A photographic unsharp masking technique for improving the latitude of chest radiographs without sacrificing image contrast or detail is described. An unsharp mask film, prepared from a scout film of the patient's chest, is placed between the film and the front (entrance) screen in the cassette. A second radiograph then is recorded using technique factors that provide a well-penetrated view of the central mediastinum, etc. The unsharp mask absorbs light from the screen in those areas of the chest that normally are well penetrated, preventing overexposure of these areas and resulting in an improved balance of densities across the chest image. Improvement of contrast by a factor of 2 is demonstrated for mediastinal and retrocardiac structures with no loss of contrast in the central lung fields. Nodule detection studies with a chest phantom and simulated nodules suggest that a single unsharp masked film provides higher nodule detection rates than a pair of films consisting of a normally penetrated and an overpenetrated view, possibly because of facilitation of visual search patterns and contrast/brightness adaptation mechanisms of the visual system. Initial clinical studies indicate that unsharp masking may provide additional useful clinical information.
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http://dx.doi.org/10.1097/00004424-198107000-00007 | DOI Listing |
Pol J Radiol
December 2024
Nuclear Fuel Research School, Nuclear Science and Technology Research Institute, Tehran, Iran.
Purpose: This study explored the use of computer-aided diagnosis (CAD) systems to enhance mammography image quality and identify potentially suspicious areas, because mammography is the primary method for breast cancer screening. The primary aim was to find the best combination of preprocessing algorithms to enable more precise classification and interpretation of mammography images because the selected preprocessing algorithms significantly impact the effectiveness of later classification and segmentation processes.
Material And Methods: The study utilised the mini-MIAS database of mammography images and examined the impact of applying various preprocessing method combinations to differentiate between malignant and benign breast lesions.
Prog Biomed Eng (Bristol)
September 2024
University of Coimbra, CISUC, Department of Informatics Engineering, Coimbra 3030-290, Portugal.
Mammography imaging remains the gold standard for breast cancer detection and diagnosis, but challenges in image quality can lead to misdiagnosis, increased radiation exposure, and higher healthcare costs. This comprehensive review evaluates traditional and machine learning-based techniques for improving mammography image quality, aiming to benefit clinicians and enhance diagnostic accuracy. Our literature search, spanning 2015 - 2024, identified 115 articles focusing on contrast enhancement and noise reduction methods, including histogram equalization, filtering, unsharp masking, fuzzy logic, transform-based techniques, and advanced machine learning approaches.
View Article and Find Full Text PDFSensors (Basel)
June 2024
School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China.
Domain adaptation techniques are crucial for addressing the discrepancies between training and testing data distributions caused by varying operational conditions in practical bearing fault diagnosis. However, transfer fault diagnosis faces significant challenges under complex conditions with dispersed data and distinct distribution differences. Hence, this paper proposes CWT-SimAM-DAMS, a domain adaptation method for bearing fault diagnosis based on SimAM and an adaptive weighting strategy.
View Article and Find Full Text PDFPLoS One
July 2024
School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, Hubei, China.
Brain tumors pose a significant threat to health, and their early detection and classification are crucial. Currently, the diagnosis heavily relies on pathologists conducting time-consuming morphological examinations of brain images, leading to subjective outcomes and potential misdiagnoses. In response to these challenges, this study proposes an improved Vision Transformer-based algorithm for human brain tumor classification.
View Article and Find Full Text PDFSci Rep
March 2024
Division of Intramural Research, National Institute of Neurological Disorders and Stroke, National Institutes of Health, 35 Convent Dr., Bethesda, MD, 20892, USA.
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