Registration of optical and synthetic aperture radar (SAR) images is challenging because extracting located identically and unique features on both images are tricky. This paper proposes a novel optical and SAR image registration method based on relative total variation (RTV) and scale-invariant feature transform (SIFT), named RTV-SIFT, to extract feature points on the edges of structures and construct structural edge descriptors to improve the registration accuracy. First, a novel RTV-Harris feature point detection method by combining the RTV and the multiscale Harris algorithm is proposed to extract feature points on both images’ significant structures. This ensures a high repetition rate of the feature points. Second, the feature point descriptors are constructed on enhanced phase congruency edge (EPCE), which combines the Sobel operator and maximum moment of phase congruency (PC) to extract edges from structured images that enhance robustness to nonlinear intensity differences and speckle noise. Finally, after coarse registration, the position and orientation Euclidean distance (POED) between feature points is utilized to achieve fine feature point matching to improve the registration accuracy. The experimental results demonstrate the superiority of the proposed RTV-SIFT method in different scenes and image capture conditions, indicating its robustness and effectiveness in optical and SAR image registration.

Although military metaphors have been used widely to discuss diseases, little research has been conducted from the perspective of diachronic quantitative analysis, especially in the Chinese context. The outbreak of the major disease Severe Acute Respiratory Syndrome (SARS) occurred in China in 2002 and 2003, which was followed by Corona Virus Disease-19 (COVID-19) in 2019 and 2020. A semantic network analysis is conducted to gain insights into the relationship between the external environment and the use of military metaphors in the Chinese coverage of diseases and to provide a basis for the analysis of COVID-19 discourse in China. A semantic network analysis is conducted to examine the use of military metaphors in the coverage of diseases in the People’s Daily. The findings showed that the use of military metaphors in the contemporary Chinese coverage of disease has declined overall, but there were two peaks in the periods from 1966–1975 and from 1996–2006. These peaks could be explained by the contemporary cultural and social changes in China and/or the sudden emergence of infectious diseases throughout the country. The findings also showed that offensive military metaphors have been favored over defensive and military process metaphors, which may be related to the traditional demonization of disease in China. Finally, the findings showed connections between the collectivization of the subject and the use of military metaphors in the Chinese coverage of disease.

Objective: Existing methods for muscle atrophy evaluation based on muscle size measures from ultrasound images are inadequate in precision. Radiomics has been widely used in various medical studies, but its validity for the evaluation of muscle atrophy has not been fully explored. Methods: This study presents a radiomics analysis for muscle atrophy evaluation using ultrasound images. The hindlimb unloading rat model was developed to simulate weightlessness muscle atrophy and ultrasound images of the hind limbs were acquired for both the hindlimb unloaded (HU) and control groups during a 21-day HU period. A total of 368 radiomics features were extracted and the stable and informative features were selected through a two-stage feature selection procedure. The feature change trajectory of the stable features was analyzed using the hierarchical clustering method. Finally, an adaptive longitudinal feature selection and grading network, ALNet, was developed to evaluate muscle atrophy. Results: The clustering trajectories of ultrasound image features showed similar trends to the changes in muscle atrophy at the molecular level. The best grading accuracy achieved by the ALNet was 79.5% for the Soleus (Sol) muscle and 82.6% for the Gastrocnemius (Gas) muscle. Conclusion: The test-retest is essential in performing radiomics analysis on ultrasound images. The longitudinal feature selection is important for muscle atrophy grading. The ultrasound image features of the Gas muscle have better discrimination ability than that of the Sol muscle. This study proves for the first time the capability of ultrasound image features for muscle atrophy evaluation.

Synthetic Aperture Radar (SAR) is an all-weather sensing technology that has proven its effectiveness for ship detection. However, detecting ships accurately with oriented bounding boxes (OBB) on SAR images is challenging due to arbitrary ship orientations and misleading scattering. In this article, we propose a novel anchor-free key-point-based detection method, KeyShip, for detecting orientated SAR ships with high precision. Our approach uses a shape descriptor to model a ship as a combination of three types of key points located at the short-edge centers, long-edge centers, and the target center. These key points are detected separately and clustered based on predicted shape descriptors to construct the final OBB detection results. To address the boundary problem that arises with the shape descriptor representation, we propose a soft training target assignment strategy that facilitates successful shape descriptor training and implicitly learns the shape information of the targets. Our experimental results on three datasets (SSDD, RSDD, and HRSC2016) demonstrate our proposed method’s high performance and robustness.