The automated analysis of computed tomography (CT) scans of vertebrae, for the purpose of determining an individual's age and sex constitutes a vital area of research. Accurate assessment of bone age ...in children facilitates the monitoring of their growth and development. Moreover, the determination of both age and sex has significant relevance in various legal contexts involving human remains. We have built a dataset comprising CT scans of vertebral bodies from 166 patients of diverse genders, acquired during routine cardiac examinations. These images were rescaled to 8-bit data, and textural features were computed using the qMaZda software. The results were analysed employing conventional machine learning techniques and deep convolutional networks. The regression model, developed for the automatic estimation of bone age, accurately determined patients' ages, with a mean absolute error of 3.14 years and R2 = 0.79. In the context of classifying patient gender through textural analysis supported by machine learning, we achieved an accuracy of 69 %. However, the application of deep convolutional networks for this task yielded a slightly lower accuracy of 59 %.
Multidetector computed tomography-based trabecular bone microstructure analysis ensures promising results in fracture risk prediction caused by osteoporosis. Because multidetector computed tomography ...is associated with high radiation exposure, its clinical routine use is limited. Hence, in this study, we investigated in 11 thoracic midvertebral specimens whether trabecular texture parameters are comparable derived from (1) images reconstructed using statistical iterative reconstruction (SIR) and filtered back projection as criterion standard at different exposures (80, 150, 220, and 500 mAs) and (2) from SIR-based sparse sampling projections (12.5%, 25%, 50%, and 100%) and equivalent exposures as criterion standard. Twenty-four texture features were computed, and those that showed similar values between (1) filtered back projection and SIR at the different exposure levels and (2) sparse sampling and equivalent exposures and reconstructed with SIR were identified. These parameters can be of equal value in determining trabecular bone microstructure with lower radiation exposure using sparse sampling and SIR.
Late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) is valuable for diagnosis and assessment of the severity of various myocardial diseases owing to its potential to visualize ...myocardial scars. T1 mapping is complementary to LGE because it can quantify the degree of myocardial fibrosis or edema. As such, T1-weighted imaging techniques, including LGE using an inversion recovery sequence, contribute to cardiac MRI. T2-weighted imaging is widely used to characterize the tissue of many organs. T2-weighted imaging is used in cardiac MRI to identify myocardial edema related to chest pain, acute myocardial diseases, or severe myocardial injuries. However, it is difficult to determine the presence and extent of myocardial edema because of the low contrast between normal and diseased myocardium and image artifacts of T2-weighted images and the lack of an established method to quantify the images. T2 mapping quantifies myocardial T2 values and help identify myocardial edema. The T2 values are significantly related to the clinical symptoms or severity of nonischemic cardiomyopathy. Texture analysis is a postprocessing method to quantify tissue alterations that are reflected in the T2-weighted images. Texture analysis provides a variety of parameters, such as skewness, entropy, and grey-scale non-uniformity, without the need for additional sequences. The abnormal signal intensity on T2-weighted images or T2 values may correspond to not only myocardial edema but also other tissue alterations. In this review, the techniques of cardiac T2 mapping and texture analysis and their clinical relevance are described.
Texture filtering seeks to smooth out textured details in order to present structures prominently. To filter out multi-scale textured details while preserving structures, some methods propose to ...adjust the size of the filtering windows by handling the pixels near structures with small windows and the other pixels with large windows. Unfortunately, their adjustment measures are not very effective in treating complex situations. They may handle the pixels inside small structures with large windows, which overly smooths them, or they may be incapable of smoothing out large-scale textures. With regard to this, in this paper, we present a novel method that adjusts the window size for a pixel by checking similar pixels in its neighborhood. In general, pixels nearer structures have fewer similar pixels in their neighborhoods than the pixels that are farther away from structures. Thus, our adjustment can adaptively adjust window sizes by the distances from pixels to structures, which improves the potential to treat complex situations. The experimental results show that we can well filter out very large-scale textures while preserving small structures with high quality in a manner superior to the state-of-the-art methods. In addition, our method is very simple and efficient, and can be used easily in applications.
