Texture Legal Bv

Mammography is the best approach for early detection of breast cancer. In mammography classification, accuracy is determined by feature extraction methods and classifier. In this study, we propose a mammographic classification that uses Law`s texture energy measurement (LAWS) as a method for extracting texture characteristics. The artificial neural network (RNA) is used as a classifier for normal-abnormal and benign-malignant images. Training data for the mammography classification model are extracted from the VIES database. The result shows that LAWS offers better accuracy than other similar methods such as GLCM. LAWS provides an accuracy of 93.90% for normal-abnormal classification and 83.30% for benign-malignant classification, while GLCM provides only 72.20% accuracy for normal-abnormal classification and 53.06% for benign-malignant classification. Objectives: To investigate the use of texture analysis for the detection of osteoporosis on non-contrast cranial CT scans and to investigate optimal specimen regions in craniofacial bones. Conclusion: The results of this study suggest that specific texture analysis features derived from multi-site regions of interest at the base of the skull and jaw can distinguish between patients with normal bone mineral density and patients with osteoporosis. This study demonstrates the potential benefit of texture analysis to identify osteoporosis in CT scan of the head, which may help identify patients who have not been screened with conventional DXA.

Methods: In this retrospective study approved by the BRI, the clivus, bilateral sphenoidal triangles and mandibular condyles were manually segmented on each head CT scan without contrast, and 41 texture characteristics were extracted from 29 patients with normal bone density (NBD); and 29 patients with osteoporosis. Basic descriptive statistics, including correction for false detection rate, were performed to assess differences in texture traits between cohorts. Initially, isotropic aggregates of crystalline grains exhibit texture-induced anisotropy of their inelastic and elastic behavior when subjected to large inelastic deformations. However, the latter is generally neglected, although experiments and numerical simulations clearly show a strong change in the elastic properties of some materials. The main objective of the thesis is to formulate a phenomenological model for the development of elastic properties of cubic crystal aggregates. The effective elastic properties are determined by the means of orientation of the tensors of local elasticity. The arithmetic, geometric and harmonic means are compared. It can be shown that for cubic crystalline aggregates, all these means depend on the same irreducible tensor of the fourth order, which is the purely anisotropic part of the effective elasticity tensor. Coupled equations for the flow rule and the evolution of the anisotropic part of the elasticity tensor are formulated. The flow rule is based on an anisotropic norm of stress deviation, which is defined by elastic anisotropy. In the evolution equation for the anisotropic part of the elasticity tensor, the direction of the rate of change depends only on the rate of inelastic deformation. The equation of evolution is derived according to the theory of isotropic tensor functions.

The transition from an elastically isotropic initial state to a final (path-dependent) anisotropic state is discussed for polycrystalline copper. Model predictions are compared with micro-macro simulations based on the Taylorâlin model and experimental data. Results: Sixteen texture characteristics showed significant differences (P < 0.01) between MNB and osteoporosis in climus, including 4 histogram features, 2 grayscale co-occurrence matrix characteristics, 8 grayscale series length characteristics, and 2 distribution characteristics. Nineteen texture features, including 9 histogram features, 1 GLCM feature, 2 GLRL features, 5 distribution features, and 2 GLGM features, showed statistically significant differences on both sides of the sphenoidal triangles. A total of 24 texture features showed statistically significant differences between normal BMD and osteoporosis in the left sphenoid bone and a total of 31 texture characteristics in the left condyle. In addition, a total of 22 texture features, including 6 histogram features, 3 GLCM features, 9 GLRL features, 2 law features, and 2 GLGM features, showed statistically significant differences on both sides of the mandibular condyles. Peer review under the responsibility of the Organizing Committee of the International Conference on Computer Science and Computational Intelligence (ICCSCI 2015).