ArticleJournal of medical systems2020
Quantitative Analysis Methods Using Histogram and Entropy for Detector Performance Evaluation According to the Sensitivity Change of the Automatic Exposure Control in Digital Radiography.
Article in Journal of medical systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Evaluation of the reproducibility of automatic exposure control systems in general X-ray machines using a coin-based method.Radiological physics and technology · 2025Article
- A bi-directional deep learning architecture for lung nodule semantic segmentation.The Visual computer · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study is to evaluate detector performance using histogram and entropy analysis according to the sensitivity change of the automatic exposure control (AEC). The experiment was performed as follows: The sensitivity of the detector was analyzed through a normalized histogram with sensitivities of S200, S400, S800, and S1000 of the AEC; the entropy of the image was then analyzed, and the signal volume of the detector was evaluated according to the sensitivity change. As the sensitivity of the AEC was increased from S200 to S1000, the histogram showed underflow, quantization separation, and dynamic range discrepancy. In addition, entropy showed a decrease as sensitivity was set higher; in particular, entropy degradation was more prominent at sensitivities above S800. Through the histogram and entropy analysis, it was concluded that the detector does not reproduce the sensitivity and signal volume accurately when the sensitivity of the AEC is set high in performance evaluation.
Indexed as
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Registered trials
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