBJECTIVESThe aim of this study was to test whether texture analysis and machine learning enable the detection of myocardial infarction (MI) on non–contrast-enhanced low radiation dose cardiac ...computed tomography (CCT) images.
MATERIALS AND METHODSIn this institutional review board–approved retrospective study, we included non–contrast-enhanced electrocardiography-gated low radiation dose CCT image data (effective dose, 0.5 mSv) acquired for the purpose of calcium scoring of 27 patients with acute MI (9 female patients; mean age, 60 ± 12 years), 30 patients with chronic MI (8 female patients; mean age, 68 ± 13 years), and in 30 subjects (9 female patients; mean age, 44 ± 6 years) without cardiac abnormality, hereafter termed controls. Texture analysis of the left ventricle was performed using free-hand regions of interest, and texture features were classified twice (Model Icontrols versus acute MI versus chronic MI; Model IIcontrols versus acute and chronic MI). For both classifications, 6 commonly used machine learning classifiers were useddecision tree C4.5 (J48), k-nearest neighbors, locally weighted learning, RandomForest, sequential minimal optimization, and an artificial neural network employing deep learning. In addition, 2 blinded, independent readers visually assessed noncontrast CCT images for the presence or absence of MI.
RESULTSIn Model I, best classification results were obtained using the k-nearest neighbors classifier (sensitivity, 69%; specificity, 85%; false-positive rate, 0.15). In Model II, the best classification results were found with the locally weighted learning classification (sensitivity, 86%; specificity, 81%; false-positive rate, 0.19) with an area under the curve from receiver operating characteristics analysis of 0.78. In comparison, both readers were not able to identify MI in any of the noncontrast, low radiation dose CCT images.
CONCLUSIONSThis study indicates the ability of texture analysis and machine learning in detecting MI on noncontrast low radiation dose CCT images being not visible for the radiologistsʼ eye.
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•A quantitative and conventional method has been developed for FDM filament characterization.•Pure polymer and drug loaded filaments have been used to demonstrate printability ...assessment.•A parameter, toughness, was predictive of successful filament printability.•This assessment tool could be potentially applied to filament development and quality control.
Lack of a conventional quantitative characterization method for filament printability has been recognized as a critical barrier to fused deposition modeling (FDM) 3D printing application. In this study, a small molecule drug, indomethacin, was utilized as a model compound. Polymers with various solubility were mixed with model drug and extruded into filaments using hot melt extrusion method. Thirty-two filaments with or without indomethacin were evaluated by texture analyzer to study the correlation between mechanical properties and the printability. Three different texture analysis methods were utilized and compared, and a parameter “toughness” calculated by stiffness test was identified to quantitatively describe the printability of filaments in the FDM 3D printer. The toughness threshold value of printable filament was defined as a process window of certain FDM printing. This study provides a quantitative way to evaluate and predict filament printability, and it has great potential to be applied to FDM filament development and quality control in the pharmaceutical industry.
Retinal Nerve Fiber Layer Optical Texture Analysis Su, Clarice Kai-Ying; Guo, Philip Yawen; Chan, Poemen Pui Man ...
Ophthalmology (Rochester, Minn.),
October 2023, Letnik:
130, Številka:
10
Journal Article
Recenzirano
Odprti dostop
To apply retinal nerve fiber layer (RNFL) optical texture analysis (ROTA) to investigate the prevalence, patterns, and risk factors of RNFL defects in patients with ocular hypertension (OHT) who ...showed normal optic disc and RNFL configuration in clinical examination, normal RNFL thickness on OCT analysis, and normal visual field (VF) results.
Cross-sectional study.
Six hundred eyes of 306 patients with OHT.
All participants underwent clinical examination of the optic disc and RNFL, OCT RNFL imaging, and 24-2 standard automated perimetry. To detect RNFL defects, ROTA was applied. The risk score for glaucoma development was calculated according to the Ocular Hypertension Treatment Study and European Glaucoma Prevention Study (OHTS-EGPS) risk prediction model. Risk factors associated with RNFL defects were analyzed using multilevel logistic regression analysis.
Prevalence of RNFL defects.
The average intraocular pressure (IOP) measured from 3 separate visits within 6 months was 24.9 ± 1.8 mmHg for the eye with higher IOP and 23.7 ± 1.7 mmHg for the eye with lower IOP; the respective central corneal thicknesses were 568.7 ± 30.8 μm and 568.8 ± 31.2 μm. Of 306 patients with OHT, 10.8% (33 patients, 37 eyes) demonstrated RNFL defects in ROTA in at least 1 eye. Of the 37 eyes with RNFL defects, the superior arcuate bundle was the most frequently involved (62.2%), followed by the superior papillomacular bundle (27.0%) and the inferior papillomacular bundle (21.6%). Papillofoveal bundle defects were observed in 10.8% of eyes. The smallest RNFL defect spanned 0.0° along Bruch’s membrane opening margin, whereas the widest RNFL defect extended over 29.3°. Age (years) (odds ratio OR, 1.08; 95% confidence interval CI, 1.03–1.13), VF pattern standard deviation (decibels dB) (OR, 1.82; 95% CI, 1.01–3.29), cup volume (mm3) (OR, 1.24; 95% CI, 1.01–1.53), and the OHTS-EPGS risk score (OR, 1.04; 95% CI, 1.01–1.07) were associated with RNFL defects.
