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Proceedings Paper

On the properties of artificial neural network filters for bone-suppressed digital radiography
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Paper Abstract

Dual-energy imaging can enhance lesion conspicuity. However, the conventional (fast kilovoltage switching) dual-shot dual-energy imaging is vulnerable to patient motion. The single-shot method requires a special design of detector system. Alternatively, single-shot bone-suppressed imaging is possible using post-image processing combined with a filter obtained from training an artificial neural network. In this study, the authors investigate the general properties of artificial neural network filters for bone-suppressed digital radiography. The filter properties are characterized in terms of various parameters such as the size of input vector, the number of hidden units, the learning rate, and so on. The preliminary result shows that the bone-suppressed image obtained from the filter, which is designed with 5,000 teaching images from a single radiograph, results in about 95% similarity with a commercial bone-enhanced image.

Paper Details

Date Published: 4 April 2016
PDF: 8 pages
Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97836B (4 April 2016); doi: 10.1117/12.2216739
Show Author Affiliations
Eunpyeong Park, Pusan National Univ. (Korea, Republic of)
Junbeom Park, Pusan National Univ. (Korea, Republic of)
Daecheon Kim, Pusan National Univ. (Korea, Republic of)
Hanbean Youn, Pusan National Univ. Yangsan Hospital (Korea, Republic of)
Pusan National Univ. (Korea, Republic of)
Hosang Jeon, Pusan National Univ. Yangsan Hospital (Korea, Republic of)
Pusan National Univ. (Korea, Republic of)
Jin Sung Kim, Samsung Medical Ctr. (Korea, Republic of)
Dong-Joong Kang, Pusan National Univ. (Korea, Republic of)
Ho Kyung Kim, Pusan National Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 9783:
Medical Imaging 2016: Physics of Medical Imaging
Despina Kontos; Thomas G. Flohr, Editor(s)

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