Revisiting dynamic range and image enhancement ability of contemporary digital radiographic systems
Dento maxillo facial radiology, 2021 OBJECTIVES To assess the dynamic range and enhancement ability of radiographs acquired with contemporary digital systems. METHODS Five repeated periapical radiographs of human mandibles with an aluminium step-wedge were acquired using two sensor-based ...
Luiz Eduardo Marinho-Vieira +4 more
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RADIOGRAPHIC IMAGE ENHANCEMENT USING HYBRID ALGORITHM
, 2016 Radiographic image quality is important in the medical field since it can increase the visibility of anatomical structures and even improve the medical diagnosis.
Varin Chouvatut, Ekkarat Boonchieng
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Dental radiography image enhancement for treatment evaluation through digital image processing [PDF]
Journal of Clinical and Experimental Dentistry, 2018 Background Evaluation of dental treatment is performed by observing dental periapical radiography to obtain information of filling’s condition, pulp tissue, remain dentin thickness, periodontal ligament, and lamina dura.
Hanifah Rahmi Fajrin +3 more
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SURVEY OF RADIOGRAPHIC IMAGE ENHANCEMENT EXPERIENCE.
, 1972 B.R. Hunt, D. H. Janney, R. Zeigler
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Detecting Bone Lesions in X-Ray Under Diverse Acquisition Conditions [PDF]
SPIE Journal of Medical Imaging, vol. 11 (2), pp. 1-12, March 2024, 2022 The diagnosis of primary bone tumors is challenging, as the initial complaints are often non-specific. Early detection of bone cancer is crucial for a favorable prognosis. Incidentally, lesions may be found on radiographs obtained for other reasons. However, these early indications are often missed.
arxiv +1 more source
Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis [PDF]
Algorithms, Vol. 17, No. 1, pp. 8, 2024, 2023 Diagnosing knee joint osteoarthritis (KOA), a major cause of disability worldwide, is challenging due to subtle radiographic indicators and the varied progression of the disease. Using deep learning for KOA diagnosis requires broad, comprehensive datasets.
arxiv +1 more source
Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images [PDF]
Eur Radiol Exp 8, 10 (2024), 2023 Pre-training datasets, like ImageNet, have become the gold standard in medical image analysis. However, the emergence of self-supervised learning (SSL), which leverages unlabeled data to learn robust features, presents an opportunity to bypass the intensive labeling process.
arxiv +1 more source
Research of Radiographic Image Enhancement Technology
, 2013 mage enhancement has applied widely in biomedical, nondestructive testing, satellite remote sensing and other fields. Especially for the low contrast radiographic images, usually there are some disadvantages for a radiographic image such as the local ...
Yan Chen, Yanling Shao, Z. Gui
semanticscholar +1 more source
Learn From Orientation Prior for Radiograph Super-Resolution: Orientation Operator Transformer [PDF]
arXiv, 2023 Background and objective: High-resolution radiographic images play a pivotal role in the early diagnosis and treatment of skeletal muscle-related diseases. It is promising to enhance image quality by introducing single-image super-resolution (SISR) model into the radiology image field.
arxiv
Masked Conditional Diffusion Models for Image Analysis with Application to Radiographic Diagnosis of Infant Abuse [PDF]
arXiv, 2023 The classic metaphyseal lesion (CML) is a distinct injury that is highly specific for infant abuse. It commonly occurs in the distal tibia. To aid radiologists detect these subtle fractures, we need to develop a model that can flag abnormal distal tibial radiographs (i.e. those with CMLs). Unfortunately, the development of such a model requires a large
arxiv