13 March 2006 Classifying pulmonary nodules using dynamic enhanced single slice and multi slice CT images
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Abstract
Pulmonary nodules are classified into three types such as solid, mixed GGO, and pure GGO types on the basis of the visual assessment of CT appearance. In our current study a quantitative classification algorithm has been developed by using volumetric data sets obtained from thin-section CT images. The algorithm can classify the pulmonary nodules into five types (α, β, γ, δ, and ε) on the basis of internal features extracted from CT number histograms inside nodules. We applied dynamic enhanced single slice and multi slice CT images to this classification algorithm and we analyzed it in each type.
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Kazuhiro Minami, Kazuhiro Minami, Yoshiki Kawata, Yoshiki Kawata, Noboru Niki, Noboru Niki, Hironobu Ohmatsu, Hironobu Ohmatsu, Masahiko Kusumoto, Masahiko Kusumoto, Kouzou Yamada, Kouzou Yamada, Ryuutaro Kakinuma, Ryuutaro Kakinuma, Kenji Eguchi, Kenji Eguchi, Kiyoshi Mori, Kiyoshi Mori, Masahiro Kaneko, Masahiro Kaneko, Noriyuki Moriyama, Noriyuki Moriyama, } "Classifying pulmonary nodules using dynamic enhanced single slice and multi slice CT images", Proc. SPIE 6143, Medical Imaging 2006: Physiology, Function, and Structure from Medical Images, 614334 (13 March 2006); doi: 10.1117/12.654683; https://doi.org/10.1117/12.654683
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