Polarization imaging techniques have more prominent advantages for imaging in strongly scattered media. Previous de-scattering methods of polarization imaging usually require the priori information of the background region, and rarely consider the effect of non-uniformity of the optical field on image recovery, which not only reduces the processing speed of imaging but also introduces errors in image recovery, especially for moving targets in complex scattering environments. In this paper, we propose a turbid underwater moving image recovery method based on the global estimation of the intensity and the degree of polarization (DOP) of the backscattered light, combined with polarization-relation histogram processing techniques. The full spatial distribution of the intensity and the DOP of the backscattered light are obtained by using frequency domain analysis and filtering. Besides, a threshold factor is set in the frequency domain low-pass filter, which is used to adjust the execution region of the filter, which effectively reduces the error in image recovery caused by estimating the DOP of the backscattered light as a constant in traditional methods with non-uniform illumination. Meanwhile, our method requires no human-computer interaction, which effectively solves the drawbacks that the moving target is difficult to be recovered by traditional methods. Experimental studies were conducted on static and moving targets under turbid water, and satisfactory image recovery quality is achieved.
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Cureus
December 2024
Obstetrics and Gynaecology, Tata Main Hospital, Jamshedpur, IND.
Leiomyomas are benign tumors of the female genital tract, usually arising from the uterus. Vaginal leiomyomas are extremely rare. We describe here a case of vaginal leiomyoma in a 28-year-old unmarried woman who presented with excessive vaginal bleeding and acute retention of urine.
View Article and Find Full Text PDFArch Bone Jt Surg
January 2025
Orthopedic Research Center, Department of Orthopedic Surgery, Mashhad University of Medical Sciences, Mashhad, Iran.
Artificial Intelligence (AI) is rapidly transforming healthcare, particularly in orthopedics, by enhancing diagnostic accuracy, surgical planning, and personalized treatment. This review explores current applications of AI in orthopedics, focusing on its contributions to diagnostics and surgical procedures. Key methodologies such as artificial neural networks (ANNs), convolutional neural networks (CNNs), support vector machines (SVMs), and ensemble learning have significantly improved diagnostic precision and patient care.
View Article and Find Full Text PDFBMC Public Health
January 2025
Centre for Prevention, Lifestyle and Health, National Institute for Public Health and The Environment, Bilthoven, The Netherlands.
Background: A new paradigm of hybrid working exists, with most office workers sharing their work between the office and home office environment. Working from home increases time spent or prolonged sitting, which is associated with an increased risk of chronic disease. Interventions to reduce sitting time, specifically designed for both the office and home-office environments, are required to address this growing public health issue.
View Article and Find Full Text PDFNat Commun
January 2025
School of Emergent Soft Matter, South China University of Technology, Guangzhou, China.
Radioactive molecular iodine (I) is a critical volatile pollutant generated in nuclear energy applications, necessitating sensors that rapidly and selectively detect low concentrations of I vapor to protect human health and the environment. In this study, we design and prepare a three-component sensing material comprising reduced graphene oxide (rGO) as the substrate, silver iodide (AgI) particles as active sites, and polystyrene sulfonate as an additive. The AgI particles enable reversible adsorption and conversion of I molecules into polyiodides, inducing substantial charge density variation in rGO.
View Article and Find Full Text PDFJ Nucl Med
January 2025
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
Accurate quantification in emission tomography is essential for internal radiopharmaceutical therapy dosimetry. Mean activity concentration measurements in objects with diameters less than 10 times the full width at half maximum of the imaging system's spatial resolution are significantly affected (>10%) by the partial-volume effect. This study develops a framework for PET and SPECT spatial resolution characterization and proposes 2 MIRD recovery coefficient models-a geometric mean approximation (RECOVER-GM) and an empirical model (RECOVER-EM)-that provide shape-specific partial-volume correction (PVC).
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