17 March 2008 Classifying pulmonary nodules using dynamic enhanced CT images based on CT number histogram
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Abstract
Pulmonary nodule evaluation based on analyses of contrast-enhanced CT images becomes useful for differentiating malignant and benign nodules. There are several types of nodule regarding inside density (such as solid, mixed GGO, and pure GGO) and size. This paper presents relationships between contrast enhancement characteristics and nodule types. Thin-section, contrast-enhanced CT (pre-contrast, and post-contrast series acquired at 2 and 4 minutes) was performed on 86 patients with pulmonary nodules (42 benign and 44 malignant). Nodule regions were segmented from an isotropic volume reconstructed from each image series. In this study, the contrast-enhancement characteristics of nodules were quantified by using CT number histogram. The CT number histograms inside the segmented nodules were computed on pre-contrast and post-contrast series. A feature characterizing variation between two histograms was computed by subtracting the histogram of post-contrast series from that of pre-contrast series, and dividing the summation of subtracted frequency of each bin by the volume of the segmented nodule on pre-contrast series. The nodules were classified into five types (α, β, γ, δ, and ε) on the basis of internal features extracted from CT number histogram on pre-contrast series. The nodule data set was categorized into subset through the nodule type and size and the performance of the feature to classify malignant from benign nodules was evaluated for each subset.
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Kazuhiro Minami, Yoshiki Kawata, Noboru Niki, Hironobu Ohmatsu, Kiyoshi Mori, Kouzou Yamada, Kenji Eguchi, Masahiro Kaneko, Noriyuki Moriyama, "Classifying pulmonary nodules using dynamic enhanced CT images based on CT number histogram", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69152P (17 March 2008); doi: 10.1117/12.770198; https://doi.org/10.1117/12.770198
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