Pulmonary nodule classification based on CT density distribution using 3D thoracic CT images

Yoshiki Kawata; Noboru Niki; Hironobu Ohamatsu; Masahiko Kusumoto; Ryutaro Kakinuma; Kiyoshi Mori; Kozo Yamada; Hiroyuki Nishiyama; Kenji Eguchi; Masahiro Kaneko; Noriyuki Moriyama

doi:10.1117/12.535032

30 April 2004 Pulmonary nodule classification based on CT density distribution using 3D thoracic CT images

Yoshiki Kawata, Noboru Niki, Hironobu Ohamatsu, Masahiko Kusumoto, Ryutaro Kakinuma, Kiyoshi Mori, Kozo Yamada, Hiroyuki Nishiyama, Kenji Eguchi, Masahiro Kaneko, Noriyuki Moriyama

Author Affiliations +

Proceedings Volume 5369, Medical Imaging 2004: Physiology, Function, and Structure from Medical Images; (2004) https://doi.org/10.1117/12.535032
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

Computer-aided diagnosis (CAD) has been investigated to provide physicians with quantitative information, such as estimates of the malignant likelihood, to aid in the classification of abnormalities detected at screening of lung cancers. The purpose of this study is to develop a method for classifying nodule density patterns that provides information with respect to nodule statuses such as lesion stage. This method consists of three steps, nodule segmentation, histogram analysis of CT density inside nodule, and classifying nodules into five types based on histogram patterns. In this paper, we introduce a two-dimensional (2-D) joint histogram with respect to distance from nodule center and CT density inside nodule and explore numerical features with respect to shape and position of the joint histogram.

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Yoshiki Kawata, Noboru Niki, Hironobu Ohamatsu, Masahiko Kusumoto, Ryutaro Kakinuma, Kiyoshi Mori, Kozo Yamada, Hiroyuki Nishiyama, Kenji Eguchi, Masahiro Kaneko, and Noriyuki Moriyama "Pulmonary nodule classification based on CT density distribution using 3D thoracic CT images", Proc. SPIE 5369, Medical Imaging 2004: Physiology, Function, and Structure from Medical Images, (30 April 2004); https://doi.org/10.1117/12.535032

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