Texture analysis is an emergent imaging technique to quantify heterogeneity in radiological images. It is still unclear whether this technique is capable to reflect tumor microstructure. The present ...study sought to correlate histopathology parameters with texture features derived from contrast-enhanced CT images in head and neck squamous cell carcinomas (HNSCC).
Twenty-eight patients with histopathological proven HNSCC were retrospectively analyzed. In every case EGFR, VEGF, Hif1-alpha, Ki67, p53 expression derived from immunhistochemical specimen were semiautomatically calculated. Furthermore, mean cell count was estimated. Texture analysis was performed on contrast-enhanced CT images as a whole lesion measurement. Spearman's correlation analysis was performed, adjusted with Benjamini-Hochberg correction for multiple tests.
Several texture features correlated with histopathological parameters. After correction only CT texture joint entropy and CT entropy correlation with Hif1-alpha expression remained statistically significant (
= -0.60 and
= -0.50, respectively).
CT texture joint entropy and CT entropy were associated with Hif1-alpha expression in HNSCC and might be able to reflect hypoxic areas in this entity.
Currently, very little is known about the ecology of extinct Eurasian cercopithecids. Dietary information is crucial in understanding the ecological adaptations and diversity of extinct ...cercopithecids and the evolution of this family. For example, the colobine genus Dolichopithecus is represented by multiple large-bodied species that inhabited Eurasia during the Pliocene-Early Pleistocene. The available evidence, though limited, suggests semiterrestrial locomotion, which contrasts with most extant African and Asian colobines that exhibit morphological and physiological adaptations for arboreality and folivory. These differences raise questions regarding the dietary specialization of early colobine taxa and how/if that influenced their dispersion out of Africa and into Eurasia. To further our understanding of the ecology of Plio-Pleistocene cercopithecids, we characterized the dental capabilities and potential dietary adaptations of Dolichopithecus ruscinensis through dental topographic and enamel thickness analyses on an M1 from the locality of Serrat d'en Vacquer, Perpignan (France). We also assessed the feeding behavior of D. ruscinensis through dental microwear texture analysis on a broad sample of fossil molars from fossil sites in France, Greece, Bulgaria, and Romania. Dental topographic and enamel thickness analyses suggest that D. ruscinensis could efficiently process a wide range of foods. Results of the dental microwear texture analysis suggest that its diet ranged from folivory to the consumption of more mechanically challenging foods. Collectively, this suggests a more opportunistic feeding behavior for Dolichopithecus than characteristic of most extant colobines.
Speckle in polarization structured light Kumar, Basant; Lochab, Priyanka; Baidya Kayal, Esha ...
Journal of modern optics,
01/2022, Letnik:
69, Številka:
1
Journal Article
Recenzirano
We show that the far-field speckle patterns observed on the transmission of polarization structured light beams through a random diffuser screen have distinct spatial textures, which significantly ...differ from the texture of the fully developed scalar speckle. In particular, we report on speckle observed using the V-point and C-point polarization singular beams for illumination in this work. The V-point and C-point polarization singularities are generated by a combination of different orbital angular momentum (OAM) states in orthogonal polarizations. When passed through a random diffuser screen, the on-axis far-field diffraction intensity is an incoherent summation of speckle intensities due to the individual OAM states. Unlike the typical grainy structure in the scalar speckle, the speckle patterns observed with V-point and C-point illuminating beams are seen to have a noodle-like and sponge-like textures respectively. The speckle from V-point and C-point illuminations is difficult to distinguish using the usual intensity statistics which does not account for the distinct spatial structure of the speckle patterns. The Gray Level Co-occurrence Matrix (GLCM) methodology was therefore used to extract a number of spatial textural features from the speckle patterns observed using scalar and polarization structured illuminations. The principal component analysis (PCA) technique when applied to the texture parameters shows that it is possible to identify the nature of the illuminating beam by simply observing the texture properties of the resultant speckle pattern. When coherent light beams pass through a random medium resulting in a fully developed speckle pattern, the information regarding the profile of the input beam is generally considered to be lost. Our new observations on distinct textural features in speckle from structured light therefore suggest the possibility of information transmission through random media by means of switching the polarization structure of the incident beam.