A considerable proportion of patients with OHT who showed no signs of optic disc and RNFL thickness abnormalities on clinical and OCT examination exhibited RNFL defects on ROTA. Axonal fiber bundle defects on ROTA may represent the earliest discernible sign of glaucoma in the glaucoma continuum.
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
The impact of PET image acquisition and reconstruction parameters on SUV measurements or radiomic feature values is widely documented. This scanner effect is detrimental to the design and validation ...of predictive or prognostic models and limits the use of large multicenter cohorts. To reduce the impact of this scanner effect, the ComBat method has been proposed and is now used in various contexts. The purpose of this article is to explain and illustrate the use of ComBat based on practical examples. We also give examples in which the ComBat assumptions are not met and, thus, in which ComBat should not be used.
Extrusion of pastes from squeezable tubes is a ubiquitous but complex process, and it is not well studied. A common example is toothpaste, which needs to be easily extrudable from its tube, but it is ...not always the case due to the complex rheology of the paste. This may be particularly problematic if the base liquid in the formula is anhydrous leading to the paste hardening at temperatures close to ambient. In this work, we use various testing techniques to study the squeezability of the tubes containing hydrous and anhydrous paste formulations. We show that mechanical testing imitating human hand operation adequately predicts the actual sensorial panel data while also correlating with simple viscosity measurements. Furthermore, for anhydrous pastes sensitive to cooling their effective hardening temperature may be predicted by thermal analysis of their base liquids. Overall, it is expected that the results and the methodologies presented in this work will be of good guidance for product/packaging developers.
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•Rheology and cooling effect on paste extrusion were investigated using texture analysis.•Paste extrusion from squeezable tubes correlates with the measured viscosity.•The freezing/hardening point of anhydrous bases and their pastes was also investigated by rheological and thermal analysis.•Anhydrous bases and their pastes showed similar freezing/hardening behavior.•Studying the freezing point of the base suffices to determine the hardening point of its paste.
Automated visual inspection of patterned fabrics, rather than of plain and twill fabrics, has been increasingly focused on by our peers. The aim of this inspection is to detect, identify and locate ...any defects on a patterned fabric surface to maintain high quality control in manufacturing. This paper presents a novel Elo rating (ER) method to achieve defect detection in the spirit of sportsmanship, i.e., fair matches between partitions on an image. An image can be divided into partitions of standard size. With a start-up reference point, matches between various partitions are updated through an Elo point matrix. A partition with a light defect is regarded as a strong player who will always win, a defect-free partition is an average player with a tied result, and a partition with a dark defect is a weak player who will always lose. After finishing all matches, partitions with light defects accumulate high Elo points and partitions with dark defects accumulate low Elo points. Any partition with defects will be shown in the resultant thresholded image: a white resultant image corresponds to a light defect and a grey resultant image corresponds to a dark defect. The ER method was evaluated on databases of dot-patterned fabrics (110 defect-free and 120 defective images), star-patterned fabrics (30 defect-free and 26 defective images) and box-patterned fabrics (25 defect-free and 25 defective images). By comparing the resultant and ground-truth images, an overall detection success rate of 97.07% was achieved, which is comparable to the state-of-the-art methods.
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•An Elo rating fabric inspection method in the sportsmanship׳s spirit is presented.•Fabric inspection is achieved by fair matches between partitions on an image.•Matches between partitions are updated via an Elo point matrix.•A partition with a light defect is seen as a strong player who will always win.•An overall 97.07% detection success rate was achieved for 336 patterned images.
Low back pain is a very common symptom and the leading cause of disability throughout the world. Several degenerative imaging findings seen on magnetic resonance imaging are associated with low back ...pain but none of them is specific for the presence of low back pain as abnormal findings are prevalent among asymptomatic subjects as well. The purpose of this population‐based study was to investigate if more specific magnetic resonance imaging predictors of low back pain could be found via texture analysis and machine learning. We used this methodology to classify T2‐weighted magnetic resonance images from the Northern Finland Birth Cohort 1966 data to symptomatic and asymptomatic groups. Lumbar spine magnetic resonance imaging was performed using a fast spin‐echo sequence at 1.5 T. Texture analysis pipeline consisting of textural feature extraction, principal component analysis, and logistic regression classifier was applied to the data to classify them into symptomatic (clinically relevant pain with frequency ≥30 days and intensity ≥6/10) and asymptomatic (frequency ≤7 days, intensity ≤3/10, and no previous pain episodes in the follow‐up period) groups. Best classification results were observed applying texture analysis to the two lowest intervertebral discs (L4‐L5 and L5‐S1), with accuracy of 83%, specificity of 83%, sensitivity of 82%, negative predictive value of 94%, precision of 56%, and receiver operating characteristic area‐under‐curve of 0.91. To conclude, textural features from T2‐weighted magnetic resonance images can be applied in low back pain classification.
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