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India had faced three waves throughout the Coronavirus disease 2019 (COVID-19) pandemic, which had already impacted economic lives and affected the healthcare setting and infrastructure. The widespread impacts have inspired researchers to look for clinical indicators of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection prognosis. Cyclic threshold values have been used to correlate the viral load in COVID-19 patients and for viral transmission. In light of this correlation, a retrospective study was conducted to assess the trend of viral load in clinical and demographic profiles across the three waves. Data of a total of 11,125 COVID-19-positive patients were obtained, which had a Ct value of <35. We stratified Ct values as follows: under 25 (high viral load), 25-30 (moderate viral load), and over 30 (low viral load). We found a significantly high proportion of patients with high viral load during the second wave. A significantly high viral load across the symptomatic and vaccinated populations was found in all three waves, whereas a significantly high viral load across age groups was found only in the first wave. With the widespread availability of real-time PCR and the limited use of genomic surveillance, the Ct value and viral load could be a suitable tool for population-level monitoring and forecasting. [ABSTRACT FROM AUTHOR]

Objective: The aim of the present study was to design and evaluate two MeVisLab networks, one for co-registration of positron emission tomography/computed tomography (PET/CT) images and second for cropping the co-registered PET/CT images. Materials and Methods: Two MeVisLab networks, one to co-register and export PET/CT DICOM images and second for cropping the co-registered PET/CT images were designed using different modules of registration toolkit MERIT. One hundred and twenty-five PET/CT studies were exported from Siemens and GE scanners in DICOM format. These images were co-registered and cropped with our designed networks. The images co-registered with our network were compared visually with the co-registered images of same PET/CT studies on vendor provided workstations by an experienced nuclear medicine physician (NMP). The perfection of the cropping of co-registered images was also assessed visually. Results: Visually, NMP found all 125 images co-registered using the network designed in our study similar to the co-registered images of vendor provided workstations. Furthermore, the cropping of all co-registered images was perfectly done by our network. Conclusion: Two MeVisLab networks designed and evaluated in the present study can be used for co-registration of PET/CT DICOM images and cropping the co-registered PET/CT images. [ABSTRACT FROM AUTHOR]

The evolution of image retargeting technique demands the exploitation of multi‐operators since they are capable of preserving the structure and salient objects of the image. However, these multi‐operators are mostly based on seam carving with scaling or cropping operators which lead to significant distortions in the retargeted image. This study proposes a new multi‐operator scheme which has improved seam carving, through the proposed seam diversion based image retargeting algorithm, integrated with the cropping and warping operator. A total of six different multi‐operator schemes have been proposed out of which the MO6 technique gave remarkable results in terms of image quality, least distortion, and lowest run‐time. To simplify image retargeting operations, an optimised image distance function was used. The optimised image distance function was formulated which combines bidirectional image Euclidean distance, dominant colour descriptor, and an energy‐based coefficient to bypass seams from the point where seams start clashing and hence the defined threshold violates. By integrating cropping and warping into the proposed algorithm, it preserves the salient features of the retargeted images. Typical results have been presented which demonstrates the effectiveness of the proposed methods. User‐based subjective analysis has also been carried out which shows that image retargeted using the MO6 technique has high user preference. [ABSTRACT FROM AUTHOR]

With the advancement in the field of image processing, images are being processed using various image processing algorithms. Nowadays, many efficient content-aware image resizing techniques are being used to safeguard the prominent regions and to generate better results that are visually appealing and pleasing while resizing. Advancements in the new display device with varying screen size demands the development of efficient image resizing algorithm. This paper presents a survey on various image retargeting methods, comparison of image retargeting results based on performance, and also exposes the main challenges in image retargeting such as content preservation of important regions, distortion minimization, and improving the efficiency of image retargeting methods. After reviewing literature from researchers it is suggested that the use of the single operator in image retargeting such as scaling, cropping, seam carving, and warping is not sufficient for obtaining satisfactory results, hence it is essential to combine multiple image retargeting operators. This survey is useful for the researchers interested in content-aware image retargeting. [ABSTRACT FROM AUTHOR]

In this study, we have proposed and validated that histogram of a good‑quality bone scan image can enhance a poor‑quality bone scan image. The histograms of two good‑quality technetium-99m methyl diphosphonate bone scan images I A and I B recommended by nuclear medicine physicians (NMPs) were used to enhance 87 poor‑quality bone scan images. Processed images and their corresponding input images were compared visually by two NMPs with scoring and also quantitatively using entropy, Structural similarity index measure, edge‑based contrast measure, and absolute brightness mean error. Barnard’s unconditional test was applied with a null hypothesis that the histogram of both I A and I B produces similar output image at α =0.05. The mean values of quantitative metrices of the processed images obtained using I A and I B were compared using Kolmogorov–Smirnov test. Histogram of a good‑quality bone scan image can enhance a poor‑quality bone scan image. Visually, histogram of I B improved statistically significantly higher proportion (P < 0.0001) of images (86/87) as compared to histogram of I A (51/87). Quantitatively, I B performed better than I A, and the Chi‑square distance of input and I B was smaller than that of I A . In addition, a statistically significant (P < 0.05) difference in all the quantitative metrics among the outputs obtained using I A and I B was observed. In our study, reference histogram of good‑quality bone scan images transformed the majority of poor‑quality bone scan images (98.85%) into visually good‑quality images acceptable by NMPs. [ABSTRACT FROM AUTHOR]

Breast cancer is a public health problem. A tumor can be of two types benign or malignant. In benign tumors do not spread to other parts of the body, but malignant tumors can spread to other parts of the body and it is cancerous. Early stage detection is the effective method to reduce the mortality rates. MRI, Ultra sound imaging, Computerized Tomography (CT) imaging etc are certain imaging techniques for breast examination. In current situation among these techniques mammography is an effective method for the radiologist to detect the breast cancer. This paper survey on concept of breast cancer and brief survey on various techniques for breast cancer detection. [ABSTRACT FROM AUTHOR]

Introduction The second wave of the coronavirus disease 2019 (COVID-19) pandemic in India, which started from April 2021, has been more severe and deadly than the first wave. The aim of this prospective study was to determine the possibility of other respiratory pathogens contributing towards the severity and hospitalization in the current second wave. Materials and methods Nasopharyngeal and oropharyngeal swab samples were collected and processed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by reverse transcription polymerase chain reaction (RT-PCR). These samples were further processed for detection of co-infection in SARS CoV-2 patients by BioFire® Filmarray® 2.0 (bioMérieux, USA). Results We screened 77 COVID-19-positive patients admitted to All India Institute of Medical Sciences (AIIMS), Rishikesh and found cases of co-infections in five (6.49 %) patients. Conclusion Our finding suggests that co-infections had no or minimal role in augmenting the second wave of the COVID-19 pandemic in India, and the emergence of new variants may be the probable cause., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Gupta et al.)