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Proceedings Paper

Pixel-defect corrections for radiography detectors based on deep learning
Author(s): Eunyeong Hong; Songhee Kang; Eunae Lee; Namjo Yoo; Jae Young Choi; Dong Sik Kim
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Paper Abstract

Flat-panel radiography detectors employ the thin film transistor (TFT) panels to acquire high-quality x-ray images. Pixel defects in the TFT panel can degrade the image quality and lower the production yield of the panel, and ultimately increase the production cost. Hence, developing an appropriate defect correction algorithm for acquired images is important. Conventional algorithms are based on statistical learning and hence optimizing their performances is difficult especially for image edge parts. To alleviate this problem, a template matching technique can be used. In this paper, we considered various pixel-defect correction algorithms based on deep learning techniques, such as the artificial neural network (ANN), convolutional neural network (CNN), and generative adversarial networks, and compared their performances. The defect-correction performances are compared using practical x-ray images acquired from general radiography detectors. A concatenate CNN showed the best defect-correction performance. We also showed that a single-layer ANN could conduct an efficient defect correction in terms of both correction and computational complexity performances.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113124E (16 March 2020); doi: 10.1117/12.2549684
Show Author Affiliations
Eunyeong Hong, Hankuk Univ. of Foreign Studies (Korea, Republic of)
Songhee Kang, Hankuk Univ. of Foreign Studies (Korea, Republic of)
Eunae Lee, Hankuk Univ. of Foreign Studies (Korea, Republic of)
Namjo Yoo, Hankuk Univ. of Foreign Studies (Korea, Republic of)
Jae Young Choi, Hankuk Univ. of Foreign Studies (Korea, Republic of)
Dong Sik Kim, Hankuk Univ. of Foreign Studies (Korea, Republic of)


Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

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