To evaluate the ability of texture features for distinguishing between benign and malignant testicular masses, and furthermore, for identifying primary testicular lymphoma in malignant tumors and ...identifying seminoma in testicular germ cell tumors, respectively.
We retrospectively collected 77 patients with an abdominal and pelvic enhanced computed tomography (CT) examination and a histopathologically confirmed testicular mass from a single center. The ROI of each mass was split into two parts by the largest cross-sectional slice and deemed to be two samples. After all processing steps, three-dimensional texture features were extracted from unenhanced and contrast-enhanced CT images. Excellent reproducibility of texture features was defined as intra-class correlation coefficient ≥0.8 (ICC ≥0.8). All the groups were balanced via the synthetic minority over-sampling technique (SMOTE) method. Dimension reduction was based on pearson correlation coefficient (PCC). Before model building, minimum-redundancy maximum-relevance (mRMR) selection and recursive feature elimination (RFE) were used for further feature selection. At last, three ML classifiers with the highest cross validation with 5-fold were selected: autoencoder (AE), support vector machine(SVM), linear discriminant analysis (LAD). Logistics regression (LR) and LR-LASSO were also constructed to compare with the ML classifiers.
985 texture features with ICC ≥0.8 were extracted for further feature selection process. With the highest AUC of 0.946 (P <0.01), logistics regression was proved to be the best model for the identification of benign or malignant testicular masses. Besides, LR also had the best performance in identifying primary testicular lymphoma in malignant testicular tumors and in identifying seminoma in testicular germ cell tumors, with the AUC of 0.982 (P <0.01) and 0.928 (P <0.01), respectively.
Until now, this is the first study that applied CT texture analysis (CTTA) to assess the heterogeneity of testicular tumors. LR model based on CTTA might be a promising non-invasive tool for the diagnosis and differentiation of testicular masses. The accurate diagnosis of testicular masses would assist urologists in correct preoperative and perioperative decision making.
Photodynamic therapy (PDT) offers localized focal ablation in unresectable pancreatic tumors while tissues surrounding the treatment volume experience a lower light dose, termed photodynamic priming ...(PDP). While PDP does not cause tissue damage, it has been demonstrated to promote vascular permeability, improve drug delivery, alleviate tumor cell density, and reduce desmoplasia and the resultant internal pressure in pre-clinical evaluation. Preclinical data supports PDP as a neoadjuvant therapy beneficial to subsequent chemotherapy or immunotherapy, yet it is challenging to quantify PDP effects in clinical treatment without additional imaging and testing. This study investigated the potential of radiomic analysis using CT scans acquired before and after PDT to identify areas experiencing PDT-induced necrosis as well as quantify PDP effects in the surrounding tissues. A total of 235 CT tumor slices from seven patients undergoing PDT for pancreatic tumors were examined. Radiomic features assessed included intensity metrics (CT number in Hounsfield Units) and texture analysis using several gray-level co-occurrence matrix (GLCM) parameters. Pre-treatment scans of tumor areas that resulted in PDT-induced necrosis showed statistically significant differences in intensity and texture-based features that could be used to predict the regions that did respond (paired t-test, response versus no response,
< 0.001). Evaluation of PDP effects on the surrounding tissues also demonstrated statistically significant differences, in tumor mean value, standard deviation, and GLCM parameters of contrast, dissimilarity and homogeneity (t-test, pre versus post,
< 0.001). Using leave-one-out cross validation, six intensity and texture-based features were combined into a support-vector machine model which demonstrated reliable prediction of treatment effects for six out of seven patients (ROC curve, AUC = 0.93). This study provides pilot evidence that texture features extracted from CT scans could be utilized as an effective clinical diagnostic prediction and assessment of PDT and PDP effects in pancreatic tumors. (clinical trial NCT03033225).
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