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Proceedings Paper

Classifying pulmonary nodules using dynamic enhanced CT images based on CT number histogram
Author(s): Kazuhiro Minami; Yoshiki Kawata; Noboru Niki; Hironobu Ohmatsu; Masahiko Kusumoto; Ryuutaro Kakinuma; Kenji Eguchi; Kiyoshi Mori; Masahiro Kaneko; Noriyuki Moriyama
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Paper 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 (&agr;, &bgr;, &ggr;, &dgr;, 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.

Paper Details

Date Published: 5 April 2007
PDF: 11 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65143B (5 April 2007); doi: 10.1117/12.710556
Show Author Affiliations
Kazuhiro Minami, Univ. of Tokushima (Japan)
Yoshiki Kawata, Univ. of Tokushima (Japan)
Noboru Niki, Univ. of Tokushima (Japan)
Hironobu Ohmatsu, National Cancer Ctr. Hospital East (Japan)
Masahiko Kusumoto, National Cancer Ctr. Hospital (Japan)
Ryuutaro Kakinuma, National Cancer Ctr. Research Ctr. for Cancer Prevention and Screening (Japan)
Kenji Eguchi, Tokai Univ. (Japan)
Kiyoshi Mori, Tochigi Cancer Ctr. Hospital (Japan)
Masahiro Kaneko, National Cancer Ctr. Hospital (Japan)
Noriyuki Moriyama, National Cancer Ctr. Research Ctr. for Cancer Prevention and Screening (Japan)

Published in SPIE Proceedings Vol. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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