Literature DB >> 32886270

Quantitative Analysis Methods Using Histogram and Entropy for Detector Performance Evaluation According to the Sensitivity Change of the Automatic Exposure Control in Digital Radiography.

Jun-Ho Hwang1,2, Kyung-Bae Lee1, Ji-An Choi1, Tae-Soo Lee3.   

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.

Keywords:  Automatic exposure control (AEC); Detector; Digital radiography (DR); Entropy; Histogram

Mesh:

Year:  2020        PMID: 32886270     DOI: 10.1007/s10916-020-01652-0

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  1 in total

1.  A bi-directional deep learning architecture for lung nodule semantic segmentation.

Authors:  Debnath Bhattacharyya; N Thirupathi Rao; Eali Stephen Neal Joshua; Yu-Chen Hu
Journal:  Vis Comput       Date:  2022-09-08       Impact factor: 2.835

  1 in total

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