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.
Emotional state detection is an important part of human-machine interaction studies. The features used in emotion recognition are derived from the changes in facial mimics and speech signals. In ...emotion recognition from facial expressions, facial expressions are processed by image processing methods. If emotion recognition is performed via speech, speech is digitized by signal processing methods, and various features of speech are obtained by acoustic analysis. However, since the change in the features obtained by acoustic analysis is different according to emotion, the general success rate is changing. To overcome this limitation, the study of the effect of spectrogram images on emotional recognition is a current field of study. The purpose of this study is to investigate the effects of texture analysis methods and spectrogram images on speech emotion recognition. For this purpose, spectrogram images of speech were processed by four different texture analysis methods to obtain feature sets. The success rates for the emotion recognition of the obtained feature sets were experimentally investigated using support vector machines. In addition, the success of texture analysis methods was compared with acoustic analysis methods. The results have shown that texture analysis methods can be used for speech emotion recognition. When the results of the texture analysis methods were compared with those of the acoustic analysis, the texture analysis methods resulted in a 0.4% reduction in emotion recognition success rate. However, the combined use of both methods increased the success rate.
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.
We aim at improving quantitative measures of emphysema in computed tomography (CT) images of the lungs. Current standard measures, such as the relative area of emphysema (RA), rely on a single ...intensity threshold on individual pixels, thus ignoring any interrelations between pixels. Texture analysis allows for a much richer representation that also takes the local structure around pixels into account. This paper presents a texture classification-based system for emphysema quantification in CT images. Measures of emphysema severity are obtained by fusing pixel posterior probabilities output by a classifier. Local binary patterns (LBP) are used as texture features, and joint LBP and intensity histograms are used for characterizing regions of interest (ROIs). Classification is then performed using a k nearest neighbor classifier with a histogram dissimilarity measure as distance. A 95.2% classification accuracy was achieved on a set of 168 manually annotated ROIs, comprising the three classes: normal tissue, centrilobular emphysema, and paraseptal emphysema. The measured emphysema severity was in good agreement with a pulmonary function test (PFT) achieving correlation coefficients of up to | r | = 0.79 in 39 subjects. The results were compared to RA and to a Gaussian filter bank, and the texture-based measures correlated significantly better with PFT than did RA.
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.
Dental and artifact microwear analyses have a lot in common regarding the questions they address, their developmental history and their issues. However, few paleontologists and archeologists are ...aware of this, and even those who are, do not take into account most of the methodological insights from the other field.
In this focus article, we briefly review the main developmental steps of both methods, highlight how similar their histories are and how combining methodological developments can improve both research fields. In both cases, the traditional analyses have been strongly criticized mainly because of their subjectivity and their lack of repeatability and reproducibility. Quantitative surface texture analyses have been proposed in response, resulting in dental microwear texture analysis (DMTA) and quantitative artifact microwear analysis (QAMA). DMTA is however a more mature method than QAMA and is well supported within the paleontological community.
In this paper, focused on the methodological framework of both fields, we address this topic by arguing that traceologists could borrow a lot from DMTA; this would allow QAMA to become an established method much more quickly. Dental microwear analysts can also learn from traceology, especially regarding sample preparation, experimentation and residue analysis.
We hope that this focus article will stimulate more awareness, exchange and collaboration between paleontologists and archeologists, and especially between dental and artifact microwear analysts. Paleontology, archeology and the field of surface analysis as a whole would all benefit from such cooperation.
•Dental and artifact microwear analyses apply quantitative surface texture analysis.•There is limited exchange between paleontology and archeology about these methods.•We briefly review the main developmental steps of both methods.•Combining methodological developments can improve both research fields.•We hope to stimulate collaborations between dental and artifact microwear analysts.
This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new ...composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDG-PET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 ± 0.002, a sensitivity of 0.955 ± 0.006, and a specificity of 0.926 ± 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.
Several reports have shown that radiomic features are affected by acquisition and reconstruction parameters, thus hampering multicenter studies. We propose a method that, by removing the center ...effect while preserving patient-specific effects, standardizes features measured from PET images obtained using different imaging protocols.
Pretreatment
F-FDG PET images of patients with breast cancer were included. In one nuclear medicine department (department A), 63 patients were scanned on a time-of-flight PET/CT scanner, and 16 lesions were triple-negative (TN). In another nuclear medicine department (department B), 74 patients underwent PET/CT on a different brand of scanner and a different reconstruction protocol, and 15 lesions were TN. The images from department A were smoothed using a gaussian filter to mimic data from a third department (department A-S). The primary lesion was segmented to obtain a lesion volume of interest (VOI), and a spheric VOI was set in healthy liver tissue. Three SUVs and 6 textural features were computed in all VOIs. A harmonization method initially described for genomic data was used to estimate the department effect based on the observed feature values. Feature distributions in each department were compared before and after harmonization.
In healthy liver tissue, the distributions significantly differed for 4 of 9 features between departments A and B and for 6 of 9 between departments A and A-S (
< 0.05, Wilcoxon test). After harmonization, none of the 9 feature distributions significantly differed between 2 departments (
> 0.1). The same trend was observed in lesions, with a realignment of feature distributions between the departments after harmonization. Identification of TN lesions was largely enhanced after harmonization when the cutoffs were determined on data from one department and applied to data from the other department.
The proposed harmonization method is efficient at removing the multicenter effect for textural features and SUVs. The method is easy to use, retains biologic variations not related to a center effect, and does not require any feature recalculation. Such harmonization allows for multicenter studies and for external validation of radiomic models or cutoffs and should facilitate the use of radiomic models in clinical practice.
Online palmprint identification Zhang, D.; Wai-Kin Kong; You, J. ...
IEEE transactions on pattern analysis and machine intelligence,
09/2003, Letnik:
25, Številka:
9
Journal Article
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Odprti dostop
Biometrics-based personal identification is regarded as an effective method for automatically recognizing, with a high confidence, a person's identity. This paper presents a new biometric approach to ...online personal identification using palmprint technology. In contrast to the existing methods, our online palmprint identification system employs low-resolution palmprint images to achieve effective personal identification. The system consists of two parts: a novel device for online palmprint image acquisition and an efficient algorithm for fast palmprint recognition. A robust image coordinate system is defined to facilitate image alignment for feature extraction. In addition, a 2D Gabor phase encoding scheme is proposed for palmprint feature extraction and representation. The experimental results demonstrate the feasibility of the proposed system.
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