The present work shows an application of the Chan-Vese algorithm for the semi-automatic segmentation of anatomical structures of interest (lungs and lung tumor) in 4DCT images of the thorax, as well as their three-dimensional reconstruction. The segmentation and reconstruction were performed on 10 CT images, which make up an inspiration-expiration cycle. The maximum displacement was calculated for the case of the lung tumor using the reconstructions of the onset of inspiration, the onset of expiration, and the voxel information. The proposed method achieves appropriate segmentation of the studied structures regardless of their size and shape. The three-dimensional reconstruction allows us to visualize the dynamics of the structures of interest throughout the respiratory cycle. In the future, it is expected to have more evidence of the good performance of the proposed method and to have the feedback of the clinical expert, since the knowledge of the characteristics of anatomical structures, such as their dimension and spatial position, helps in the planning of Radiotherapy (RT) treatments, optimizing the radiation dose to cancer cells and minimizing it in healthy organs. Therefore, the information found in this work may be of interest for the planning of RT treatments.

The optimized methodology and results of the new characterization in terms of dose and image quality of the X-ray system used in the main pediatric hemodynamics service in Chile are presented. In addition, scattered dose rate values at the operator’s eye level are reported for all acquisition modes available in different thicknesses of absorbent media and angiography. The characterization was performed according to the European DIMOND and SENTINEL protocols adapted to pediatric procedures. The air kerma at the entrance surface (ESAK) was measured and the image quality parameters signal-to-noise ratio (SNR) and a figure of merit (FOM) were calculated. The scattered dose rate was measured in personal dose equivalent units. The ESAK for fluoroscopic modes ranged from 0.2 to 35.6 μGy/image when passing from 4 to 20 cm of polymethyl methacrylate (PMMA). For the cine mode, these values ranged from 2.8 to 160.1 μGy/image. The values of the image quality parameters showed a correct system configuration, although abnormal values were observed in the medium fluoroscopic mode. As for the scattered dose rate at the level of the cardiologist’s eyes, the highest value is PMMA with a thickness of 20 cm, where the cine mode reached 9.41 mSv·h-1. The differences found from previous evaluations can be explained by the deterioration of the system and the change of one of the X-ray tubes.

The Huaiyang Canal, a significant section of the Grand Canal, boasts representative tourist attractions. This study analysis of online reviews from Ctrip and Mahive using R language, Gephi, ROST CM, and SPSS has provided insights into tourists’ perceptions of the Huaiyang Canal’s image. Key findings include: (1) Dominant landscape images encompass gardens, canals, and buildings, emphasizing the historical and cultural assets. Both cultural and natural landscapes equally captivate tourists. (2) The canal’s tourism image perception follows a “garden-history-canal” hierarchy with the canal as the central space and history expanding its tourism features. (3) The perceptions can be categorized into historical and cultural landscapes, man-made projects, and attraction perception. Despite varying tourist numbers in Huaian and Yangzhou, scenic spot experiences are similar. The overall perception of tourists is largely positive, but some express concerns about service attitudes and travel time planning.

In view of the fact that the convolution neural network segmentation method lacks to capture the global dependency of infected areas in COVID-19 images, which is not conducive to the complete segmentation of scattered lesion areas, this paper proposes a COVID-19 lesion segmentation method UniUNet based on UniFormer with its strong ability to capture global dependency. Firstly, a U-shaped encoder-decoder structure based on UniFormer is designed, which can enhance the cooperation ability of local and global relations. Secondly, Swin spatial pyramid pooling module is introduced to compensate the influence of spatial resolution reduction in the encoder process and generate multi-scale representation. Multi-scale attention gate is introduced at the skip connection to suppress redundant features and enhance important features. Experiment results show that, compared with the other four methods, the proposed model achieves better results in Dice, loU and Recall on COVID-19-CT-Seg and CC-CCIII dataset, and achieves a more complete segmentation of the lesion area.

This paper provides a comprehensive review of SURF (speeded up robust features) feature descriptor, commonly used technique for image feature extraction. The SURF algorithm has obtained significant popularity because to its robustness, efficiency, and invariance to various image transformations. In this paper, an in-depth analysis of the underlying principles of SURF, its key components, and its use in computer vision tasks such as object recognition, image matching, and 3D reconstruction are proposed. Furthermore, we discuss recent advancements and variations of the SURF algorithm and compare it with other popular feature descriptors. Through this review, the aim is to provide a clear understanding of the SURF feature descriptor and its significance in the area of computer vision.

In Costa Rica, there is no explicit recommendation from the competent authorities for the use of a specific phantom, so experts must explore what suppliers offer, among which the Normi Mam Digital phantom from PTW stands out. This article presents the results of the dosimetry and image quality control applied to the Normi Mam Digital phantom to validate it as equipment that complies with the recommendations of the Human Health Series No. 17. The results obtained were satisfactory, proving that the equipment complies with the tolerances recommended by international health bodies.