Journal of radiological science and technology (대한방사선기술학회지:방사선기술과학)

Korean Society of Radiological Science (대한방사선과학회)
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Baseline Correction in Computed Radiography Images with 1D Morphological Filter

CR 영상에서 기저선 보정을 위한 1차원 모폴로지컬 필터의 이용에 관한 연구

  • Kim, Yong-Gwon (Department of Radiological Science, Konyang University) ;
  • Ryu, Yeunchul (Department of Radiological Science, Gachon University)
  • 김용권 (건양대학교 방사선학과) ;
  • 류연철 (가천대학교 방사선학과)
  • Received : 2022.06.20
  • Accepted : 2022.10.04
  • Published : 2022.10.31
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Abstract

Computed radiography (CR) systems, which convert an analog signal recorded on a cassette into a digital image, combine the characteristics of analog and digital imaging systems. Compared to digital radiography (DR) systems, CR systems have presented difficulties in evaluating system performance because of their lower detective quantum efficiency, their lower signal-to-noise ratio (SNR), and lower modulation transfer function (MTF). During the step of energy-storing and reading out, a baseline offset occurs in the edge area and makes low-frequency overestimation. The low-frequency offset component in the line spread function (LSF) critically affects the MTF and other image-analysis or qualification processes. In this study, we developed the method of baseline correction using mathematical morphology to determine the LSF and MTF of CR systems accurately. We presented a baseline correction that used a morphological filter to effectively remove the low-frequency offset from the LSF. We also tried an MTF evaluation of the CR system to demonstrate the effectiveness of the baseline correction. The MTF with a 3-pixel structuring element (SE) fluctuated since it overestimated the low-frequency component. This overestimation led the algorithm to over-compensate in the low-frequency region so that high-frequency components appeared relatively strong. The MTFs with between 11- and 15-pixel SEs showed little variation. Compared to spatial or frequency filtering that eliminated baseline effects in the edge spread function, our algorithm performed better at precisely locating the edge position and the averaged LSF was narrower.

Keywords

Acknowledgement

This work was supported by the Gachon University research fund of 2015(GCU-2015-0062).

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