Implementing a non-local means method to CTA data of aortic dissection

It is necessary to conserve important information, like edges, details, and textures, in CT aortic dissection images, as this helps the radiologist examine and diagnose the disease. Hence, a less noisy image is required to support medical experts in performing better diagnoses. In this work, the non-local means (NLM) method is conducted to minimize the noise in CT images of aortic dissection patients as a preprocessing step to produce accurate aortic segmentation results. The method is implemented in an existing segmentation system using six different kernel functions, and the evaluation is done by assessing DSC, precision, and recall of segmentation results. Furthermore, the visual quality of denoised images is also taken into account to be determined. Besides, a comparative analysis between NLM and other denoising methods is done in this experiment. The results showed that NLM yields encouraging segmentation results, even though the visualization of denoised images is unacceptable. Applying the NLM algorithm with the flat function provides the highest DSC, precision, and recall values of 0.937101, 0.954835, and 0.920517 consecutively

Комплексный лингвостилистический анализ текстов научного стиля в сфере энергетики (на материале статей из журнала "Теплофизика высоких температур")

Предпереводческий анализ - это условие последовательного грамотно выполненного сравнительного лингвостилистического анализа подлинника и перевода, с помощью которого можно обнаружить переводческие удачи и потери. В статье рассматриваются стилистические средства, которые характеризуют каждую стилевую черту на грамматическом, синтаксическом и морфологическом уровнях. The pre-translation analysis is a condition of a logical consistent correctly made comparative linguistic analysis ofan original text and its translation, where you can find translation successes and losses. This article examines the stylistic means that characterize each style feature on the grammatical, syntactic and morphological levels