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Proceedings Paper

Artifact reduction using segmentation constrained RPCA for CT
Author(s): Y. Kim; D. Choi; S. Lim; S. Cho
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Paper Abstract

In this study, we aim to separate the ghost artifacts from the limited angle CT image by using Robust Principle Component Analysis (RPCA) and thus improve the reconstructed CT images. Conventionally, RPCA method separates the foreground and the background. Often, the background is assumed as static or quasi-static. When applied to limited angle CT images, the artifacts are considered as quasi-static background whereas the anatomical structures are considered foreground. Thus, RPCA is performed to segment the foreground from the background. Finally, different post-reconstruction de-noising parameters are applied to each foreground and background to remove the artifact effectively.

Paper Details

Date Published: 27 March 2019
PDF: 5 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500H (27 March 2019); doi: 10.1117/12.2523642
Show Author Affiliations
Y. Kim, KAIST (Korea, Republic of)
D. Choi, KAIST (Korea, Republic of)
S. Lim, KAIST (Korea, Republic of)
S. Cho, KAIST (Korea, Republic of)


Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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