Classifying pulmonary nodules using dynamic enhanced CT images based on CT number histogram

Kazuhiro Minami; Yoshiki Kawata; Noboru Niki; Hironobu Ohmatsu; Masahiko Kusumoto; Ryuutaro Kakinuma; Kenji Eguchi; Kiyoshi Mori; Masahiro Kaneko; Noriyuki Moriyama

doi:10.1117/12.710556

5 April 2007 Classifying pulmonary nodules using dynamic enhanced CT images based on CT number histogram

Kazuhiro Minami, Yoshiki Kawata, Noboru Niki, Hironobu Ohmatsu, Masahiko Kusumoto, Ryuutaro Kakinuma, Kenji Eguchi, Kiyoshi Mori, Masahiro Kaneko, Noriyuki Moriyama

Author Affiliations +

Proceedings Volume 6514, Medical Imaging 2007: Computer-Aided Diagnosis; 65143B (2007) https://doi.org/10.1117/12.710556
Event: Medical Imaging, 2007, San Diego, CA, United States

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.

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Kazuhiro Minami, Yoshiki Kawata, Noboru Niki, Hironobu Ohmatsu, Masahiko Kusumoto, Ryuutaro Kakinuma, Kenji Eguchi, Kiyoshi Mori, Masahiro Kaneko, and Noriyuki Moriyama "Classifying pulmonary nodules using dynamic enhanced CT images based on CT number histogram", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65143B (5 April 2007); https://doi.org/10.1117/12.710556

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